Science.gov

Sample records for adaptive learning rate

  1. Do learning rates adapt to the distribution of rewards?

    PubMed

    Gershman, Samuel J

    2015-10-01

    Studies of reinforcement learning have shown that humans learn differently in response to positive and negative reward prediction errors, a phenomenon that can be captured computationally by positing asymmetric learning rates. This asymmetry, motivated by neurobiological and cognitive considerations, has been invoked to explain learning differences across the lifespan as well as a range of psychiatric disorders. Recent theoretical work, motivated by normative considerations, has hypothesized that the learning rate asymmetry should be modulated by the distribution of rewards across the available options. In particular, the learning rate for negative prediction errors should be higher than the learning rate for positive prediction errors when the average reward rate is high, and this relationship should reverse when the reward rate is low. We tested this hypothesis in a series of experiments. Contrary to the theoretical predictions, we found that the asymmetry was largely insensitive to the average reward rate; instead, the dominant pattern was a higher learning rate for negative than for positive prediction errors, possibly reflecting risk aversion.

  2. Adaptive properties of differential learning rates for positive and negative outcomes.

    PubMed

    Cazé, Romain D; van der Meer, Matthijs A A

    2013-12-01

    The concept of the reward prediction error-the difference between reward obtained and reward predicted-continues to be a focal point for much theoretical and experimental work in psychology, cognitive science, and neuroscience. Models that rely on reward prediction errors typically assume a single learning rate for positive and negative prediction errors. However, behavioral data indicate that better-than-expected and worse-than-expected outcomes often do not have symmetric impacts on learning and decision-making. Furthermore, distinct circuits within cortico-striatal loops appear to support learning from positive and negative prediction errors, respectively. Such differential learning rates would be expected to lead to biased reward predictions and therefore suboptimal choice performance. Contrary to this intuition, we show that on static "bandit" choice tasks, differential learning rates can be adaptive. This occurs because asymmetric learning enables a better separation of learned reward probabilities. We show analytically how the optimal learning rate asymmetry depends on the reward distribution and implement a biologically plausible algorithm that adapts the balance of positive and negative learning rates from experience. These results suggest specific adaptive advantages for separate, differential learning rates in simple reinforcement learning settings and provide a novel, normative perspective on the interpretation of associated neural data.

  3. An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis

    PubMed Central

    Ali, Syed Saad Azhar; Moinuddin, Muhammad; Raza, Kamran

    2014-01-01

    Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of the learning algorithm is discussed in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. An intelligent adaptation rule is developed for the learning rate of RBFNN which gives faster convergence via an estimate of error energy while giving guarantee to the l 2 stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development. PMID:24987745

  4. An adaptive learning rate for RBFNN using time-domain feedback analysis.

    PubMed

    Ali, Syed Saad Azhar; Moinuddin, Muhammad; Raza, Kamran; Adil, Syed Hasan

    2014-01-01

    Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of the learning algorithm is discussed in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. An intelligent adaptation rule is developed for the learning rate of RBFNN which gives faster convergence via an estimate of error energy while giving guarantee to the l 2 stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development.

  5. Adaptive high learning rate probabilistic disruption predictors from scratch for the next generation of tokamaks

    NASA Astrophysics Data System (ADS)

    Vega, J.; Murari, A.; Dormido-Canto, S.; Moreno, R.; Pereira, A.; Acero, A.; Contributors, JET-EFDA

    2014-12-01

    The development of accurate real-time disruption predictors is a pre-requisite to any mitigation action. Present theoretical models of disruptions do not reliably cope with the disruption issues. This article deals with data-driven predictors and a review of existing machine learning techniques, from both physics and engineering points of view, is provided. All these methods need large training datasets to develop successful predictors. However, ITER or DEMO cannot wait for hundreds of disruptions to have a reliable predictor. So far, the attempts to extrapolate predictors between different tokamaks have not shown satisfactory results. In addition, it is not clear how valid this approach can be between present devices and ITER/DEMO, due to the differences in their respective scales and possibly underlying physics. Therefore, this article analyses the requirements to create adaptive predictors from scratch to learn from the data of an individual machine from the beginning of operation. A particular algorithm based on probabilistic classifiers has been developed and it has been applied to the database of the three first ITER-like wall campaigns of JET (1036 non-disruptive and 201 disruptive discharges). The predictions start from the first disruption and only 12 re-trainings have been necessary as a consequence of missing 12 disruptions only. Almost 10 000 different predictors have been developed (they differ in their features) and after the chronological analysis of the 1237 discharges, the predictors recognize 94% of all disruptions with an average warning time (AWT) of 654 ms. This percentage corresponds to the sum of tardy detections (11%), valid alarms (76%) and premature alarms (7%). The false alarm rate is 4%. If only valid alarms are considered, the AWT is 244 ms and the standard deviation is 205 ms. The average probability interval about the reliability and accuracy of all the individual predictions is 0.811 ± 0.189.

  6. Guided filter and adaptive learning rate based non-uniformity correction algorithm for infrared focal plane array

    NASA Astrophysics Data System (ADS)

    Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian

    2016-05-01

    Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.

  7. Learning Rate Updating Methods Applied to Adaptive Fuzzy Equalizers for Broadband Power Line Communications

    NASA Astrophysics Data System (ADS)

    Ribeiro, Moisés V.

    2004-12-01

    This paper introduces adaptive fuzzy equalizers with variable step size for broadband power line (PL) communications. Based on delta-bar-delta and local Lipschitz estimation updating rules, feedforward, and decision feedback approaches, we propose singleton and nonsingleton fuzzy equalizers with variable step size to cope with the intersymbol interference (ISI) effects of PL channels and the hardness of the impulse noises generated by appliances and nonlinear loads connected to low-voltage power grids. The computed results show that the convergence rates of the proposed equalizers are higher than the ones attained by the traditional adaptive fuzzy equalizers introduced by J. M. Mendel and his students. Additionally, some interesting BER curves reveal that the proposed techniques are efficient for mitigating the above-mentioned impairments.

  8. Learning and Domain Adaptation

    NASA Astrophysics Data System (ADS)

    Mansour, Yishay

    Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, yet related, domain for which no labeled data is available. This generalization across domains is a very significant challenge for many machine learning applications and arises in a variety of natural settings, including NLP tasks (document classification, sentiment analysis, etc.), speech recognition (speakers and noise or environment adaptation) and face recognition (different lighting conditions, different population composition).

  9. Perceptual learning in sensorimotor adaptation.

    PubMed

    Darainy, Mohammad; Vahdat, Shahabeddin; Ostry, David J

    2013-11-01

    Motor learning often involves situations in which the somatosensory targets of movement are, at least initially, poorly defined, as for example, in learning to speak or learning the feel of a proper tennis serve. Under these conditions, motor skill acquisition presumably requires perceptual as well as motor learning. That is, it engages both the progressive shaping of sensory targets and associated changes in motor performance. In the present study, we test the idea that perceptual learning alters somatosensory function and in so doing produces changes to human motor performance and sensorimotor adaptation. Subjects in these experiments undergo perceptual training in which a robotic device passively moves the subject's arm on one of a set of fan-shaped trajectories. Subjects are required to indicate whether the robot moved the limb to the right or the left and feedback is provided. Over the course of training both the perceptual boundary and acuity are altered. The perceptual learning is observed to improve both the rate and extent of learning in a subsequent sensorimotor adaptation task and the benefits persist for at least 24 h. The improvement in the present studies varies systematically with changes in perceptual acuity and is obtained regardless of whether the perceptual boundary shift serves to systematically increase or decrease error on subsequent movements. The beneficial effects of perceptual training are found to be substantially dependent on reinforced decision-making in the sensory domain. Passive-movement training on its own is less able to alter subsequent learning in the motor system. Overall, this study suggests perceptual learning plays an integral role in motor learning.

  10. Mutation rates as adaptations.

    PubMed

    Maley, C

    1997-06-01

    In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of a gene for the mutation rate of the individual. This allowed the mutation rate itself to evolve in a lineage. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that one possible cause for differential mutation rates across genes may be the coevolutionary pressure of the various forms of interactions with other genes. PMID:9219670

  11. Adaptive Learning and Risk Taking

    ERIC Educational Resources Information Center

    Denrell, Jerker

    2007-01-01

    Humans and animals learn from experience by reducing the probability of sampling alternatives with poor past outcomes. Using simulations, J. G. March (1996) illustrated how such adaptive sampling could lead to risk-averse as well as risk-seeking behavior. In this article, the author develops a formal theory of how adaptive sampling influences risk…

  12. Organization of Distributed Adaptive Learning

    ERIC Educational Resources Information Center

    Vengerov, Alexander

    2009-01-01

    The growing sensitivity of various systems and parts of industry, society, and even everyday individual life leads to the increased volume of changes and needs for adaptation and learning. This creates a new situation where learning from being purely academic knowledge transfer procedure is becoming a ubiquitous always-on essential part of all…

  13. Brain aerobic glycolysis and motor adaptation learning

    PubMed Central

    Shannon, Benjamin J.; Vaishnavi, Sanjeev Neil; Vlassenko, Andrei G.; Shimony, Joshua S.; Rutlin, Jerrel; Raichle, Marcus E.

    2016-01-01

    Ten percent to 15% of glucose used by the brain is metabolized nonoxidatively despite adequate tissue oxygenation, a process termed aerobic glycolysis (AG). Because of the known role of glycolysis in biosynthesis, we tested whether learning-induced synaptic plasticity would lead to regionally appropriate, learning-dependent changes in AG. Functional MRI (fMRI) before, during, and after performance of a visual–motor adaptation task demonstrated that left Brodmann area 44 (BA44) played a key role in adaptation, with learning-related changes to activity during the task and altered resting-state, functional connectivity after the task. PET scans before and after task performance indicated a sustained increase in AG in left BA 44 accompanied by decreased oxygen consumption. Intersubject variability in behavioral adaptation rate correlated strongly with changes in AG in this region, as well as functional connectivity, which is consistent with a role for AG in synaptic plasticity. PMID:27217563

  14. Interoperability in Personalized Adaptive Learning

    ERIC Educational Resources Information Center

    Aroyo, Lora; Dolog, Peter; Houben, Geert-Jan; Kravcik, Milos; Naeve, Ambjorn; Nilsson, Mikael; Wild, Fridolin

    2006-01-01

    Personalized adaptive learning requires semantic-based and context-aware systems to manage the Web knowledge efficiently as well as to achieve semantic interoperability between heterogeneous information resources and services. The technological and conceptual differences can be bridged either by means of standards or via approaches based on the…

  15. Adaptively Ubiquitous Learning in Campus Math Path

    ERIC Educational Resources Information Center

    Shih, Shu-Chuan; Kuo, Bor-Chen; Liu, Yu-Lung

    2012-01-01

    The purposes of this study are to develop and evaluate the instructional model and learning system which integrate ubiquitous learning, computerized adaptive diagnostic testing system and campus math path learning. The researcher first creates a ubiquitous learning environment which is called "adaptive U-learning math path system". This system…

  16. Adaptive Batch Mode Active Learning.

    PubMed

    Chakraborty, Shayok; Balasubramanian, Vineeth; Panchanathan, Sethuraman

    2015-08-01

    Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative instances to be selected for manual annotation. More recently, there have been attempts toward a batch mode form of active learning, where a batch of data points is simultaneously selected from an unlabeled set. Real-world applications require adaptive approaches for batch selection in active learning, depending on the complexity of the data stream in question. However, the existing work in this field has primarily focused on static or heuristic batch size selection. In this paper, we propose two novel optimization-based frameworks for adaptive batch mode active learning (BMAL), where the batch size as well as the selection criteria are combined in a single formulation. We exploit gradient-descent-based optimization strategies as well as properties of submodular functions to derive the adaptive BMAL algorithms. The solution procedures have the same computational complexity as existing state-of-the-art static BMAL techniques. Our empirical results on the widely used VidTIMIT and the mobile biometric (MOBIO) data sets portray the efficacy of the proposed frameworks and also certify the potential of these approaches in being used for real-world biometric recognition applications.

  17. Adaptable, Personalised E-Learning Incorporating Learning Styles

    ERIC Educational Resources Information Center

    Peter, Sophie E.; Bacon, Elizabeth; Dastbaz, Mohammad

    2010-01-01

    Purpose: The purpose of this paper is to discuss how learning styles and theories are currently used within personalised adaptable e-learning adaptive systems. This paper then aims to describe the e-learning platform iLearn and how this platform is designed to incorporate learning styles as part of the personalisation offered by the system.…

  18. Adapting Online Education to Different Learning Styles.

    ERIC Educational Resources Information Center

    Muir, Diana J.

    The purpose of this research project was to determine if online learning could be adapted to individual learning styles and if this made a difference in the standardized testing scores of Internet students. An overview is provided of current learning theories, including the four stages of learning (exposure, guided learning, independent, mastery)…

  19. Adaptive Units of Learning and Educational Videogames

    ERIC Educational Resources Information Center

    Moreno-Ger, Pablo; Thomas, Pilar Sancho; Martinez-Ortiz, Ivan; Sierra, Jose Luis; Fernandez-Manjon, Baltasar

    2007-01-01

    In this paper, we propose three different ways of using IMS Learning Design to support online adaptive learning modules that include educational videogames. The first approach relies on IMS LD to support adaptation procedures where the educational games are considered as Learning Objects. These games can be included instead of traditional content…

  20. Adaptive Learning Systems: Beyond Teaching Machines

    ERIC Educational Resources Information Center

    Kara, Nuri; Sevim, Nese

    2013-01-01

    Since 1950s, teaching machines have changed a lot. Today, we have different ideas about how people learn, what instructor should do to help students during their learning process. We have adaptive learning technologies that can create much more student oriented learning environments. The purpose of this article is to present these changes and its…

  1. Integrating Learning Styles into Adaptive E-Learning System

    ERIC Educational Resources Information Center

    Truong, Huong May

    2015-01-01

    This paper provides an overview and update on my PhD research project which focuses on integrating learning styles into adaptive e-learning system. The project, firstly, aims to develop a system to classify students' learning styles through their online learning behaviour. This will be followed by a study on the complex relationship between…

  2. Block adaptive rate controlled image data compression

    NASA Technical Reports Server (NTRS)

    Rice, R. F.; Hilbert, E.; Lee, J.-J.; Schlutsmeyer, A.

    1979-01-01

    A block adaptive rate controlled (BARC) image data compression algorithm is described. It is noted that in the algorithm's principal rate controlled mode, image lines can be coded at selected rates by combining practical universal noiseless coding techniques with block adaptive adjustments in linear quantization. Compression of any source data at chosen rates of 3.0 bits/sample and above can be expected to yield visual image quality with imperceptible degradation. Exact reconstruction will be obtained if the one-dimensional difference entropy is below the selected compression rate. It is noted that the compressor can also be operated as a floating rate noiseless coder by simply not altering the input data quantization. Here, the universal noiseless coder ensures that the code rate is always close to the entropy. Application of BARC image data compression to the Galileo orbiter mission of Jupiter is considered.

  3. Adaptive Educational Software by Applying Reinforcement Learning

    ERIC Educational Resources Information Center

    Bennane, Abdellah

    2013-01-01

    The introduction of the intelligence in teaching software is the object of this paper. In software elaboration process, one uses some learning techniques in order to adapt the teaching software to characteristics of student. Generally, one uses the artificial intelligence techniques like reinforcement learning, Bayesian network in order to adapt…

  4. Different Futures of Adaptive Collaborative Learning Support

    ERIC Educational Resources Information Center

    Rummel, Nikol; Walker, Erin; Aleven, Vincent

    2016-01-01

    In this position paper we contrast a Dystopian view of the future of adaptive collaborative learning support (ACLS) with a Utopian scenario that--due to better-designed technology, grounded in research--avoids the pitfalls of the Dystopian version and paints a positive picture of the practice of computer-supported collaborative learning 25 years…

  5. Animal social learning: associations and adaptations.

    PubMed

    Reader, Simon M

    2016-01-01

    Social learning, learning from others, is a powerful process known to impact the success and survival of humans and non-human animals alike. Yet we understand little about the neurocognitive and other processes that underpin social learning. Social learning has often been assumed to involve specialized, derived cognitive processes that evolve and develop independently from other processes. However, this assumption is increasingly questioned, and evidence from a variety of organisms demonstrates that current, recent, and early life experience all predict the reliance on social information and thus can potentially explain variation in social learning as a result of experiential effects rather than evolved differences. General associative learning processes, rather than adaptive specializations, may underpin much social learning, as well as social learning strategies. Uncovering these distinctions is important to a variety of fields, for example by widening current views of the possible breadth and adaptive flexibility of social learning. Nonetheless, just like adaptationist evolutionary explanations, associationist explanations for social learning cannot be assumed, and empirical work is required to uncover the mechanisms involved and their impact on the efficacy of social learning. This work is being done, but more is needed. Current evidence suggests that much social learning may be based on 'ordinary' processes but with extraordinary consequences. PMID:27635227

  6. Animal social learning: associations and adaptations

    PubMed Central

    Reader, Simon M.

    2016-01-01

    Social learning, learning from others, is a powerful process known to impact the success and survival of humans and non-human animals alike. Yet we understand little about the neurocognitive and other processes that underpin social learning. Social learning has often been assumed to involve specialized, derived cognitive processes that evolve and develop independently from other processes. However, this assumption is increasingly questioned, and evidence from a variety of organisms demonstrates that current, recent, and early life experience all predict the reliance on social information and thus can potentially explain variation in social learning as a result of experiential effects rather than evolved differences. General associative learning processes, rather than adaptive specializations, may underpin much social learning, as well as social learning strategies. Uncovering these distinctions is important to a variety of fields, for example by widening current views of the possible breadth and adaptive flexibility of social learning. Nonetheless, just like adaptationist evolutionary explanations, associationist explanations for social learning cannot be assumed, and empirical work is required to uncover the mechanisms involved and their impact on the efficacy of social learning. This work is being done, but more is needed. Current evidence suggests that much social learning may be based on ‘ordinary’ processes but with extraordinary consequences. PMID:27635227

  7. Animal social learning: associations and adaptations

    PubMed Central

    Reader, Simon M.

    2016-01-01

    Social learning, learning from others, is a powerful process known to impact the success and survival of humans and non-human animals alike. Yet we understand little about the neurocognitive and other processes that underpin social learning. Social learning has often been assumed to involve specialized, derived cognitive processes that evolve and develop independently from other processes. However, this assumption is increasingly questioned, and evidence from a variety of organisms demonstrates that current, recent, and early life experience all predict the reliance on social information and thus can potentially explain variation in social learning as a result of experiential effects rather than evolved differences. General associative learning processes, rather than adaptive specializations, may underpin much social learning, as well as social learning strategies. Uncovering these distinctions is important to a variety of fields, for example by widening current views of the possible breadth and adaptive flexibility of social learning. Nonetheless, just like adaptationist evolutionary explanations, associationist explanations for social learning cannot be assumed, and empirical work is required to uncover the mechanisms involved and their impact on the efficacy of social learning. This work is being done, but more is needed. Current evidence suggests that much social learning may be based on ‘ordinary’ processes but with extraordinary consequences.

  8. Adaptations to a Learning Resource

    ERIC Educational Resources Information Center

    Libbrecht, Paul

    2015-01-01

    Learning resources have been created to represent digital units of exchangeable materials that teachers and learners can pull from in order to support the learning processes. They resource themselves. Leveraging the web, one can often find these resources. But what characteristics do they need in order to be easily exchangeable? Although several…

  9. Flexible Ubiquitous Learning Management System Adapted to Learning Context

    NASA Astrophysics Data System (ADS)

    Jeong, Ji-Seong; Kim, Mihye; Park, Chan; Yoo, Jae-Soo; Yoo, Kwan-Hee

    This paper proposes a u-learning management system (ULMS) appropriate to the ubiquitous learning environment, with emphasis on the significance of context awareness and adaptation in learning. The proposed system supports the basic functions of an e-learning management system and incorporates a number of tools and additional features to provide a more customized learning service. The proposed system automatically corresponds to various forms of user terminal without modifying the existing system. The functions, formats, and course learning activities of the system are dynamically and adaptively constructed at runtime according to user terminals, course types, pedagogical goals as well as student characteristics and learning context. A prototype for university use has been implemented to demonstrate and evaluate the proposed approach. We regard the proposed ULMS as an ideal u-learning system because it can not only lead students into continuous and mobile 'anytime, anywhere' learning using any kind of terminal, but can also foster enhanced self-directed learning through the establishment of an adaptive learning environment.

  10. Exploring Adaptability through Learning Layers and Learning Loops

    ERIC Educational Resources Information Center

    Lof, Annette

    2010-01-01

    Adaptability in social-ecological systems results from individual and collective action, and multi-level interactions. It can be understood in a dual sense as a system's ability to adapt to disturbance and change, and to navigate system transformation. Inherent in this conception, as found in resilience thinking, are the concepts of learning and…

  11. Adapting Active Learning in Ethiopia

    ERIC Educational Resources Information Center

    Casale, Carolyn Frances

    2010-01-01

    Ethiopia is a developing country that has invested extensively in expanding its educational opportunities. In this expansion, there has been a drastic restructuring of its system of preparing teachers and teacher educators. Often, improving teacher quality is dependent on professional development that diversifies pedagogy (active learning). This…

  12. Learning and adaptation in fuzzy neural systems

    NASA Astrophysics Data System (ADS)

    Gupta, Madan M.

    1992-03-01

    In recent years, an increasing number of researchers have become involved in the subject of fuzzy neural networks in the hope of combining the reasoning strength of fuzzy logic and the learning and adaptation power of neural networks. This provides a more powerful tool for fuzzy information processing and for exploring the functioning of human brains. In this paper, an attempt has been made to establish some basic models for fuzzy neurons. First, several possible fuzzy neuron models are proposed. Second, synaptic and somatic learning and adaptation mechanisms are proposed. Finally, the possibility of applying nonfuzzy neural networks approaches to fuzzy systems is also described.

  13. Adaptive Cognitive-Based Selection of Learning Objects

    ERIC Educational Resources Information Center

    Karampiperis, Pythagoras; Lin, Taiyu; Sampson, Demetrios G.; Kinshuk

    2006-01-01

    Adaptive cognitive-based selection is recognized as among the most significant open issues in adaptive web-based learning systems. In order to adaptively select learning resources, the definition of adaptation rules according to the cognitive style or learning preferences of the learners is required. Although some efforts have been reported in…

  14. Adaptive Learning Resources Sequencing in Educational Hypermedia Systems

    ERIC Educational Resources Information Center

    Karampiperis, Pythagoras; Sampson, Demetrios

    2005-01-01

    Adaptive learning resources selection and sequencing is recognized as among the most interesting research questions in adaptive educational hypermedia systems (AEHS). In order to adaptively select and sequence learning resources in AEHS, the definition of adaptation rules contained in the Adaptation Model, is required. Although, some efforts have…

  15. Adapting Cooperative Learning in Tertiary ELT

    ERIC Educational Resources Information Center

    Ning, Huiping

    2011-01-01

    An updated guideline for tertiary ELT in China has shifted the emphasis to the development of learners' ability to communicate in English. Using group work and getting learners actively involved in the actual use of English are highlighted more than before. This article focuses on adapting cooperative learning methods for ELT with tertiary…

  16. Making Mistakes: Emotional Adaptation and Classroom Learning

    ERIC Educational Resources Information Center

    McCaslin, Mary; Vriesema, Christine C.; Burggraf, Susan

    2016-01-01

    Background: We studied how students in Grades 4-6 participate in and emotionally adapt to the give-and-take of learning in classrooms, particularly when making mistakes. Our approach is consistent with researchers who (a) include cognitive appraisals in the study of emotional experiences, (b) consider how personal concerns might mediate…

  17. Adaptable Learning Assistant for Item Bank Management

    ERIC Educational Resources Information Center

    Nuntiyagul, Atorn; Naruedomkul, Kanlaya; Cercone, Nick; Wongsawang, Damras

    2008-01-01

    We present PKIP, an adaptable learning assistant tool for managing question items in item banks. PKIP is not only able to automatically assist educational users to categorize the question items into predefined categories by their contents but also to correctly retrieve the items by specifying the category and/or the difficulty level. PKIP adapts…

  18. Intelligent robots that adapt, learn, and predict

    NASA Astrophysics Data System (ADS)

    Hall, E. L.; Liao, X.; Ghaffari, M.; Alhaj Ali, S. M.

    2005-10-01

    The purpose of this paper is to describe the concept and architecture for an intelligent robot system that can adapt, learn and predict the future. This evolutionary approach to the design of intelligent robots is the result of several years of study on the design of intelligent machines that could adapt using computer vision or other sensory inputs, learn using artificial neural networks or genetic algorithms, exhibit semiotic closure with a creative controller and perceive present situations by interpretation of visual and voice commands. This information processing would then permit the robot to predict the future and plan its actions accordingly. In this paper we show that the capability to adapt, and learn naturally leads to the ability to predict the future state of the environment which is just another form of semiotic closure. That is, predicting a future state without knowledge of the future is similar to making a present action without knowledge of the present state. The theory will be illustrated by considering the situation of guiding a mobile robot through an unstructured environment for a rescue operation. The significance of this work is in providing a greater understanding of the applications of learning to mobile robots.

  19. Catecholaminergic Regulation of Learning Rate in a Dynamic Environment

    PubMed Central

    Jepma, Marieke; Nassar, Matthew R.; Rangel-Gomez, Mauricio; Meeter, Martijn; Nieuwenhuis, Sander

    2016-01-01

    Adaptive behavior in a changing world requires flexibly adapting one’s rate of learning to the rate of environmental change. Recent studies have examined the computational mechanisms by which various environmental factors determine the impact of new outcomes on existing beliefs (i.e., the ‘learning rate’). However, the brain mechanisms, and in particular the neuromodulators, involved in this process are still largely unknown. The brain-wide neurophysiological effects of the catecholamines norepinephrine and dopamine on stimulus-evoked cortical responses suggest that the catecholamine systems are well positioned to regulate learning about environmental change, but more direct evidence for a role of this system is scant. Here, we report evidence from a study employing pharmacology, scalp electrophysiology and computational modeling (N = 32) that suggests an important role for catecholamines in learning rate regulation. We found that the P3 component of the EEG—an electrophysiological index of outcome-evoked phasic catecholamine release in the cortex—predicted learning rate, and formally mediated the effect of prediction-error magnitude on learning rate. P3 amplitude also mediated the effects of two computational variables—capturing the unexpectedness of an outcome and the uncertainty of a preexisting belief—on learning rate. Furthermore, a pharmacological manipulation of catecholamine activity affected learning rate following unanticipated task changes, in a way that depended on participants’ baseline learning rate. Our findings provide converging evidence for a causal role of the human catecholamine systems in learning-rate regulation as a function of environmental change. PMID:27792728

  20. Improving Adaptive Learning Technology through the Use of Response Times

    ERIC Educational Resources Information Center

    Mettler, Everett; Massey, Christine M.; Kellman, Philip J.

    2011-01-01

    Adaptive learning techniques have typically scheduled practice using learners' accuracy and item presentation history. We describe an adaptive learning system (Adaptive Response Time Based Sequencing--ARTS) that uses both accuracy and response time (RT) as direct inputs into sequencing. Response times are used to assess learning strength and…

  1. Adaptive functional systems: Learning with chaos

    NASA Astrophysics Data System (ADS)

    Komarov, M. A.; Osipov, G. V.; Burtsev, M. S.

    2010-12-01

    We propose a new model of adaptive behavior that combines a winnerless competition principle and chaos to learn new functional systems. The model consists of a complex network of nonlinear dynamical elements producing sequences of goal-directed actions. Each element describes dynamics and activity of the functional system which is supposed to be a distributed set of interacting physiological elements such as nerve or muscle that cooperates to obtain certain goal at the level of the whole organism. During "normal" behavior, the dynamics of the system follows heteroclinic channels, but in the novel situation chaotic search is activated and a new channel leading to the target state is gradually created simulating the process of learning. The model was tested in single and multigoal environments and had demonstrated a good potential for generation of new adaptations.

  2. MEAT: An Authoring Tool for Generating Adaptable Learning Resources

    ERIC Educational Resources Information Center

    Kuo, Yen-Hung; Huang, Yueh-Min

    2009-01-01

    Mobile learning (m-learning) is a new trend in the e-learning field. The learning services in m-learning environments are supported by fundamental functions, especially the content and assessment services, which need an authoring tool to rapidly generate adaptable learning resources. To fulfill the imperious demand, this study proposes an…

  3. Yet Another Adaptive Learning Management System Based on Felder and Silverman's Learning Styles and Mashup

    ERIC Educational Resources Information Center

    Chang, Yi-Hsing; Chen, Yen-Yi; Chen, Nian-Shing; Lu, You-Te; Fang, Rong-Jyue

    2016-01-01

    This study designs and implements an adaptive learning management system based on Felder and Silverman's Learning Style Model and the Mashup technology. In this system, Felder and Silverman's Learning Style model is used to assess students' learning styles, in order to provide adaptive learning to leverage learners' learning preferences.…

  4. Using Assistive Technology Adaptations To Include Students with Learning Disabilities in Cooperative Learning Activities.

    ERIC Educational Resources Information Center

    Bryant, Diane Pedrotty; Bryant, Brian R.

    1998-01-01

    Discusses a process for integrating technology adaptations for students with learning disabilities into cooperative-learning activities in terms of three components: (1) selecting adaptations; (2) monitoring use of adaptations during cooperative-learning activities; and (3) evaluating the adaptations' effectiveness. Barriers to and support systems…

  5. Adaptive Politics, Social Learning, and Military Institutions.

    ERIC Educational Resources Information Center

    Bobrow, Davis B.

    Rates and forms of change in post-industrial societies will increasingly test the viability of democratic political systems. Social learning must become faster and more powerful as the deadline on political demands becomes shorter and the complexity and variety of demands become greater. The military can play an almost uniquely helpful role in…

  6. Adaptable Learning Pathway Generation with Ant Colony Optimization

    ERIC Educational Resources Information Center

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2009-01-01

    One of the new major directions in research on web-based educational systems is the notion of adaptability: the educational system adapts itself to the learning profile, preferences and ability of the student. In this paper, we look into the issues of providing adaptability with respect to learning pathways. We explore the state of the art with…

  7. AH-Questionnaire: An Adaptive Hierarchical Questionnaire for Learning Styles

    ERIC Educational Resources Information Center

    Ortigosa, Alvaro; Paredes, Pedro; Rodriguez, Pilar

    2010-01-01

    One of the main concerns when providing learning style adaptation in Adaptive Educational Hypermedia Systems is the number of questions the students have to answer. Most of the times, adaptive material available will discriminate among a few categories for each learning style dimension. Consequently, it is only needed to take into account the…

  8. Critical Thinking, Developmental Learning, and Adaptive Flexibility in Organizational Leaders.

    ERIC Educational Resources Information Center

    Duchesne, Robert E., Jr.

    A study examined how developmental learning and adaptive flexibility relate to critical thinking though a survey of 119 organizational leaders (of 341) who had attended a 5-day Leadership Development Program. A questionnaire adapted from the Center for Creative Leadership's Job Challenge Profile measured developmental learning, the Adaptive Style…

  9. Development of Adaptive Kanji Learning System for Mobile Phone

    ERIC Educational Resources Information Center

    Li, Mengmeng; Ogata, Hiroaki; Hou, Bin; Hashimoto, Satoshi; Liu, Yuqin; Uosaki, Noriko; Yano, Yoneo

    2010-01-01

    This paper describes an adaptive learning system based on mobile phone email to support the study of Japanese Kanji. In this study, the main emphasis is on using the adaptive learning to resolve one common problem of the mobile-based email or SMS language learning systems. To achieve this goal, the authors main efforts focus on three aspects:…

  10. How Language Supports Adaptive Teaching through a Responsive Learning Culture

    ERIC Educational Resources Information Center

    Johnston, Peter; Dozier, Cheryl; Smit, Julie

    2016-01-01

    For students to learn optimally, teachers must design classrooms that are responsive to the full range of student development. The teacher must be adaptive, but so must each student and the learning culture itself. In other words, adaptive teaching means constructing a responsive learning culture that accommodates and even capitalizes on diversity…

  11. Illusory Reversal of Causality between Touch and Vision has No Effect on Prism Adaptation Rate.

    PubMed

    Tanaka, Hirokazu; Homma, Kazuhiro; Imamizu, Hiroshi

    2012-01-01

    Learning, according to Oxford Dictionary, is "to gain knowledge or skill by studying, from experience, from being taught, etc." In order to learn from experience, the central nervous system has to decide what action leads to what consequence, and temporal perception plays a critical role in determining the causality between actions and consequences. In motor adaptation, causality between action and consequence is implicitly assumed so that a subject adapts to a new environment based on the consequence caused by her action. Adaptation to visual displacement induced by prisms is a prime example; the visual error signal associated with the motor output contributes to the recovery of accurate reaching, and a delayed feedback of visual error can decrease the adaptation rate. Subjective feeling of temporal order of action and consequence, however, can be modified or even reversed when her sense of simultaneity is manipulated with an artificially delayed feedback. Our previous study (Tanaka et al., 2011; Exp. Brain Res.) demonstrated that the rate of prism adaptation was unaffected when the subjective delay of visual feedback was shortened. This study asked whether subjects could adapt to prism adaptation and whether the rate of prism adaptation was affected when the subjective temporal order was illusory reversed. Adapting to additional 100 ms delay and its sudden removal caused a positive shift of point of simultaneity in a temporal order judgment experiment, indicating an illusory reversal of action and consequence. We found that, even in this case, the subjects were able to adapt to prism displacement with the learning rate that was statistically indistinguishable to that without temporal adaptation. This result provides further evidence to the dissociation between conscious temporal perception and motor adaptation. PMID:23248609

  12. Illusory Reversal of Causality between Touch and Vision has No Effect on Prism Adaptation Rate.

    PubMed

    Tanaka, Hirokazu; Homma, Kazuhiro; Imamizu, Hiroshi

    2012-01-01

    Learning, according to Oxford Dictionary, is "to gain knowledge or skill by studying, from experience, from being taught, etc." In order to learn from experience, the central nervous system has to decide what action leads to what consequence, and temporal perception plays a critical role in determining the causality between actions and consequences. In motor adaptation, causality between action and consequence is implicitly assumed so that a subject adapts to a new environment based on the consequence caused by her action. Adaptation to visual displacement induced by prisms is a prime example; the visual error signal associated with the motor output contributes to the recovery of accurate reaching, and a delayed feedback of visual error can decrease the adaptation rate. Subjective feeling of temporal order of action and consequence, however, can be modified or even reversed when her sense of simultaneity is manipulated with an artificially delayed feedback. Our previous study (Tanaka et al., 2011; Exp. Brain Res.) demonstrated that the rate of prism adaptation was unaffected when the subjective delay of visual feedback was shortened. This study asked whether subjects could adapt to prism adaptation and whether the rate of prism adaptation was affected when the subjective temporal order was illusory reversed. Adapting to additional 100 ms delay and its sudden removal caused a positive shift of point of simultaneity in a temporal order judgment experiment, indicating an illusory reversal of action and consequence. We found that, even in this case, the subjects were able to adapt to prism displacement with the learning rate that was statistically indistinguishable to that without temporal adaptation. This result provides further evidence to the dissociation between conscious temporal perception and motor adaptation.

  13. Illusory Reversal of Causality between Touch and Vision has No Effect on Prism Adaptation Rate

    PubMed Central

    Tanaka, Hirokazu; Homma, Kazuhiro; Imamizu, Hiroshi

    2012-01-01

    Learning, according to Oxford Dictionary, is “to gain knowledge or skill by studying, from experience, from being taught, etc.” In order to learn from experience, the central nervous system has to decide what action leads to what consequence, and temporal perception plays a critical role in determining the causality between actions and consequences. In motor adaptation, causality between action and consequence is implicitly assumed so that a subject adapts to a new environment based on the consequence caused by her action. Adaptation to visual displacement induced by prisms is a prime example; the visual error signal associated with the motor output contributes to the recovery of accurate reaching, and a delayed feedback of visual error can decrease the adaptation rate. Subjective feeling of temporal order of action and consequence, however, can be modified or even reversed when her sense of simultaneity is manipulated with an artificially delayed feedback. Our previous study (Tanaka et al., 2011; Exp. Brain Res.) demonstrated that the rate of prism adaptation was unaffected when the subjective delay of visual feedback was shortened. This study asked whether subjects could adapt to prism adaptation and whether the rate of prism adaptation was affected when the subjective temporal order was illusory reversed. Adapting to additional 100 ms delay and its sudden removal caused a positive shift of point of simultaneity in a temporal order judgment experiment, indicating an illusory reversal of action and consequence. We found that, even in this case, the subjects were able to adapt to prism displacement with the learning rate that was statistically indistinguishable to that without temporal adaptation. This result provides further evidence to the dissociation between conscious temporal perception and motor adaptation. PMID:23248609

  14. Distributed reinforcement learning for adaptive and robust network intrusion response

    NASA Astrophysics Data System (ADS)

    Malialis, Kleanthis; Devlin, Sam; Kudenko, Daniel

    2015-07-01

    Distributed denial of service (DDoS) attacks constitute a rapidly evolving threat in the current Internet. Multiagent Router Throttling is a novel approach to defend against DDoS attacks where multiple reinforcement learning agents are installed on a set of routers and learn to rate-limit or throttle traffic towards a victim server. The focus of this paper is on online learning and scalability. We propose an approach that incorporates task decomposition, team rewards and a form of reward shaping called difference rewards. One of the novel characteristics of the proposed system is that it provides a decentralised coordinated response to the DDoS problem, thus being resilient to DDoS attacks themselves. The proposed system learns remarkably fast, thus being suitable for online learning. Furthermore, its scalability is successfully demonstrated in experiments involving 1000 learning agents. We compare our approach against a baseline and a popular state-of-the-art throttling technique from the network security literature and show that the proposed approach is more effective, adaptive to sophisticated attack rate dynamics and robust to agent failures.

  15. Adaptive and perceptual learning technologies in medical education and training.

    PubMed

    Kellman, Philip J

    2013-10-01

    Recent advances in the learning sciences offer remarkable potential to improve medical education and maximize the benefits of emerging medical technologies. This article describes 2 major innovation areas in the learning sciences that apply to simulation and other aspects of medical learning: Perceptual learning (PL) and adaptive learning technologies. PL technology offers, for the first time, systematic, computer-based methods for teaching pattern recognition, structural intuition, transfer, and fluency. Synergistic with PL are new adaptive learning technologies that optimize learning for each individual, embed objective assessment, and implement mastery criteria. The author describes the Adaptive Response-Time-based Sequencing (ARTS) system, which uses each learner's accuracy and speed in interactive learning to guide spacing, sequencing, and mastery. In recent efforts, these new technologies have been applied in medical learning contexts, including adaptive learning modules for initial medical diagnosis and perceptual/adaptive learning modules (PALMs) in dermatology, histology, and radiology. Results of all these efforts indicate the remarkable potential of perceptual and adaptive learning technologies, individually and in combination, to improve learning in a variety of medical domains.

  16. An Adaptive Scaffolding E-Learning System for Middle School Students' Physics Learning

    ERIC Educational Resources Information Center

    Chen, Ching-Huei

    2014-01-01

    This study presents a framework that utilizes cognitive and motivational aspects of learning to design an adaptive scaffolding e-learning system. It addresses scaffolding processes and conditions for designing adaptive scaffolds. The features and effectiveness of this adaptive scaffolding e-learning system are discussed and evaluated. An…

  17. Genomic mutation rates that neutralize adaptive evolution and natural selection.

    PubMed

    Gerrish, Philip J; Colato, Alexandre; Sniegowski, Paul D

    2013-08-01

    When mutation rates are low, natural selection remains effective, and increasing the mutation rate can give rise to an increase in adaptation rate. When mutation rates are high to begin with, however, increasing the mutation rate may have a detrimental effect because of the overwhelming presence of deleterious mutations. Indeed, if mutation rates are high enough: (i) adaptive evolution may be neutralized, resulting in a zero (or negative) adaptation rate despite the continued availability of adaptive and/or compensatory mutations, or (ii) natural selection may be neutralized, because the fitness of lineages bearing adaptive and/or compensatory mutations--whether established or newly arising--is eroded by excessive mutation, causing such lineages to decline in frequency. We apply these two criteria to a standard model of asexual adaptive evolution and derive mathematical expressions--some new, some old in new guise--delineating the mutation rates under which either adaptive evolution or natural selection is neutralized. The expressions are simple and require no a priori knowledge of organism- and/or environment-specific parameters. Our discussion connects these results to each other and to previous theory, showing convergence or equivalence of the different results in most cases.

  18. Genomic mutation rates that neutralize adaptive evolution and natural selection.

    PubMed

    Gerrish, Philip J; Colato, Alexandre; Sniegowski, Paul D

    2013-08-01

    When mutation rates are low, natural selection remains effective, and increasing the mutation rate can give rise to an increase in adaptation rate. When mutation rates are high to begin with, however, increasing the mutation rate may have a detrimental effect because of the overwhelming presence of deleterious mutations. Indeed, if mutation rates are high enough: (i) adaptive evolution may be neutralized, resulting in a zero (or negative) adaptation rate despite the continued availability of adaptive and/or compensatory mutations, or (ii) natural selection may be neutralized, because the fitness of lineages bearing adaptive and/or compensatory mutations--whether established or newly arising--is eroded by excessive mutation, causing such lineages to decline in frequency. We apply these two criteria to a standard model of asexual adaptive evolution and derive mathematical expressions--some new, some old in new guise--delineating the mutation rates under which either adaptive evolution or natural selection is neutralized. The expressions are simple and require no a priori knowledge of organism- and/or environment-specific parameters. Our discussion connects these results to each other and to previous theory, showing convergence or equivalence of the different results in most cases. PMID:23720539

  19. M-Learning: Implications in Learning Domain Specificities, Adaptive Learning, Feedback, Augmented Reality, and the Future of Online Learning

    ERIC Educational Resources Information Center

    Squires, David R.

    2014-01-01

    The aim of this paper is to examine the potential and effectiveness of m-learning in the field of Education and Learning domains. The purpose of this research is to illustrate how mobile technology can and is affecting novel change in instruction, from m-learning and the link to adaptive learning, to the uninitiated learner and capacities of…

  20. Adaptive Device Context Based Mobile Learning Systems

    ERIC Educational Resources Information Center

    Pu, Haitao; Lin, Jinjiao; Song, Yanwei; Liu, Fasheng

    2011-01-01

    Mobile learning is e-learning delivered through mobile computing devices, which represents the next stage of computer-aided, multi-media based learning. Therefore, mobile learning is transforming the way of traditional education. However, as most current e-learning systems and their contents are not suitable for mobile devices, an approach for…

  1. A generic mechanism for adaptive growth rate regulation.

    PubMed

    Furusawa, Chikara; Kaneko, Kunihiko

    2008-01-01

    How can a microorganism adapt to a variety of environmental conditions despite the existence of a limited number of signal transduction mechanisms? We show that for any growing cells whose gene expression fluctuate stochastically, the adaptive cellular state is inevitably selected by noise, even without a specific signal transduction network for it. In general, changes in protein concentration in a cell are given by its synthesis minus dilution and degradation, both of which are proportional to the rate of cell growth. In an adaptive state with a higher growth speed, both terms are large and balanced. Under the presence of noise in gene expression, the adaptive state is less affected by stochasticity since both the synthesis and dilution terms are large, while for a nonadaptive state both the terms are smaller so that cells are easily kicked out of the original state by noise. Hence, escape time from a cellular state and the cellular growth rate are negatively correlated. This leads to a selection of adaptive states with higher growth rates, and model simulations confirm this selection to take place in general. The results suggest a general form of adaptation that has never been brought to light--a process that requires no specific mechanisms for sensory adaptation. The present scheme may help explain a wide range of cellular adaptive responses including the metabolic flux optimization for maximal cell growth.

  2. The Influence of Learning Behaviour on Team Adaptability

    ERIC Educational Resources Information Center

    Murray, Peter A.; Millett, Bruce

    2011-01-01

    Multiple contexts shape team activities and how they learn, and group learning is a dynamic construct that reflects a repertoire of potential behaviour. The purpose of this developmental paper is to examine how better learning behaviours in semi-autonomous teams improves the level of team adaptability and performance. The discussion suggests that…

  3. Keeping up with a warming world; assessing the rate of adaptation to climate change.

    PubMed

    Visser, Marcel E

    2008-03-22

    The pivotal question in the debate on the ecological effects of climate change is whether species will be able to adapt fast enough to keep up with their changing environment. If we establish the maximal rate of adaptation, this will set an upper limit to the rate at which temperatures can increase without loss of biodiversity. The rate of adaptation will primarily be set by the rate of microevolution since (i) phenotypic plasticity alone is not sufficient as reaction norms will no longer be adaptive and hence microevolution on the reaction norm is needed, (ii) learning will be favourable to the individual but cannot be passed on to the next generations, (iii) maternal effects may play a role but, as with other forms of phenotypic plasticity, the response of offspring to the maternal cues will no longer be adaptive in a changing environment, and (iv) adaptation via immigration of individuals with genotypes adapted to warmer environments also involves microevolution as these genotypes are better adapted in terms of temperature, but not in terms of, for instance, photoperiod.Long-term studies on wild populations with individually known animals play an essential role in detecting and understanding the temporal trends in life-history traits, and to estimate the heritability of, and selection pressures on, life-history traits. However, additional measurements on other trophic levels and on the mechanisms underlying phenotypic plasticity are needed to predict the rate of microevolution, especially under changing conditions. Using this knowledge on heritability of, and selection on, life-history traits, in combination with climate scenarios, we will be able to predict the rate of adaptation for different climate scenarios. The final step is to use ecoevolutionary dynamical models to make the link to population viability and from there to biodiversity loss for those scenarios where the rate of adaptation is insufficient.

  4. Keeping up with a warming world; assessing the rate of adaptation to climate change

    PubMed Central

    Visser, Marcel E

    2008-01-01

    The pivotal question in the debate on the ecological effects of climate change is whether species will be able to adapt fast enough to keep up with their changing environment. If we establish the maximal rate of adaptation, this will set an upper limit to the rate at which temperatures can increase without loss of biodiversity. The rate of adaptation will primarily be set by the rate of microevolution since (i) phenotypic plasticity alone is not sufficient as reaction norms will no longer be adaptive and hence microevolution on the reaction norm is needed, (ii) learning will be favourable to the individual but cannot be passed on to the next generations, (iii) maternal effects may play a role but, as with other forms of phenotypic plasticity, the response of offspring to the maternal cues will no longer be adaptive in a changing environment, and (iv) adaptation via immigration of individuals with genotypes adapted to warmer environments also involves microevolution as these genotypes are better adapted in terms of temperature, but not in terms of, for instance, photoperiod. Long-term studies on wild populations with individually known animals play an essential role in detecting and understanding the temporal trends in life-history traits, and to estimate the heritability of, and selection pressures on, life-history traits. However, additional measurements on other trophic levels and on the mechanisms underlying phenotypic plasticity are needed to predict the rate of microevolution, especially under changing conditions. Using this knowledge on heritability of, and selection on, life-history traits, in combination with climate scenarios, we will be able to predict the rate of adaptation for different climate scenarios. The final step is to use ecoevolutionary dynamical models to make the link to population viability and from there to biodiversity loss for those scenarios where the rate of adaptation is insufficient. PMID:18211875

  5. Engaging a moving target: Adapting to rates of climate change

    NASA Astrophysics Data System (ADS)

    Shayegh, S.; Caldeira, K.; Moreno-Cruz, J.

    2015-12-01

    Climate change is affecting the planet and its human and natural systems at an increasing rate. As temperatures continue to rise, the international community has increasingly been considering adaptation measures to prepare for future climate change. However, most discussion around adaptation strategies has focused on preparedness for some expected amount of climate change impacts, e.g. 2 meters sea level rise. In this study, we discuss adaptation to rates of change as an alternative conceptual framework for thinking about adaptation. Adaptation is not only about adapting to amounts of change, but the rate at which these changes occur is also critically important. We ground our discussion with an example of optimal coastal investment in the face of ongoing sea level rise. Sea level rise threatens coastal assets. Finite resources could be devoted to building infrastructure further inland or to building coastal defense systems. A possible policy response could be to create a "no-build" coastal buffer zone that anticipates a future higher sea level. We present a quantitative model that illustrates the interplay among various important factors (rate of sea level rise, discount rate, capital depreciation rate, attractiveness of coastal land, etc). For some cases, strategies that combine periodic defensive investments (e.g. dikes) with planned retreat can maximize welfare when adapting to rates of climate change. In other cases, planned retreat may be optimal. It is important to prepare for ongoing increasing amounts of climate change. Preparing for a fixed amount of climate change can lead to a suboptimal solution. Climate is likely to continue changing throughout this century and beyond. To reduce adverse climate impacts, ecosystems and human systems will need to continuously adapt to a moving target.

  6. An Adaptive E-Learning System Based on Students' Learning Styles: An Empirical Study

    ERIC Educational Resources Information Center

    Drissi, Samia; Amirat, Abdelkrim

    2016-01-01

    Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia.…

  7. How to Represent Adaptation in e-Learning with IMS Learning Design

    ERIC Educational Resources Information Center

    Burgos, Daniel; Tattersall, Colin; Koper, Rob

    2007-01-01

    Adaptation in e-learning has been an important research topic for the last few decades in computer-based education. In adaptivity the behaviour of the user triggers some actions in the system that guides the learning process. In adaptability, the user makes changes and takes decisions. Progressing from computer-based training and adaptive…

  8. A Competency-Based Guided-Learning Algorithm Applied on Adaptively Guiding E-Learning

    ERIC Educational Resources Information Center

    Hsu, Wei-Chih; Li, Cheng-Hsiu

    2015-01-01

    This paper presents a new algorithm called competency-based guided-learning algorithm (CBGLA), which can be applied on adaptively guiding e-learning. Computational process analysis and mathematical derivation of competency-based learning (CBL) were used to develop the CBGLA. The proposed algorithm could generate an effective adaptively guiding…

  9. A Framework for Adaptive E-Learning Based on Distributed Re-Usable Learning Activities.

    ERIC Educational Resources Information Center

    Brusilovsky, Peter; Nijhavan, Hemanta

    This paper suggests that a way to the new generation of powerful E-learning systems starts on the crossroads of two emerging fields: courseware re-use and adaptive educational systems. The paper presents the KnowledgeTree, a framework for adaptive E-learning based on distributed re-usable learning activities currently under development. The goal…

  10. Diminished neural adaptation during implicit learning in autism.

    PubMed

    Schipul, Sarah E; Just, Marcel Adam

    2016-01-15

    Neuroimaging studies have shown evidence of disrupted neural adaptation during learning in individuals with autism spectrum disorder (ASD) in several types of tasks, potentially stemming from frontal-posterior cortical underconnectivity (Schipul et al., 2012). The aim of the current study was to examine neural adaptations in an implicit learning task that entails participation of frontal and posterior regions. Sixteen high-functioning adults with ASD and sixteen neurotypical control participants were trained on and performed an implicit dot pattern prototype learning task in a functional magnetic resonance imaging (fMRI) session. During the preliminary exposure to the type of implicit prototype learning task later to be used in the scanner, the ASD participants took longer than the neurotypical group to learn the task, demonstrating altered implicit learning in ASD. After equating task structure learning, the two groups' brain activation differed during their learning of a new prototype in the subsequent scanning session. The main findings indicated that neural adaptations in a distributed task network were reduced in the ASD group, relative to the neurotypical group, and were related to ASD symptom severity. Functional connectivity was reduced and did not change as much during learning for the ASD group, and was related to ASD symptom severity. These findings suggest that individuals with ASD show altered neural adaptations during learning, as seen in both activation and functional connectivity measures. This finding suggests why many real-world implicit learning situations may pose special challenges for ASD.

  11. A Model of Adaptive Language Learning

    ERIC Educational Resources Information Center

    Woodrow, Lindy J.

    2006-01-01

    This study applies theorizing from educational psychology and language learning to hypothesize a model of language learning that takes into account affect, motivation, and language learning strategies. The study employed a questionnaire to assess variables of motivation, self-efficacy, anxiety, and language learning strategies. The sample…

  12. Poststroke Hemiparesis Impairs the Rate but not Magnitude of Adaptation of Spatial and Temporal Locomotor Features

    PubMed Central

    Savin, Douglas N.; Tseng, Shih-Chiao; Whitall, Jill; Morton, Susanne M.

    2015-01-01

    Background Persons with stroke and hemiparesis walk with a characteristic pattern of spatial and temporal asymmetry that is resistant to most traditional interventions. It was recently shown in nondisabled persons that the degree of walking symmetry can be readily altered via locomotor adaptation. However, it is unclear whether stroke-related brain damage affects the ability to adapt spatial or temporal gait symmetry. Objective Determine whether locomotor adaptation to a novel swing phase perturbation is impaired in persons with chronic stroke and hemiparesis. Methods Participants with ischemic stroke (14) and nondisabled controls (12) walked on a treadmill before, during, and after adaptation to a unilateral perturbing weight that resisted forward leg movement. Leg kinematics were measured bilaterally, including step length and single-limb support (SLS) time symmetry, limb angle center of oscillation, and interlimb phasing, and magnitude of “initial” and “late” locomotor adaptation rates were determined. Results All participants had similar magnitudes of adaptation and similar initial adaptation rates both spatially and temporally. All 14 participants with stroke and baseline asymmetry temporarily walked with improved SLS time symmetry after adaptation. However, late adaptation rates poststroke were decreased (took more strides to achieve adaptation) compared with controls. Conclusions Mild to moderate hemiparesis does not interfere with the initial acquisition of novel symmetrical gait patterns in both the spatial and temporal domains, though it does disrupt the rate at which “late” adaptive changes are produced. Impairment of the late, slow phase of learning may be an important rehabilitation consideration in this patient population. PMID:22367915

  13. Adaptive Estimation of Intravascular Shear Rate Based on Parameter Optimization

    NASA Astrophysics Data System (ADS)

    Nitta, Naotaka; Takeda, Naoto

    2008-05-01

    The relationships between the intravascular wall shear stress, controlled by flow dynamics, and the progress of arteriosclerosis plaque have been clarified by various studies. Since the shear stress is determined by the viscosity coefficient and shear rate, both factors must be estimated accurately. In this paper, an adaptive method for improving the accuracy of quantitative shear rate estimation was investigated. First, the parameter dependence of the estimated shear rate was investigated in terms of the differential window width and the number of averaged velocity profiles based on simulation and experimental data, and then the shear rate calculation was optimized. The optimized result revealed that the proposed adaptive method of shear rate estimation was effective for improving the accuracy of shear rate calculation.

  14. Rate-Agnostic (Causal) Structure Learning

    PubMed Central

    Plis, Sergey; Danks, David; Freeman, Cynthia; Calhoun, Vince

    2016-01-01

    Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a “rate-agnostic” manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling. PMID:27182188

  15. An Intelligent E-Learning System Based on Learner Profiling and Learning Resources Adaptation

    ERIC Educational Resources Information Center

    Tzouveli, Paraskevi; Mylonas, Phivos; Kollias, Stefanos

    2008-01-01

    Taking advantage of the continuously improving, web-based learning systems plays an important role for self-learning, especially in the case of working people. Nevertheless, learning systems do not generally adapt to learners' profiles. Learners have to spend a lot of time before reaching the learning goal that is compatible with their knowledge…

  16. A Learning Style Perspective to Investigate the Necessity of Developing Adaptive Learning Systems

    ERIC Educational Resources Information Center

    Hwang, Gwo-Jen; Sung, Han-Yu; Hung, Chun-Ming; Huang, Iwen

    2013-01-01

    Learning styles are considered to be one of the factors that need to be taken into account in developing adaptive learning systems. However, few studies have been conducted to investigate if students have the ability to choose the best-fit e-learning systems or content presentation styles for themselves in terms of learning style perspective. In…

  17. Learning Experiences Reuse Based on an Ontology Modeling to Improve Adaptation in E-Learning Systems

    ERIC Educational Resources Information Center

    Hadj M'tir, Riadh; Rumpler, Béatrice; Jeribi, Lobna; Ben Ghezala, Henda

    2014-01-01

    Current trends in e-Learning focus mainly on personalizing and adapting the learning environment and learning process. Although their increasingly number, theses researches often ignore the concepts of capitalization and reuse of learner experiences which can be exploited later by other learners. Thus, the major challenge of distance learning is…

  18. Adaptive strategies for cumulative cultural learning.

    PubMed

    Ehn, Micael; Laland, Kevin

    2012-05-21

    The demographic and ecological success of our species is frequently attributed to our capacity for cumulative culture. However, it is not yet known how humans combine social and asocial learning to generate effective strategies for learning in a cumulative cultural context. Here we explore how cumulative culture influences the relative merits of various pure and conditional learning strategies, including pure asocial and social learning, critical social learning, conditional social learning and individual refiner strategies. We replicate the Rogers' paradox in the cumulative setting. However, our analysis suggests that strategies that resolved Rogers' paradox in a non-cumulative setting may not necessarily evolve in a cumulative setting, thus different strategies will optimize cumulative and non-cumulative cultural learning.

  19. Functionally dissociable influences on learning rate in a dynamic environment

    PubMed Central

    McGuire, Joseph T.; Nassar, Matthew R.; Gold, Joshua I.; Kable, Joseph W.

    2015-01-01

    Summary Maintaining accurate beliefs in a changing environment requires dynamically adapting the rate at which one learns from new experiences. Beliefs should be stable in the face of noisy data, but malleable in periods of change or uncertainty. Here we used computational modeling, psychophysics and fMRI to show that adaptive learning is not a unitary phenomenon in the brain. Rather, it can be decomposed into three computationally and neuroanatomically distinct factors that were evident in human subjects performing a spatial-prediction task: (1) surprise-driven belief updating, related to BOLD activity in visual cortex; (2) uncertainty-driven belief updating, related to anterior prefrontal and parietal activity; and (3) reward-driven belief updating, a context-inappropriate behavioral tendency related to activity in ventral striatum. These distinct factors converged in a core system governing adaptive learning. This system, which included dorsomedial frontal cortex, responded to all three factors and predicted belief updating both across trials and across individuals. PMID:25459409

  20. Enhancing Student Motivation and Learning within Adaptive Tutors

    ERIC Educational Resources Information Center

    Ostrow, Korinn S.

    2015-01-01

    My research is rooted in improving K-12 educational practice using motivational facets made possible through adaptive tutoring systems. In an attempt to isolate best practices within the science of learning, I conduct randomized controlled trials within ASSISTments, an online adaptive tutoring system that provides assistance and assessment to…

  1. Implementation of an Adaptive Learning System Using a Bayesian Network

    ERIC Educational Resources Information Center

    Yasuda, Keiji; Kawashima, Hiroyuki; Hata, Yoko; Kimura, Hiroaki

    2015-01-01

    An adaptive learning system is proposed that incorporates a Bayesian network to efficiently gauge learners' understanding at the course-unit level. Also, learners receive content that is adapted to their measured level of understanding. The system works on an iPad via the Edmodo platform. A field experiment using the system in an elementary school…

  2. Integrating Adaptive Games in Student-Centered Virtual Learning Environments

    ERIC Educational Resources Information Center

    del Blanco, Angel; Torrente, Javier; Moreno-Ger, Pablo; Fernandez-Manjon, Baltasar

    2010-01-01

    The increasing adoption of e-Learning technology is facing new challenges, such as how to produce student-centered systems that can be adapted to each student's needs. In this context, educational video games are proposed as an ideal medium to facilitate adaptation and tracking of students' performance for assessment purposes, but integrating the…

  3. Learning rates of lq coefficient regularization learning with gaussian kernel.

    PubMed

    Lin, Shaobo; Zeng, Jinshan; Fang, Jian; Xu, Zongben

    2014-10-01

    Regularization is a well-recognized powerful strategy to improve the performance of a learning machine and l(q) regularization schemes with 0 < q < ∞ are central in use. It is known that different q leads to different properties of the deduced estimators, say, l(2) regularization leads to a smooth estimator, while l(1) regularization leads to a sparse estimator. Then how the generalization capability of l(q) regularization learning varies with q is worthy of investigation. In this letter, we study this problem in the framework of statistical learning theory. Our main results show that implementing l(q) coefficient regularization schemes in the sample-dependent hypothesis space associated with a gaussian kernel can attain the same almost optimal learning rates for all 0 < q < ∞. That is, the upper and lower bounds of learning rates for l(q) regularization learning are asymptotically identical for all 0 < q < ∞. Our finding tentatively reveals that in some modeling contexts, the choice of q might not have a strong impact on the generalization capability. From this perspective, q can be arbitrarily specified, or specified merely by other nongeneralization criteria like smoothness, computational complexity or sparsity.

  4. Combining Adaptive Hypermedia with Project and Case-Based Learning

    ERIC Educational Resources Information Center

    Papanikolaou, Kyparisia; Grigoriadou, Maria

    2009-01-01

    In this article we investigate the design of educational hypermedia based on constructivist learning theories. According to the principles of project and case-based learning we present the design rational of an Adaptive Educational Hypermedia system prototype named MyProject; learners working with MyProject undertake a project and the system…

  5. Adaptive versus Learner Control in a Multiple Intelligence Learning Environment

    ERIC Educational Resources Information Center

    Kelly, Declan

    2008-01-01

    Within the field of technology enhanced learning, adaptive educational systems offer an advanced form of learning environment that attempts to meet the needs of different students. Such systems capture and represent, for each student, various characteristics such as knowledge and traits in an individual learner model. Subsequently, using the…

  6. Adaptive E-Learning Environments: Research Dimensions and Technological Approaches

    ERIC Educational Resources Information Center

    Di Bitonto, Pierpaolo; Roselli, Teresa; Rossano, Veronica; Sinatra, Maria

    2013-01-01

    One of the most closely investigated topics in e-learning research has always been the effectiveness of adaptive learning environments. The technological evolutions that have dramatically changed the educational world in the last six decades have allowed ever more advanced and smarter solutions to be proposed. The focus of this paper is to depict…

  7. RASCAL: A Rudimentary Adaptive System for Computer-Aided Learning.

    ERIC Educational Resources Information Center

    Stewart, John Christopher

    Both the background of computer-assisted instruction (CAI) systems in general and the requirements of a computer-aided learning system which would be a reasonable assistant to a teacher are discussed. RASCAL (Rudimentary Adaptive System for Computer-Aided Learning) is a first attempt at defining a CAI system which would individualize the learning…

  8. Increased Adaptation Rates and Reduction in Trial-by-Trial Variability in Subjects with Cerebral Palsy Following a Multi-session Locomotor Adaptation Training

    PubMed Central

    Mawase, Firas; Bar-Haim, Simona; Joubran, Katherin; Rubin, Lihi; Karniel, Amir; Shmuelof, Lior

    2016-01-01

    Cerebral Palsy (CP) results from an insult to the developing brain and is associated with deficits in locomotor and manual skills and in sensorimotor adaptation. We hypothesized that the poor sensorimotor adaptation in persons with CP is related to their high execution variability and does not reflect a general impairment in adaptation learning. We studied the interaction between performance variability and adaptation deficits using a multi-session locomotor adaptation design in persons with CP. Six adolescents with diplegic CP were exposed, during a period of 15 weeks, to a repeated split-belt treadmill perturbation spread over 30 sessions and were tested again 6 months after the end of training. Compared to age-matched healthy controls, subjects with CP showed poor adaptation and high execution variability in the first exposure to the perturbation. Following training they showed marked reduction in execution variability and an increase in learning rates. The reduction in variability and the improvement in adaptation were highly correlated in the CP group and were retained 6 months after training. Interestingly, despite reducing their variability in the washout phase, subjects with CP did not improve learning rates during washout phases that were introduced only four times during the experiment. Our results suggest that locomotor adaptation in subjects with CP is related to their execution variability. Nevertheless, while variability reduction is generalized to other locomotor contexts, the development of savings requires both reduction in execution variability and multiple exposures to the perturbation. PMID:27199721

  9. Adaptation of the Patterns of Adaptive Learning Scales (PALS) to Turkish Students: Factorial Validity and Reliability

    ERIC Educational Resources Information Center

    Cikrikci-Demirtash, R. Nukhet

    2005-01-01

    The study presented in this article was conducted to determine psychometric features of scales for Turkish students by adapting the Patterns of Adaptive Learning Scales (PALS) developed by Midgley and others (2000) to the Turkish language in order to measure personal and classroom goal orientations. The scales were developed to test…

  10. A neural learning classifier system with self-adaptive constructivism for mobile robot control.

    PubMed

    Hurst, Jacob; Bull, Larry

    2006-01-01

    For artificial entities to achieve true autonomy and display complex lifelike behavior, they will need to exploit appropriate adaptable learning algorithms. In this context adaptability implies flexibility guided by the environment at any given time and an open-ended ability to learn appropriate behaviors. This article examines the use of constructivism-inspired mechanisms within a neural learning classifier system architecture that exploits parameter self-adaptation as an approach to realize such behavior. The system uses a rule structure in which each rule is represented by an artificial neural network. It is shown that appropriate internal rule complexity emerges during learning at a rate controlled by the learner and that the structure indicates underlying features of the task. Results are presented in simulated mazes before moving to a mobile robot platform.

  11. Motor sequence learning and motor adaptation in primary cervical dystonia.

    PubMed

    Katschnig-Winter, Petra; Schwingenschuh, Petra; Davare, Marco; Sadnicka, Anna; Schmidt, Reinhold; Rothwell, John C; Bhatia, Kailash P; Edwards, Mark J

    2014-06-01

    Motor sequence learning and motor adaptation rely on overlapping circuits predominantly involving the basal ganglia and cerebellum. Given the importance of these brain regions to the pathophysiology of primary dystonia, and the previous finding of abnormal motor sequence learning in DYT1 gene carriers, we explored motor sequence learning and motor adaptation in patients with primary cervical dystonia. We recruited 12 patients with cervical dystonia and 11 healthy controls matched for age. Subjects used a joystick to move a cursor from a central starting point to radial targets as fast and accurately as possible. Using this device, we recorded baseline motor performance, motor sequence learning and a visuomotor adaptation task. Patients with cervical dystonia had a significantly higher peak velocity than controls. Baseline performance with random target presentation was otherwise normal. Patients and controls had similar levels of motor sequence learning and motor adaptation. Our patients had significantly higher peak velocity compared to controls, with similar movement times, implying a different performance strategy. The preservation of motor sequence learning in cervical dystonia patients contrasts with the previously observed deficit seen in patients with DYT1 gene mutations, supporting the hypothesis of differing pathophysiology in different forms of primary dystonia. Normal motor adaptation is an interesting finding. With our paradigm we did not find evidence that the previously documented cerebellar abnormalities in cervical dystonia have a behavioral correlate, and thus could be compensatory or reflect "contamination" rather than being directly pathological.

  12. Individual Variability in Sensorimotor Network Functional Connectivity Correlates With the Rate of Early Visuomotor Adaptation

    NASA Technical Reports Server (NTRS)

    Cassady, K.; Ruitenberg, M.; Koppelmans, V.; DeDios, Y.; Gadd, N.; Wood, S.; Reuter-Lorenz, P.; Riascos, R.; Kofman, I.; Bloomberg, J.; Mulavara, A.; Seidler, R.

    2016-01-01

    Sensorimotor adaptation is a type of procedural motor learning that enables individuals to preserve accurate movements in the presence of external or internal perturbations. Adaptation learning can be divided into an early, more cognitively demanding stage, and a later, more automatic stage. In recent years, several investigations have identified significant associations between sensorimotor adaptation and brain structure and function. However, the question of whether individual variability in functional connectivity strength is predictive of sensorimotor adaptation performance has been largely unaddressed. In the present study, we investigate whether such variability in early sensorimotor adaptation is associated with individual differences in resting-state functional connectivity. We used resting state functional magnetic resonance imaging (rs-fMRI) to estimate functional connectivity strength using hypothesis-driven (seed-to-voxel) and hypothesis-free (voxel-to-voxel) approaches. For the hypothesis-driven analysis, we selected several regions of interest (ROIs) from sensorimotor and default mode networks of the brain. We then correlated these connectivity measures with the rate of early learning during a visuomotor adaptation task in 16 healthy participants. For this task, participants lay supine in the MRI scanner and moved an MRI-compatible dual axis joystick with their right hand to hit targets presented on a screen. Each movement was initiated from the central position on the display screen. Participants were instructed to move the cursor to the target as quickly as possible by moving the joystick, and to hold the cursor within the target until it disappeared. They were then instructed to release the joystick handle after target disappearance, allowing the cursor to re-center for the next trial. Performance was assessed by measuring direction error (DE), defined as the angle between the line from the start to the target position, and the line from the start

  13. Adaptive downsampling to improve image compression at low bit rates.

    PubMed

    Lin, Weisi; Dong, Li

    2006-09-01

    At low bit rates, better coding quality can be achieved by downsampling the image prior to compression and estimating the missing portion after decompression. This paper presents a new algorithm in such a paradigm, based on the adaptive decision of appropriate downsampling directions/ratios and quantization steps, in order to achieve higher coding quality with low bit rates with the consideration of local visual significance. The full-resolution image can be restored from the DCT coefficients of the downsampled pixels so that the spatial interpolation required otherwise is avoided. The proposed algorithm significantly raises the critical bit rate to approximately 1.2 bpp, from 0.15-0.41 bpp in the existing downsample-prior-to-JPEG schemes and, therefore, outperforms the standard JPEG method in a much wider bit-rate scope. The experiments have demonstrated better PSNR improvement over the existing techniques before the critical bit rate. In addition, the adaptive mode decision not only makes the critical bit rate less image-independent, but also automates the switching coders in variable bit-rate applications, since the algorithm turns to the standard JPEG method whenever it is necessary at higher bit rates.

  14. Evolutionary and adaptive learning in complex markets: a brief summary

    NASA Astrophysics Data System (ADS)

    Hommes, Cars H.

    2007-06-01

    We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply cobweb model. We discuss and combine two different approaches on learning. According to the adaptive learning approach, agents behave as econometricians using time series observations to form expectations, and update the parameters as more observations become available. This approach has become popular in macro. The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory experiments with human subjects.

  15. Learning & retention in adaptive serious games.

    PubMed

    Bergeron, Bryan P

    2008-01-01

    Serious games are being actively explored as supplements to and, in some cases, replacement for traditional didactic lectures and computer-based instruction in venues ranging from medicine to the military. As part of an intelligent tutoring system (ITS) for nuclear event first responders, we designed and evaluated two serious games that were integrated with adaptive multimedia content. Results reveal that there was no decay in score six weeks following game-based training, which contrasts with results expected with traditional training. This study suggests that adaptive serious games may help integrate didactic content presented though conventional means.

  16. Applying statistical process control to the adaptive rate control problem

    NASA Astrophysics Data System (ADS)

    Manohar, Nelson R.; Willebeek-LeMair, Marc H.; Prakash, Atul

    1997-12-01

    Due to the heterogeneity and shared resource nature of today's computer network environments, the end-to-end delivery of multimedia requires adaptive mechanisms to be effective. We present a framework for the adaptive streaming of heterogeneous media. We introduce the application of online statistical process control (SPC) to the problem of dynamic rate control. In SPC, the goal is to establish (and preserve) a state of statistical quality control (i.e., controlled variability around a target mean) over a process. We consider the end-to-end streaming of multimedia content over the internet as the process to be controlled. First, at each client, we measure process performance and apply statistical quality control (SQC) with respect to application-level requirements. Then, we guide an adaptive rate control (ARC) problem at the server based on the statistical significance of trends and departures on these measurements. We show this scheme facilitates handling of heterogeneous media. Last, because SPC is designed to monitor long-term process performance, we show that our online SPC scheme could be used to adapt to various degrees of long-term (network) variability (i.e., statistically significant process shifts as opposed to short-term random fluctuations). We develop several examples and analyze its statistical behavior and guarantees.

  17. Teacher Adaptation to Open Learning Spaces

    ERIC Educational Resources Information Center

    Alterator, Scott; Deed, Craig

    2013-01-01

    The "open classroom" emerged as a reaction against the industrial-era enclosed and authoritarian classroom. Although contemporary school architecture continues to incorporate and express ideas of openness, more research is needed about how teachers adapt to new and different built contexts. Our purpose is to identify teacher reaction to…

  18. The immune system, adaptation, and machine learning

    NASA Astrophysics Data System (ADS)

    Farmer, J. Doyne; Packard, Norman H.; Perelson, Alan S.

    1986-10-01

    The immune system is capable of learning, memory, and pattern recognition. By employing genetic operators on a time scale fast enough to observe experimentally, the immune system is able to recognize novel shapes without preprogramming. Here we describe a dynamical model for the immune system that is based on the network hypothesis of Jerne, and is simple enough to simulate on a computer. This model has a strong similarity to an approach to learning and artificial intelligence introduced by Holland, called the classifier system. We demonstrate that simple versions of the classifier system can be cast as a nonlinear dynamical system, and explore the analogy between the immune and classifier systems in detail. Through this comparison we hope to gain insight into the way they perform specific tasks, and to suggest new approaches that might be of value in learning systems.

  19. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1978-01-01

    A learning control system and its utilization as a flight control system for F-8 Digital Fly-By-Wire (DFBW) research aircraft is studied. The system has the ability to adjust a gain schedule to account for changing plant characteristics and to improve its performance and the plant's performance in the course of its own operation. Three subsystems are detailed: (1) the information acquisition subsystem which identifies the plant's parameters at a given operating condition; (2) the learning algorithm subsystem which relates the identified parameters to predetermined analytical expressions describing the behavior of the parameters over a range of operating conditions; and (3) the memory and control process subsystem which consists of the collection of updated coefficients (memory) and the derived control laws. Simulation experiments indicate that the learning control system is effective in compensating for parameter variations caused by changes in flight conditions.

  20. Resident Ratings of Communication Skills Using the Kalamazoo Adapted Checklist

    PubMed Central

    Porcerelli, John H.; Brennan, Simone; Carty, Jennifer; Ziadni, Maisa; Markova, Tsveti

    2015-01-01

    Background The Kalamazoo Essential Elements Communication Checklist–Adapted (KEECC-A) is a well-regarded instrument for evaluating communication and interpersonal skills. To date, little research has been conducted that assesses the accuracy of resident self-ratings of their communication skills. Objective To assess whether residents can accurately self-rate communication skills, using the KEECC-A, during an objective structured clinical examination (OSCE). Methods A group of 104 residents from 8 specialties completed a multistation OSCE as part of an institutional communication skills curriculum conducted at a single institution. Standardized patients (SPs) and observers were trained in rating communication skills using the KEECC-A. Standardized patient ratings and resident self-ratings were completed immediately following each OSCE encounter, and trained observers rated archived videotapes of the encounters. Results Resident self-ratings and SP ratings using the KEECC-A were significantly correlated (r104 = 0.238, P = .02), as were resident self-ratings and observer ratings (r104 = 0.284, P = .004). The correlation between the SP ratings and observer (r104 = 0.378, P = .001) ratings were larger in magnitude, but not significantly different (P > .05) from resident/SP or resident/observer correlations. Conclusions The results suggest that residents, with a modicum of training using the KEECC-A, can accurately rate their own communication and interpersonal skills during an OSCE. Using trained observers to rate resident communication skills provides a unique opportunity for evaluating SP and resident self-ratings. Our findings also lend further support for the reliability and validity of the KEECC-A. PMID:26457156

  1. Professional Learning to Nurture Adaptive Teachers

    ERIC Educational Resources Information Center

    Lee, Kar-Tin

    2013-01-01

    This paper presents the findings of a study conducted in China to identify the potential benefits of incorporating robotics as an educational tool for 100 primary and 320 secondary school teachers of general technology. The Professional Learning Program was conducted from 2010-2013 in China. The major focus of the program was on the development…

  2. Women, Subjectivities and Learning to Be Adaptable

    ERIC Educational Resources Information Center

    Cavanagh, Jillian

    2010-01-01

    Purpose: The purpose of this paper is to advance understandings of the subjectivities that influence auxiliary-level female employees' work and learning experiences in general legal practice. Moreover, the aim is to maximise the opportunities for these workers. Design/methodology/approach: A broader critical ethnographic study investigated…

  3. Complex ordering in spin networks: Critical role of adaptation rate for dynamically evolving interactions

    NASA Astrophysics Data System (ADS)

    Pathak, Anand; Sinha, Sitabhra

    2015-09-01

    Many complex systems can be represented as networks of dynamical elements whose states evolve in response to interactions with neighboring elements, noise and external stimuli. The collective behavior of such systems can exhibit remarkable ordering phenomena such as chimera order corresponding to coexistence of ordered and disordered regions. Often, the interactions in such systems can also evolve over time responding to changes in the dynamical states of the elements. Link adaptation inspired by Hebbian learning, the dominant paradigm for neuronal plasticity, has been earlier shown to result in structural balance by removing any initial frustration in a system that arises through conflicting interactions. Here we show that the rate of the adaptive dynamics for the interactions is crucial in deciding the emergence of different ordering behavior (including chimera) and frustration in networks of Ising spins. In particular, we observe that small changes in the link adaptation rate about a critical value result in the system exhibiting radically different energy landscapes, viz., smooth landscape corresponding to balanced systems seen for fast learning, and rugged landscapes corresponding to frustrated systems seen for slow learning.

  4. Auditory-Perceptual Learning Improves Speech Motor Adaptation in Children

    PubMed Central

    Shiller, Douglas M.; Rochon, Marie-Lyne

    2015-01-01

    Auditory feedback plays an important role in children’s speech development by providing the child with information about speech outcomes that is used to learn and fine-tune speech motor plans. The use of auditory feedback in speech motor learning has been extensively studied in adults by examining oral motor responses to manipulations of auditory feedback during speech production. Children are also capable of adapting speech motor patterns to perceived changes in auditory feedback, however it is not known whether their capacity for motor learning is limited by immature auditory-perceptual abilities. Here, the link between speech perceptual ability and the capacity for motor learning was explored in two groups of 5–7-year-old children who underwent a period of auditory perceptual training followed by tests of speech motor adaptation to altered auditory feedback. One group received perceptual training on a speech acoustic property relevant to the motor task while a control group received perceptual training on an irrelevant speech contrast. Learned perceptual improvements led to an enhancement in speech motor adaptation (proportional to the perceptual change) only for the experimental group. The results indicate that children’s ability to perceive relevant speech acoustic properties has a direct influence on their capacity for sensory-based speech motor adaptation. PMID:24842067

  5. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies.

    PubMed

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-01-01

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people's adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics. PMID:27282089

  6. Auditory-perceptual learning improves speech motor adaptation in children.

    PubMed

    Shiller, Douglas M; Rochon, Marie-Lyne

    2014-08-01

    Auditory feedback plays an important role in children's speech development by providing the child with information about speech outcomes that is used to learn and fine-tune speech motor plans. The use of auditory feedback in speech motor learning has been extensively studied in adults by examining oral motor responses to manipulations of auditory feedback during speech production. Children are also capable of adapting speech motor patterns to perceived changes in auditory feedback; however, it is not known whether their capacity for motor learning is limited by immature auditory-perceptual abilities. Here, the link between speech perceptual ability and the capacity for motor learning was explored in two groups of 5- to 7-year-old children who underwent a period of auditory perceptual training followed by tests of speech motor adaptation to altered auditory feedback. One group received perceptual training on a speech acoustic property relevant to the motor task while a control group received perceptual training on an irrelevant speech contrast. Learned perceptual improvements led to an enhancement in speech motor adaptation (proportional to the perceptual change) only for the experimental group. The results indicate that children's ability to perceive relevant speech acoustic properties has a direct influence on their capacity for sensory-based speech motor adaptation.

  7. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

    PubMed Central

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-01-01

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics. PMID:27282089

  8. Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

    NASA Astrophysics Data System (ADS)

    Yu, Chao; Tan, Guozhen; Lv, Hongtao; Wang, Zhen; Meng, Jun; Hao, Jianye; Ren, Fenghui

    2016-06-01

    Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

  9. Generalized perceptual learning in the absence of sensory adaptation.

    PubMed

    Harris, Hila; Gliksberg, Michael; Sagi, Dov

    2012-10-01

    Repeated performance of visual tasks leads to long-lasting increased sensitivity to the trained stimulus, a phenomenon termed perceptual learning. A ubiquitous property of visual learning is specificity: performance improvement obtained during training applies only for the trained stimulus features, which are thought to be encoded in sensory brain regions [1-3]. However, recent results show performance decrements with an increasing number of trials within a training session [4, 5]. This selective sensitivity reduction is thought to arise due to sensory adaptation [5, 6]. Here we show, using the standard texture discrimination task [7], that location specificity is a consequence of sensory adaptation; that is, it results from selective reduced sensitivity due to repeated stimulation. Observers practiced the texture task with the target presented at a fixed location within a background texture. To remove adaptation, we added task-irrelevant ("dummy") trials with the texture oriented 45° relative to the target's orientation, known to counteract adaptation [8]. The results indicate location specificity with the standard paradigm, but complete generalization to a new location when adaptation is removed. We suggest that adaptation interferes with invariant pattern-discrimination learning by inducing network-dependent changes in local visual representations.

  10. The presence of a single-nucleotide polymorphism in the BDNF gene affects the rate of locomotor adaptation after stroke.

    PubMed

    Helm, Erin E; Tyrell, Christine M; Pohlig, Ryan T; Brady, Lucas D; Reisman, Darcy S

    2016-02-01

    Induction of neural plasticity through motor learning has been demonstrated in animals and humans. Brain-derived neurotrophic factor (BDNF), a member of the neurotrophin family of growth factors, is thought to play an integral role in modulation of central nervous system plasticity during learning and motor skill recovery. Thirty percent of humans possess a single-nucleotide polymorphism on the BDNF gene (Val66Met), which has been linked to decreased activity-dependent release of BDNF. Presence of the polymorphism has been associated with altered cortical activation, short-term plasticity and altered skill acquisition, and learning in healthy humans. The impact of the Val66Met polymorphism on motor learning post-stroke has not been explored. The purpose of this study was to examine the impact of the Val66Met polymorphism in learning of a novel locomotor task in subjects with chronic stroke. It was hypothesized that subjects with the polymorphism would have an altered rate and magnitude of adaptation to a novel locomotor walking paradigm (the split-belt treadmill), compared to those without the polymorphism. The rate of adaptation was evaluated as the reduction in gait asymmetry during the first 30 (early adaptation) and last 100 (late adaptation) strides. Twenty-seven individuals with chronic stroke participated in a single session of split-belt treadmill walking and tested for the polymorphism. Step length and limb phase were measured to assess adaptation of spatial and temporal parameters of walking. The rate of adaptation of step length asymmetry differed significantly between those with and without the polymorphism, while the amount of total adaptation did not. These results suggest that chronic stroke survivors, regardless of presence or absence of the polymorphism, are able to adapt their walking pattern over a period of trial-and-error practice; however, the presence of the polymorphism influences the rate at which this is achieved. PMID:26487176

  11. Beyond adaptive-critic creative learning for intelligent mobile robots

    NASA Astrophysics Data System (ADS)

    Liao, Xiaoqun; Cao, Ming; Hall, Ernest L.

    2001-10-01

    Intelligent industrial and mobile robots may be considered proven technology in structured environments. Teach programming and supervised learning methods permit solutions to a variety of applications. However, we believe that to extend the operation of these machines to more unstructured environments requires a new learning method. Both unsupervised learning and reinforcement learning are potential candidates for these new tasks. The adaptive critic method has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning methods that goes beyond the adaptive critic method for unstructured environments. The adaptive critic is a form of reinforcement learning. A critic element provides only high level grading corrections to a cognition module that controls the action module. In the proposed system the critic's grades are modeled and forecasted, so that an anticipated set of sub-grades are available to the cognition model. The forecasting grades are interpolated and are available on the time scale needed by the action model. The success of the system is highly dependent on the accuracy of the forecasted grades and adaptability of the action module. Examples from the guidance of a mobile robot are provided to illustrate the method for simple line following and for the more complex navigation and control in an unstructured environment. The theory presented that is beyond the adaptive critic may be called creative theory. Creative theory is a form of learning that models the highest level of human learning - imagination. The application of the creative theory appears to not only be to mobile robots but also to many other forms of human endeavor such as educational learning and business forecasting. Reinforcement learning such as the adaptive critic may be applied to known problems to aid in the discovery of their solutions. The significance of creative theory is that it

  12. Controlling the local false discovery rate in the adaptive Lasso.

    PubMed

    Sampson, Joshua N; Chatterjee, Nilanjan; Carroll, Raymond J; Müller, Samuel

    2013-09-01

    The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated coefficients to zero, and its ability to serve as a variable selection procedure. Using data-adaptive weights, the adaptive Lasso modified the original procedure to increase the penalty terms for those variables estimated to be less important by ordinary least squares. Although this modified procedure attained the oracle properties, the resulting models tend to include a large number of "false positives" in practice. Here, we adapt the concept of local false discovery rates (lFDRs) so that it applies to the sequence, λn, of smoothing parameters for the adaptive Lasso. We define the lFDR for a given λn to be the probability that the variable added to the model by decreasing λn to λn-δ is not associated with the outcome, where δ is a small value. We derive the relationship between the lFDR and λn, show lFDR =1 for traditional smoothing parameters, and show how to select λn so as to achieve a desired lFDR. We compare the smoothing parameters chosen to achieve a specified lFDR and those chosen to achieve the oracle properties, as well as their resulting estimates for model coefficients, with both simulation and an example from a genetic study of prostate specific antigen.

  13. Design of scheduling and rate-adaptation algorithms for adaptive HTTP streaming

    NASA Astrophysics Data System (ADS)

    Hesse, Stephan

    2013-09-01

    In adaptive HTTP streaming model, the HTTP server stores multiple representations of media content, encoded at different rates. It is the function of a streaming client to select and retrieve segments of appropriate representations to enable continuous media playback under varying network conditions. In this paper we describe design of a control mechanism enabling such a selection and retrieval of media data during streaming session. We also describe the architecture of a streaming client for adaptive HTTP streaming and provide simulation data illustrating the effectiveness of the proposed control mechanism for handling bandwidth fluctuations typical for TCP traffic.

  14. Delayed feedback during sensorimotor learning selectively disrupts adaptation but not strategy use.

    PubMed

    Brudner, Samuel N; Kethidi, Nikhit; Graeupner, Damaris; Ivry, Richard B; Taylor, Jordan A

    2016-03-01

    In sensorimotor adaptation tasks, feedback delays can cause significant reductions in the rate of learning. This constraint is puzzling given that many skilled behaviors have inherently long delays (e.g., hitting a golf ball). One difference in these task domains is that adaptation is primarily driven by error-based feedback, whereas skilled performance may also rely to a large extent on outcome-based feedback. This difference suggests that error- and outcome-based feedback may engage different learning processes, and these processes may be associated with different temporal constraints. We tested this hypothesis in a visuomotor adaptation task. Error feedback was indicated by the terminal position of a cursor, while outcome feedback was indicated by points. In separate groups of participants, the two feedback signals were presented immediately at the end of the movement, after a delay, or with just the error feedback delayed. Participants learned to counter the rotation in a similar manner regardless of feedback delay. However, the aftereffect, an indicator of implicit motor adaptation, was attenuated with delayed error feedback, consistent with the hypothesis that a different learning process supports performance under delay. We tested this by employing a task that dissociates the contribution of explicit strategies and implicit adaptation. We find that explicit aiming strategies contribute to the majority of the learning curve, regardless of delay; however, implicit learning, measured over the course of learning and by aftereffects, was significantly attenuated with delayed error-based feedback. These experiments offer new insight into the temporal constraints associated with different motor learning processes. PMID:26792878

  15. Towards an Adaptive Multimedia Learning Environment: Enhancing the Student Experience.

    ERIC Educational Resources Information Center

    Kurzel, Frank; Slay, Jill; Rath, Michelle; Chau, Yenha

    This paper describes the development of an adaptive multimedia learning environment that utilizes multimedia presentation techniques in its interface while still providing Internet connectivity for management and delivery purposes. The system supports the WWW as its addressing space but uses the local client areas to store media items expensive in…

  16. Adaptive Learning in Psychology: Wayfinding in the Digital Age

    ERIC Educational Resources Information Center

    Dziuban, Charles D.; Moskal, Patsy D.; Cassisi, Jeffrey; Fawcett, Alexis

    2016-01-01

    This paper presents the results of a pilot study investigating the use of the Realizeit adaptive learning platform to deliver a fully online General Psychology course across two semesters. Through mutual cooperation, UCF and vendor (CCKF) researchers examined students' affective, behavioral, and cognitive reactions to the system. Student survey…

  17. Managing Adaptive Challenges: Learning with Principals in Bermuda and Florida

    ERIC Educational Resources Information Center

    Drago-Severson, Eleanor; Maslin-Ostrowski, Patricia; Hoffman, Alexander M.; Barbaro, Justin

    2014-01-01

    We interviewed eight principals from Bermuda and Florida about how they identify and manage their most pressing challenges. Their challenges are composed of both adaptive and technical work, requiring leaders to learn to diagnose and manage them. Challenges focused on change and were traced to accountability contexts, yet accountability was not…

  18. Adaptive Knowledge Management of Project-Based Learning

    ERIC Educational Resources Information Center

    Tilchin, Oleg; Kittany, Mohamed

    2016-01-01

    The goal of an approach to Adaptive Knowledge Management (AKM) of project-based learning (PBL) is to intensify subject study through guiding, inducing, and facilitating development knowledge, accountability skills, and collaborative skills of students. Knowledge development is attained by knowledge acquisition, knowledge sharing, and knowledge…

  19. Mispronunciation Detection for Language Learning and Speech Recognition Adaptation

    ERIC Educational Resources Information Center

    Ge, Zhenhao

    2013-01-01

    The areas of "mispronunciation detection" (or "accent detection" more specifically) within the speech recognition community are receiving increased attention now. Two application areas, namely language learning and speech recognition adaptation, are largely driving this research interest and are the focal points of this work.…

  20. Learning about colonization when managing metapopulations under an adaptive management framework.

    PubMed

    Southwell, Darren M; Hauser, Cindy E; McCarthy, Michael A

    2016-01-01

    Adaptive management is a framework for resolving key uncertainties while managing complex ecological systems. Its use has been prominent in fisheries research and wildlife harvesting; however, its application to other areas of environmental management remains somewhat limited. Indeed, adaptive management has not been used to guide and inform metapopulation restoration, despite considerable uncertainty surrounding such actions. In this study, we determined how best to learn about the colonization rate when managing metapopulations under an adaptive management framework. We developed a mainland-island metapopulation model based on the threatened bay checkerspot butterfly (Euphydryas editha bayensis) and assessed three management approaches: adding new patches, adding area to existing patches, and doing nothing. Using stochastic dynamic programming, we found the optimal passive and active adaptive management strategies by monitoring colonization of vacant patches. Under a passive adaptive strategy, increasing patch area was best when the expected colonization rate was below a threshold; otherwise, adding new patches was optimal. Under an active adaptive strategy, it was best to add patches only when we were reasonably confident that the colonization rate was high. This research provides a framework for managing mainland-island metapopulations in the face of uncertainty while learning about the dynamics of these complex systems. PMID:27039525

  1. Amygdala-prefrontal interactions in (mal)adaptive learning.

    PubMed

    Likhtik, Ekaterina; Paz, Rony

    2015-03-01

    The study of neurobiological mechanisms underlying anxiety disorders has been shaped by learning models that frame anxiety as maladaptive learning. Pavlovian conditioning and extinction are particularly influential in defining learning stages that can account for symptoms of anxiety disorders. Recently, dynamic and task related communication between the basolateral complex of the amygdala (BLA) and the medial prefrontal cortex (mPFC) has emerged as a crucial aspect of successful evaluation of threat and safety. Ongoing patterns of neural signaling within the mPFC-BLA circuit during encoding, expression and extinction of adaptive learning are reviewed. The mechanisms whereby deficient mPFC-BLA interactions can lead to generalized fear and anxiety are discussed in learned and innate anxiety. Findings with cross-species validity are emphasized.

  2. Adaptation of abbreviated mathematics anxiety rating scale for engineering students

    NASA Astrophysics Data System (ADS)

    Nordin, Sayed Kushairi Sayed; Samat, Khairul Fadzli; Sultan, Al Amin Mohamed; Halim, Bushra Abdul; Ismail, Siti Fatimah; Mafazi, Nurul Wirdah

    2015-05-01

    Mathematics is an essential and fundamental tool used by engineers to analyse and solve problems in their field. Due to this, most engineering education programs involve a concentration of study in mathematics courses whereby engineering students have to take mathematics courses such as numerical methods, differential equations and calculus in the first two years and continue to do so until the completion of the sequence. However, the students struggled and had difficulties in learning courses that require mathematical abilities. Hence, this study presents the factors that caused mathematics anxiety among engineering students using Abbreviated Mathematics Anxiety Rating Scale (AMARS) through 95 students of Universiti Teknikal Malaysia Melaka (UTeM). From 25 items in AMARS, principal component analysis (PCA) suggested that there are four mathematics anxiety factors, namely experiences of learning mathematics, cognitive skills, mathematics evaluation anxiety and students' perception on mathematics. Minitab 16 software was used to analyse the nonparametric statistics. Kruskal-Wallis Test indicated that there is a significant difference in the experience of learning mathematics and mathematics evaluation anxiety among races. The Chi-Square Test of Independence revealed that the experience of learning mathematics, cognitive skills and mathematics evaluation anxiety depend on the results of their SPM additional mathematics. Based on this study, it is recommended to address the anxiety problems among engineering students at the early stage of studying in the university. Thus, lecturers should play their part by ensuring a positive classroom environment which encourages students to study mathematics without fear.

  3. Effects of dopaminergic therapy on locomotor adaptation and adaptive learning in persons with Parkinson's disease.

    PubMed

    Roemmich, Ryan T; Hack, Nawaz; Akbar, Umer; Hass, Chris J

    2014-07-15

    Persons with Parkinson's disease (PD) are characterized by multifactorial gait deficits, though the factors which influence the abilities of persons with PD to adapt and store new gait patterns are unclear. The purpose of this study was to investigate the effects of dopaminergic therapy on the abilities of persons with PD to adapt and store gait parameters during split-belt treadmill (SBT) walking. Ten participants with idiopathic PD who were being treated with stable doses of orally-administered dopaminergic therapy participated. All participants performed two randomized testing sessions on separate days: once while optimally-medicated (ON meds) and once after 12-h withdrawal from dopaminergic medication (OFF meds). During each session, locomotor adaptation was investigated as the participants walked on a SBT for 10 min while the belts moved at a 2:1 speed ratio. We assessed locomotor adaptive learning by quantifying: (1) aftereffects during de-adaptation (once the belts returned to tied speeds immediately following SBT walking) and (2) savings during re-adaptation (as the participants repeated the same SBT walking task after washout of aftereffects following the initial SBT task). The withholding of dopaminergic medication diminished step length aftereffects significantly during de-adaptation. However, both locomotor adaptation and savings were unaffected by levodopa. These findings suggest that dopaminergic pathways influence aftereffect storage but do not influence locomotor adaptation or savings within a single session of SBT walking. It appears important that persons with PD should be optimally-medicated if walking on the SBT as gait rehabilitation.

  4. Approach for Using Learner Satisfaction to Evaluate the Learning Adaptation Policy

    ERIC Educational Resources Information Center

    Jeghal, Adil; Oughdir, Lahcen; Tairi, Hamid; Radouane, Abdelhay

    2016-01-01

    The learning adaptation is a very important phase in a learning situation in human learning environments. This paper presents the authors' approach used to evaluate the effectiveness of learning adaptive systems. This approach is based on the analysis of learner satisfaction notices collected by a questionnaire on a learning situation; to analyze…

  5. Adaptation to Low Temperature Exposure Increases Metabolic Rates Independently of Growth Rates.

    PubMed

    Williams, Caroline M; Szejner-Sigal, Andre; Morgan, Theodore J; Edison, Arthur S; Allison, David B; Hahn, Daniel A

    2016-07-01

    Metabolic cold adaptation is a pattern where ectotherms from cold, high-latitude, or -altitude habitats have higher metabolic rates than ectotherms from warmer habitats. When found, metabolic cold adaptation is often attributed to countergradient selection, wherein short, cool growing seasons select for a compensatory increase in growth rates and development times of ectotherms. Yet, ectotherms in high-latitude and -altitude environments face many challenges in addition to thermal and time constraints on lifecycles. In addition to short, cool growing seasons, high-latitude and - altitude environments are characterized by regular exposure to extreme low temperatures, which cause ectotherms to enter a transient state of immobility termed chill coma. The ability to resume activity quickly after chill coma increases with latitude and altitude in patterns consistent with local adaptation to cold conditions. We show that artificial selection for fast and slow chill coma recovery among lines of the fly Drosophila melanogaster also affects rates of respiratory metabolism. Cold-hardy fly lines, with fast recovery from chill coma, had higher respiratory metabolic rates than control lines, with cold-susceptible slow-recovering lines having the lowest metabolic rates. Fast chill coma recovery was also associated with higher respiratory metabolism in a set of lines derived from a natural population. Although their metabolic rates were higher than control lines, fast-recovering cold-hardy lines did not have faster growth rates or development times than control lines. This suggests that raised metabolic rates in high-latitude and -altitude species may be driven by adaptation to extreme low temperatures, illustrating the importance of moving "Beyond the Mean". PMID:27103615

  6. Adaptive beat-to-beat heart rate estimation in ballistocardiograms.

    PubMed

    Brüser, Christoph; Stadlthanner, Kurt; de Waele, Stijn; Leonhardt, Steffen

    2011-09-01

    A ballistocardiograph records the mechanical activity of the heart. We present a novel algorithm for the detection of individual heart beats and beat-to-beat interval lengths in ballistocardiograms (BCGs) from healthy subjects. An automatic training step based on unsupervised learning techniques is used to extract the shape of a single heart beat from the BCG. Using the learned parameters, the occurrence of individual heart beats in the signal is detected. A final refinement step improves the accuracy of the estimated beat-to-beat interval lengths. Compared to many existing algorithms, the new approach offers heart rate estimates on a beat-to-beat basis. The agreement of the proposed algorithm with an ECG reference has been evaluated. A relative beat-to-beat interval error of 1.79% with a coverage of 95.94% was achieved on recordings from 16 subjects.

  7. Distributed adaptive simulation through standards-based integration of simulators and adaptive learning systems.

    PubMed

    Bergeron, Bryan; Cline, Andrew; Shipley, Jaime

    2012-01-01

    We have developed a distributed, standards-based architecture that enables simulation and simulator designers to leverage adaptive learning systems. Our approach, which incorporates an electronic competency record, open source LMS, and open source microcontroller hardware, is a low-cost, pragmatic option to integrating simulators with traditional courseware. PMID:22356955

  8. Introduction to project ALIAS: adaptive-learning image analysis system

    NASA Astrophysics Data System (ADS)

    Bock, Peter

    1992-03-01

    As an alternative to preprogrammed rule-based artificial intelligence, collective learning systems theory postulates a hierarchical network of cellular automata which acquire their knowledge through learning based on a series of trial-and-error interactions with an evaluating environment, much as humans do. The input to the hierarchical network is provided by a set of sensors which perceive the external world. Using both this perceived information and past experience (memory), the learning automata synthesize collections of trial responses, periodically modifying their memories based on internal evaluations or external evaluations from the environment. Based on collective learning systems theory, an adaptive transputer- based image-processing engine comprising a three-layer hierarchical network of 32 learning cells and 33 nonlearning cells has been applied to a difficult image processing task: the scale, phase, and translation-invariant detection of anomalous features in otherwise `normal' images. Known as adaptive learning image analysis system (ALIAS), this parallel-processing engine has been constructed and tested at the Research institute for Applied Knowledge Processing (FAW) in Ulm, Germany under the sponsorship of Robert Bosch GmbH. Results demonstrate excellent detection, discrimination, and localization of anomalies in binary images. Recent enhancements include the ability to process gray-scale images and the automatic supervised segmentation and classification of images. Current research is directed toward the processing of time-series data and the hierarchical extension of ALIAS from the sub-symbolic level to the higher levels of symbolic association.

  9. Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

    ERIC Educational Resources Information Center

    Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

    2008-01-01

    Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

  10. Peers as Resources for Learning: A Situated Learning Approach to Adapted Physical Activity in Rehabilitation

    ERIC Educational Resources Information Center

    Standal, Oyvind F.; Jespersen, Ejgil

    2008-01-01

    The purpose of this study was to investigate the learning that takes place when people with disabilities interact in a rehabilitation context. Data were generated through in-depth interviews and close observations in a 2 one-half week-long rehabilitation program, where the participants learned both wheelchair skills and adapted physical…

  11. Adaptive Web-Assisted Learning System for Students with Specific Learning Disabilities: A Needs Analysis Study

    ERIC Educational Resources Information Center

    Polat, Elif; Adiguzel, Tufan; Akgun, Ozcan Erkan

    2012-01-01

    Because there is, currently, no education system for primary school students in grades 1-3 who have specific learning disabilities in Turkey and because such students do not receive sufficient support from face-to-face counseling, a needs analysis was conducted in order to prepare an adaptive, web-assisted learning system according to variables…

  12. Building Adaptive Game-Based Learning Resources: The Integration of IMS Learning Design and

    ERIC Educational Resources Information Center

    Burgos, Daniel; Moreno-Ger, Pablo; Sierra, Jose Luis; Fernandez-Manjon, Baltasar; Specht, Marcus; Koper, Rob

    2008-01-01

    IMS Learning Design (IMS-LD) is a specification to create units of learning (UoLs), which express a certain pedagogical model or strategy (e.g., adaptive learning with games). However, the authoring process of a UoL remains difficult because of the lack of high-level authoring tools for IMS-LD, even more so when the focus is on specific topics,…

  13. Learning to speciate: The biased learning of mate preferences promotes adaptive radiation

    PubMed Central

    Gilman, R. Tucker; Kozak, Genevieve M.

    2015-01-01

    Bursts of rapid repeated speciation called adaptive radiations have generated much of Earth's biodiversity and fascinated biologists since Darwin, but we still do not know why some lineages radiate and others do not. Understanding what causes assortative mating to evolve rapidly and repeatedly in the same lineage is key to understanding adaptive radiation. Many species that have undergone adaptive radiations exhibit mate preference learning, where individuals acquire mate preferences by observing the phenotypes of other members of their populations. Mate preference learning can be biased if individuals also learn phenotypes to avoid in mates, and shift their preferences away from these avoided phenotypes. We used individual‐based computational simulations to study whether biased and unbiased mate preference learning promotes ecological speciation and adaptive radiation. We found that ecological speciation can be rapid and repeated when mate preferences are biased, but is inhibited when mate preferences are learned without bias. Our results suggest that biased mate preference learning may play an important role in generating animal biodiversity through adaptive radiation. PMID:26459795

  14. Learning to speciate: The biased learning of mate preferences promotes adaptive radiation.

    PubMed

    Gilman, R Tucker; Kozak, Genevieve M

    2015-11-01

    Bursts of rapid repeated speciation called adaptive radiations have generated much of Earth's biodiversity and fascinated biologists since Darwin, but we still do not know why some lineages radiate and others do not. Understanding what causes assortative mating to evolve rapidly and repeatedly in the same lineage is key to understanding adaptive radiation. Many species that have undergone adaptive radiations exhibit mate preference learning, where individuals acquire mate preferences by observing the phenotypes of other members of their populations. Mate preference learning can be biased if individuals also learn phenotypes to avoid in mates, and shift their preferences away from these avoided phenotypes. We used individual-based computational simulations to study whether biased and unbiased mate preference learning promotes ecological speciation and adaptive radiation. We found that ecological speciation can be rapid and repeated when mate preferences are biased, but is inhibited when mate preferences are learned without bias. Our results suggest that biased mate preference learning may play an important role in generating animal biodiversity through adaptive radiation.

  15. Adaptive distance metric learning for diffusion tensor image segmentation.

    PubMed

    Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C N; Chu, Winnie C W

    2014-01-01

    High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework.

  16. Adaptive Distance Metric Learning for Diffusion Tensor Image Segmentation

    PubMed Central

    Kong, Youyong; Wang, Defeng; Shi, Lin; Hui, Steve C. N.; Chu, Winnie C. W.

    2014-01-01

    High quality segmentation of diffusion tensor images (DTI) is of key interest in biomedical research and clinical application. In previous studies, most efforts have been made to construct predefined metrics for different DTI segmentation tasks. These methods require adequate prior knowledge and tuning parameters. To overcome these disadvantages, we proposed to automatically learn an adaptive distance metric by a graph based semi-supervised learning model for DTI segmentation. An original discriminative distance vector was first formulated by combining both geometry and orientation distances derived from diffusion tensors. The kernel metric over the original distance and labels of all voxels were then simultaneously optimized in a graph based semi-supervised learning approach. Finally, the optimization task was efficiently solved with an iterative gradient descent method to achieve the optimal solution. With our approach, an adaptive distance metric could be available for each specific segmentation task. Experiments on synthetic and real brain DTI datasets were performed to demonstrate the effectiveness and robustness of the proposed distance metric learning approach. The performance of our approach was compared with three classical metrics in the graph based semi-supervised learning framework. PMID:24651858

  17. Learners' Perceptions and Illusions of Adaptivity in Computer-Based Learning Environments

    ERIC Educational Resources Information Center

    Vandewaetere, Mieke; Vandercruysse, Sylke; Clarebout, Geraldine

    2012-01-01

    Research on computer-based adaptive learning environments has shown exemplary growth. Although the mechanisms of effective adaptive instruction are unraveled systematically, little is known about the relative effect of learners' perceptions of adaptivity in adaptive learning environments. As previous research has demonstrated that the learners'…

  18. Learning from adaptive neural dynamic surface control of strict-feedback systems.

    PubMed

    Wang, Min; Wang, Cong

    2015-06-01

    Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

  19. Adaptive-Rate Compressive Sensing Using Side Information.

    PubMed

    Warnell, Garrett; Bhattacharya, Sourabh; Chellappa, Rama; Başar, Tamer

    2015-11-01

    We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information. The first method uses extra cross-validation measurements, and the second one exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients that comprises the images in the video sequence. Instead, we use the side information to predict the number of significant coefficients in the signal at the next time instant. We develop our techniques in the specific context of background subtraction using a spatially multiplexing CS camera such as the single-pixel camera. For each image in the video sequence, the proposed techniques specify a fixed number of CS measurements to acquire and adjust this quantity from image to image. We experimentally validate the proposed methods on real surveillance video sequences.

  20. Adaptivity in Game-Based Learning: A New Perspective on Story

    NASA Astrophysics Data System (ADS)

    Berger, Florian; Müller, Wolfgang

    Game-based learning as a novel form of e-learning still has issues in fundamental questions, the lack of a general model for adaptivity being one of them. Since adaptive techniques in traditional e-learning applications bear close similarity to certain interactive storytelling approaches, we propose a new notion of story as the joining element of arbitraty learning paths.

  1. Designing a Semantic Bliki System to Support Different Types of Knowledge and Adaptive Learning

    ERIC Educational Resources Information Center

    Huang, Shiu-Li; Yang, Chia-Wei

    2009-01-01

    Though blogs and wikis have been used to support knowledge management and e-learning, existing blogs and wikis cannot support different types of knowledge and adaptive learning. A case in point, types of knowledge vary greatly in category and viewpoints. Additionally, adaptive learning is crucial to improving one's learning performance. This study…

  2. Adaptation Criteria for the Personalised Delivery of Learning Materials: A Multi-Stage Empirical Investigation

    ERIC Educational Resources Information Center

    Thalmann, Stefan

    2014-01-01

    Personalised e-Learning represents a major step-change from the one-size-fits-all approach of traditional learning platforms to a more customised and interactive provision of learning materials. Adaptive learning can support the learning process by tailoring learning materials to individual needs. However, this requires the initial preparation of…

  3. Different visuomotor processes maturation rates in children support dual visuomotor learning systems.

    PubMed

    Gómez-Moya, Rosinna; Díaz, Rosalinda; Fernandez-Ruiz, Juan

    2016-04-01

    Different processes are involved during visuomotor learning, including an error-based procedural and a strategy based cognitive mechanism. Our objective was to analyze if the changes in the adaptation or the aftereffect components of visuomotor learning measured across development, reflected different maturation rates of the aforementioned mechanisms. Ninety-five healthy children aged 4-12years and a group of young adults participated in a wedge prism and a dove prism throwing task, which laterally displace or horizontally reverse the visual field respectively. The results show that despite the age-related differences in motor control, all children groups adapted in the error-based wedge prisms condition. However, when removing the prism, small children showed a slower aftereffects extinction rate. On the strategy-based visual reversing task only the older children group reached adult-like levels. These results are consistent with the idea of different mechanisms with asynchronous maturation rates participating during visuomotor learning. PMID:26802974

  4. Different visuomotor processes maturation rates in children support dual visuomotor learning systems.

    PubMed

    Gómez-Moya, Rosinna; Díaz, Rosalinda; Fernandez-Ruiz, Juan

    2016-04-01

    Different processes are involved during visuomotor learning, including an error-based procedural and a strategy based cognitive mechanism. Our objective was to analyze if the changes in the adaptation or the aftereffect components of visuomotor learning measured across development, reflected different maturation rates of the aforementioned mechanisms. Ninety-five healthy children aged 4-12years and a group of young adults participated in a wedge prism and a dove prism throwing task, which laterally displace or horizontally reverse the visual field respectively. The results show that despite the age-related differences in motor control, all children groups adapted in the error-based wedge prisms condition. However, when removing the prism, small children showed a slower aftereffects extinction rate. On the strategy-based visual reversing task only the older children group reached adult-like levels. These results are consistent with the idea of different mechanisms with asynchronous maturation rates participating during visuomotor learning.

  5. Accessibility and Adaptability of Learning Objects: Responding to Metadata, Learning Patterns and Profiles of Needs and Preferences

    ERIC Educational Resources Information Center

    Green, Steve; Jones, Ray; Pearson, Elaine; Gkatzidou, Stavroula

    2006-01-01

    The case for learning patterns as a design method for accessible and adaptable learning objects is explored. Patterns and templates for the design of learning objects can be derived from successful existing learning resources. These patterns can then be reused in the design of new learning objects. We argue that by attending to criteria for reuse…

  6. An adaptive online learning framework for practical breast cancer diagnosis

    NASA Astrophysics Data System (ADS)

    Chu, Tianshu; Wang, Jie; Chen, Jiayu

    2016-03-01

    This paper presents an adaptive online learning (OL) framework for supporting clinical breast cancer (BC) diagnosis. Unlike traditional data mining, which trains a particular model from a fixed set of medical data, our framework offers robust OL models that can be updated adaptively according to new data sequences and newly discovered features. As a result, our framework can naturally learn to perform BC diagnosis using experts' opinions on sequential patient cases with cumulative clinical measurements. The framework integrates both supervised learning (SL) models for BC risk assessment and reinforcement learning (RL) models for decision-making of clinical measurements. In other words, online SL and RL interact with one another, and under a doctor's supervision, push the patient's diagnosis further. Furthermore, our framework can quickly update relevant model parameters based on current diagnosis information during the training process. Additionally, it can build flexible fitted models by integrating different model structures and plugging in the corresponding parameters during the prediction (or decision-making) process. Even when the feature space is extended, it can initialize the corresponding parameters and extend the existing model structure without loss of the cumulative knowledge. We evaluate the OL framework on real datasets from BCSC and WBC, and demonstrate that our SL models achieve accurate BC risk assessment from sequential data and incremental features. We also verify that the well-trained RL models provide promising measurement suggestions.

  7. Performance & Emotion--A Study on Adaptive E-Learning Based on Visual/Verbal Learning Styles

    ERIC Educational Resources Information Center

    Beckmann, Jennifer; Bertel, Sven; Zander, Steffi

    2015-01-01

    Adaptive e-Learning systems are able to adjust to a user's learning needs, usually by user modeling or tracking progress. Such learner-adaptive behavior has rapidly become a hot topic for e-Learning, furthered in part by the recent rapid increase in the use of MOOCs (Massive Open Online Courses). A lack of general, individual, and situational data…

  8. Development of an Adaptive Learning System with Multiple Perspectives based on Students' Learning Styles and Cognitive Styles

    ERIC Educational Resources Information Center

    Yang, Tzu-Chi; Hwang, Gwo-Jen; Yang, Stephen Jen-Hwa

    2013-01-01

    In this study, an adaptive learning system is developed by taking multiple dimensions of personalized features into account. A personalized presentation module is proposed for developing adaptive learning systems based on the field dependent/independent cognitive style model and the eight dimensions of Felder-Silverman's learning style. An…

  9. Online Adaptation and Over-Trial Learning in Macaque Visuomotor Control

    PubMed Central

    Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten

    2011-01-01

    When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526

  10. Classifying work rate from heart rate measurements using an adaptive neuro-fuzzy inference system.

    PubMed

    Kolus, Ahmet; Imbeau, Daniel; Dubé, Philippe-Antoine; Dubeau, Denise

    2016-05-01

    In a new approach based on adaptive neuro-fuzzy inference systems (ANFIS), field heart rate (HR) measurements were used to classify work rate into four categories: very light, light, moderate, and heavy. Inter-participant variability (physiological and physical differences) was considered. Twenty-eight participants performed Meyer and Flenghi's step-test and a maximal treadmill test, during which heart rate and oxygen consumption (VO2) were measured. Results indicated that heart rate monitoring (HR, HRmax, and HRrest) and body weight are significant variables for classifying work rate. The ANFIS classifier showed superior sensitivity, specificity, and accuracy compared to current practice using established work rate categories based on percent heart rate reserve (%HRR). The ANFIS classifier showed an overall 29.6% difference in classification accuracy and a good balance between sensitivity (90.7%) and specificity (95.2%) on average. With its ease of implementation and variable measurement, the ANFIS classifier shows potential for widespread use by practitioners for work rate assessment. PMID:26851475

  11. Adaptive WTA with an analog VLSI neuromorphic learning chip.

    PubMed

    Häfliger, Philipp

    2007-03-01

    In this paper, we demonstrate how a particular spike-based learning rule (where exact temporal relations between input and output spikes of a spiking model neuron determine the changes of the synaptic weights) can be tuned to express rate-based classical Hebbian learning behavior (where the average input and output spike rates are sufficient to describe the synaptic changes). This shift in behavior is controlled by the input statistic and by a single time constant. The learning rule has been implemented in a neuromorphic very large scale integration (VLSI) chip as part of a neurally inspired spike signal image processing system. The latter is the result of the European Union research project Convolution AER Vision Architecture for Real-Time (CAVIAR). Since it is implemented as a spike-based learning rule (which is most convenient in the overall spike-based system), even if it is tuned to show rate behavior, no explicit long-term average signals are computed on the chip. We show the rule's rate-based Hebbian learning ability in a classification task in both simulation and chip experiment, first with artificial stimuli and then with sensor input from the CAVIAR system. PMID:17385639

  12. An adaptive learning control system for large flexible structures

    NASA Technical Reports Server (NTRS)

    Thau, F. E.

    1985-01-01

    The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.

  13. Adapted to explore: reinforcement learning in Autistic Spectrum Conditions.

    PubMed

    Yechiam, Eldad; Arshavsky, Olga; Shamay-Tsoory, Simone G; Yaniv, Shoshana; Aharon, Judith

    2010-03-01

    Recent studies have recorded a tendency of individuals with Autism Spectrum Conditions (ASC) to continually change their choices in repeated choice tasks. In the current study we examine if this finding implies that ASC individuals have a cognitive style that facilitates exploration and discovery. Six decision tasks were administered to adolescents with ASC and matched controls. Significant differences in shifting between choice options appeared in the Iowa Gambling task (Bechara, Damasio, Damasio, & Anderson, 1994). A formal cognitive modeling analysis demonstrated that for about half of the ASC participants the adaptation process did not conform to the standard reinforcement learning model. These individuals were only coarsely affected by choice-outcomes, and were more influenced by the exploratory value of choices, being attracted to previously un-explored alternatives. An examination of the five simpler decision tasks where the advantageous option was easier to determine showed no evidence of this pattern, suggesting that the shifting choice pattern is not an uncontrollable tendency independent of task outcomes. These findings suggest that ASC individuals have a unique adaptive learning style, which may be beneficial is some learning environment but maladaptive in others, particularly in social contexts. PMID:19913345

  14. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    PubMed

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification.

  15. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    PubMed

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. PMID:26055435

  16. Establishing a Dynamic Self-Adaptation Learning Algorithm of the BP Neural Network and Its Applications

    NASA Astrophysics Data System (ADS)

    Li, Xiaofeng; Xiang, Suying; Zhu, Pengfei; Wu, Min

    2015-12-01

    In order to avoid the inherent deficiencies of the traditional BP neural network, such as slow convergence speed, that easily leading to local minima, poor generalization ability and difficulty in determining the network structure, the dynamic self-adaptive learning algorithm of the BP neural network is put forward to improve the function of the BP neural network. The new algorithm combines the merit of principal component analysis, particle swarm optimization, correlation analysis and self-adaptive model, hence can effectively solve the problems of selecting structural parameters, initial connection weights and thresholds and learning rates of the BP neural network. This new algorithm not only reduces the human intervention, optimizes the topological structures of BP neural networks and improves the network generalization ability, but also accelerates the convergence speed of a network, avoids trapping into local minima, and enhances network adaptation ability and prediction ability. The dynamic self-adaptive learning algorithm of the BP neural network is used to forecast the total retail sale of consumer goods of Sichuan Province, China. Empirical results indicate that the new algorithm is superior to the traditional BP network algorithm in predicting accuracy and time consumption, which shows the feasibility and effectiveness of the new algorithm.

  17. Situated learning theory: adding rate and complexity effects via Kauffman's NK model.

    PubMed

    Yuan, Yu; McKelvey, Bill

    2004-01-01

    For many firms, producing information, knowledge, and enhancing learning capability have become the primary basis of competitive advantage. A review of organizational learning theory identifies two approaches: (1) those that treat symbolic information processing as fundamental to learning, and (2) those that view the situated nature of cognition as fundamental. After noting that the former is inadequate because it focuses primarily on behavioral and cognitive aspects of individual learning, this paper argues the importance of studying learning as interactions among people in the context of their environment. It contributes to organizational learning in three ways. First, it argues that situated learning theory is to be preferred over traditional behavioral and cognitive learning theories, because it treats organizations as complex adaptive systems rather than mere information processors. Second, it adds rate and nonlinear learning effects. Third, following model-centered epistemology, it uses an agent-based computational model, in particular a "humanized" version of Kauffman's NK model, to study the situated nature of learning. Using simulation results, we test eight hypotheses extending situated learning theory in new directions. The paper ends with a discussion of possible extensions of the current study to better address key issues in situated learning.

  18. Reducing Dropout Rates through Expanded Learning Opportunities. Issue Brief

    ERIC Educational Resources Information Center

    Harris, Laura; Princiotta, Daniel

    2009-01-01

    Expanded learning opportunities (ELOs), which include afterschool, summer learning, and extended day and extended year programs, can help states reduce dropout rates and increase graduation rates. Effective elementary, middle, and high school ELOs support academic rigor, boost student engagement, and provide students with supportive relationships.…

  19. Fault-tolerant nonlinear adaptive flight control using sliding mode online learning.

    PubMed

    Krüger, Thomas; Schnetter, Philipp; Placzek, Robin; Vörsmann, Peter

    2012-08-01

    An expanded nonlinear model inversion flight control strategy using sliding mode online learning for neural networks is presented. The proposed control strategy is implemented for a small unmanned aircraft system (UAS). This class of aircraft is very susceptible towards nonlinearities like atmospheric turbulence, model uncertainties and of course system failures. Therefore, these systems mark a sensible testbed to evaluate fault-tolerant, adaptive flight control strategies. Within this work the concept of feedback linearization is combined with feed forward neural networks to compensate for inversion errors and other nonlinear effects. Backpropagation-based adaption laws of the network weights are used for online training. Within these adaption laws the standard gradient descent backpropagation algorithm is augmented with the concept of sliding mode control (SMC). Implemented as a learning algorithm, this nonlinear control strategy treats the neural network as a controlled system and allows a stable, dynamic calculation of the learning rates. While considering the system's stability, this robust online learning method therefore offers a higher speed of convergence, especially in the presence of external disturbances. The SMC-based flight controller is tested and compared with the standard gradient descent backpropagation algorithm in the presence of system failures.

  20. Discrepancies in Parent and Teacher Ratings of Adaptive Behavior of Children with Multiple Disabilities.

    ERIC Educational Resources Information Center

    Voelker, Sylvia; And Others

    1997-01-01

    Parent and teacher ratings of the adaptive skills of 59 children (mean age 6 years) with multiple disabilities in a rehabilitation day treatment setting were compared using the Vineland Adaptive Behavior Scales. Teachers systematically rated the children as more skilled in both global and specific domains of adaptive behavior than did the parents.…

  1. Automatic learning rate adjustment for self-supervising autonomous robot control

    NASA Technical Reports Server (NTRS)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

  2. Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

    PubMed Central

    Xu, Yunfei; Choi, Jongeun

    2011-01-01

    This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme. PMID:22163785

  3. Improving Voluntary Environmental Management Programs: Facilitating Learning and Adaptation

    NASA Astrophysics Data System (ADS)

    Genskow, Kenneth D.; Wood, Danielle M.

    2011-05-01

    Environmental planners and managers face unique challenges understanding and documenting the effectiveness of programs that rely on voluntary actions by private landowners. Programs, such as those aimed at reducing nonpoint source pollution or improving habitat, intend to reach those goals by persuading landowners to adopt behaviors and management practices consistent with environmental restoration and protection. Our purpose with this paper is to identify barriers for improving voluntary environmental management programs and ways to overcome them. We first draw upon insights regarding data, learning, and adaptation from the adaptive management and performance management literatures, describing three key issues: overcoming information constraints, structural limitations, and organizational culture. Although these lessons are applicable to a variety of voluntary environmental management programs, we then present the issues in the context of on-going research for nonpoint source water quality pollution. We end the discussion by highlighting important elements for advancing voluntary program efforts.

  4. Towards Motivation-Based Adaptation of Difficulty in E-Learning Programs

    ERIC Educational Resources Information Center

    Endler, Anke; Rey, Gunter Daniel; Butz, Martin V.

    2012-01-01

    The objective of this study was to investigate if an e-learning environment may use measurements of the user's current motivation to adapt the level of task difficulty for more effective learning. In the reported study, motivation-based adaptation was applied randomly to collect a wide range of data for different adaptations in a variety of…

  5. The Emotions of Socialization-Related Learning: Understanding Workplace Adaptation as a Learning Process.

    ERIC Educational Resources Information Center

    Reio, Thomas G., Jr.

    The influence of selected discrete emotions on socialization-related learning and perception of workplace adaptation was examined in an exploratory study. Data were collected from 233 service workers in 4 small and medium-sized companies in metropolitan Washington, D.C. The sample members' average age was 32.5 years, and the sample's racial makeup…

  6. The Study and Design of Adaptive Learning System Based on Fuzzy Set Theory

    NASA Astrophysics Data System (ADS)

    Jia, Bing; Zhong, Shaochun; Zheng, Tianyang; Liu, Zhiyong

    Adaptive learning is an effective way to improve the learning outcomes, that is, the selection of learning content and presentation should be adapted to each learner's learning context, learning levels and learning ability. Adaptive Learning System (ALS) can provide effective support for adaptive learning. This paper proposes a new ALS based on fuzzy set theory. It can effectively estimate the learner's knowledge level by test according to learner's target. Then take the factors of learner's cognitive ability and preference into consideration to achieve self-organization and push plan of knowledge. This paper focuses on the design and implementation of domain model and user model in ALS. Experiments confirmed that the system providing adaptive content can effectively help learners to memory the content and improve their comprehension.

  7. The Influence of Student Characteristics on the Use of Adaptive E-Learning Material

    ERIC Educational Resources Information Center

    van Seters, J. R.; Ossevoort, M. A.; Tramper, J.; Goedhart, M. J.

    2012-01-01

    Adaptive e-learning materials can help teachers to educate heterogeneous student groups. This study provides empirical data about the way academic students differ in their learning when using adaptive e-learning materials. Ninety-four students participated in the study. We determined characteristics in a heterogeneous student group by collecting…

  8. Workplace Learning: What's the Rate of Return?

    ERIC Educational Resources Information Center

    Hase, Stewart; Davis, Lester

    A 3-year research program on workplace learning was based on action research and involved the gradual implementation of "Work Activity Briefings" every 2 weeks or whenever a major task was to be undertaken at selected construction sites managed by an Australian mining and construction company. The briefings were regular meetings between all those…

  9. Adapting to rates versus amounts of climate change: a case of adaptation to sea-level rise

    NASA Astrophysics Data System (ADS)

    Shayegh, Soheil; Moreno-Cruz, Juan; Caldeira, Ken

    2016-10-01

    Adaptation is the process of adjusting to climate change in order to moderate harm or exploit beneficial opportunities associated with it. Most adaptation strategies are designed to adjust to a new climate state. However, despite our best efforts to curtail greenhouse gas emissions, climate is likely to continue changing far into the future. Here, we show how considering rates of change affects the projected optimal adaptation strategy. We ground our discussion with an example of optimal investment in the face of continued sea-level rise, presenting a quantitative model that illustrates the interplay among physical and economic factors governing coastal development decisions such as rate of sea-level rise, land slope, discount rate, and depreciation rate. This model shows that the determination of optimal investment strategies depends on taking into account future rates of sea-level rise, as well as social and political constraints. This general approach also applies to the development of improved strategies to adapt to ongoing trends in temperature, precipitation, and other climate variables. Adaptation to some amount of change instead of adaptation to ongoing rates of change may produce inaccurate estimates of damages to the social systems and their ability to respond to external pressures.

  10. Learning Adaptive Forecasting Models from Irregularly Sampled Multivariate Clinical Data

    PubMed Central

    Liu, Zitao; Hauskrecht, Milos

    2016-01-01

    Building accurate predictive models of clinical multivariate time series is crucial for understanding of the patient condition, the dynamics of a disease, and clinical decision making. A challenging aspect of this process is that the model should be flexible and adaptive to reflect well patient-specific temporal behaviors and this also in the case when the available patient-specific data are sparse and short span. To address this problem we propose and develop an adaptive two-stage forecasting approach for modeling multivariate, irregularly sampled clinical time series of varying lengths. The proposed model (1) learns the population trend from a collection of time series for past patients; (2) captures individual-specific short-term multivariate variability; and (3) adapts by automatically adjusting its predictions based on new observations. The proposed forecasting model is evaluated on a real-world clinical time series dataset. The results demonstrate the benefits of our approach on the prediction tasks for multivariate, irregularly sampled clinical time series, and show that it can outperform both the population based and patient-specific time series prediction models in terms of prediction accuracy. PMID:27525189

  11. Peers as resources for learning: a situated learning approach to adapted physical activity in rehabilitation.

    PubMed

    Standal, Øyvind F; Jespersen, Ejgil

    2008-07-01

    The purpose of this study was to investigate the learning that takes place when people with disabilities interact in a rehabilitation context. Data were generated through in-depth interviews and close observations in a 2 (1/2) week-long rehabilitation program, where the participants learned both wheelchair skills and adapted physical activities. The findings from the qualitative data analysis are discussed in the context of situated learning (Lave & Wenger, 1991; Wenger, 1998). The results indicate that peer learning extends beyond skills and techniques, to include ways for the participants to make sense of their situations as wheelchair users. Also, it was found that the community of practice established between the participants represented a critical corrective to instructions provided by rehabilitation professionals.

  12. Theoretical and Applied Implications of Precisely Measuring Learning Rates

    ERIC Educational Resources Information Center

    Skinner, Christopher H.

    2008-01-01

    Nist and Joseph (2008) have confirmed earlier research showing that adding and interspersing a large number of time-consuming learning trials targeting known items (e.g., incremental rehearsal (IR) or interspersal) retards student learning rates. In addition, their current study has confirmed earlier research that adding and interspersing known…

  13. Adaptive Resonance Theory: how a brain learns to consciously attend, learn, and recognize a changing world.

    PubMed

    Grossberg, Stephen

    2013-01-01

    Adaptive Resonance Theory, or ART, is a cognitive and neural theory of how the brain autonomously learns to categorize, recognize, and predict objects and events in a changing world. This article reviews classical and recent developments of ART, and provides a synthesis of concepts, principles, mechanisms, architectures, and the interdisciplinary data bases that they have helped to explain and predict. The review illustrates that ART is currently the most highly developed cognitive and neural theory available, with the broadest explanatory and predictive range. Central to ART's predictive power is its ability to carry out fast, incremental, and stable unsupervised and supervised learning in response to a changing world. ART specifies mechanistic links between processes of consciousness, learning, expectation, attention, resonance, and synchrony during both unsupervised and supervised learning. ART provides functional and mechanistic explanations of such diverse topics as laminar cortical circuitry; invariant object and scenic gist learning and recognition; prototype, surface, and boundary attention; gamma and beta oscillations; learning of entorhinal grid cells and hippocampal place cells; computation of homologous spatial and temporal mechanisms in the entorhinal-hippocampal system; vigilance breakdowns during autism and medial temporal amnesia; cognitive-emotional interactions that focus attention on valued objects in an adaptively timed way; item-order-rank working memories and learned list chunks for the planning and control of sequences of linguistic, spatial, and motor information; conscious speech percepts that are influenced by future context; auditory streaming in noise during source segregation; and speaker normalization. Brain regions that are functionally described include visual and auditory neocortex; specific and nonspecific thalamic nuclei; inferotemporal, parietal, prefrontal, entorhinal, hippocampal, parahippocampal, perirhinal, and motor cortices

  14. When learning order affects sensitivity to base rates: challenges for theories of causal learning.

    PubMed

    Reips, Ulf-Dietrich; Waldmann, Michael R

    2008-01-01

    In three experiments we investigated whether two procedures of acquiring knowledge about the same causal structure, predictive learning (from causes to effects) versus diagnostic learning (from effects to causes), would lead to different base-rate use in diagnostic judgments. Results showed that learners are capable of incorporating base-rate information in their judgments regardless of the direction in which the causal structure is learned. However, this only holds true for relatively simple scenarios. When complexity was increased, base rates were only used after diagnostic learning, but were largely neglected after predictive learning. It could be shown that this asymmetry is not due to a failure of encoding base rates in predictive learning because participants in all conditions were fairly good at reporting them. The findings present challenges for all theories of causal learning.

  15. Modeling and Simulation of An Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

    ERIC Educational Resources Information Center

    Al-Hmouz, A.; Shen, Jun; Al-Hmouz, R.; Yan, Jun

    2012-01-01

    With recent advances in mobile learning (m-learning), it is becoming possible for learning activities to occur everywhere. The learner model presented in our earlier work was partitioned into smaller elements in the form of learner profiles, which collectively represent the entire learning process. This paper presents an Adaptive Neuro-Fuzzy…

  16. Breast image feature learning with adaptive deconvolutional networks

    NASA Astrophysics Data System (ADS)

    Jamieson, Andrew R.; Drukker, Karen; Giger, Maryellen L.

    2012-03-01

    Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpiñán, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e.g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).

  17. Schema-based learning of adaptable and flexible prey- catching in anurans II. Learning after lesioning.

    PubMed

    Corbacho, Fernando; Nishikawa, Kiisa C; Weerasuriya, Ananda; Liaw, Jim-Shih; Arbib, Michael A

    2005-12-01

    The previous companion paper describes the initial (seed) schema architecture that gives rise to the observed prey-catching behavior. In this second paper in the series we describe the fundamental adaptive processes required during learning after lesioning. Following bilateral transections of the hypoglossal nerve, anurans lunge toward mealworms with no accompanying tongue or jaw movement. Nevertheless anurans with permanent hypoglossal transections eventually learn to catch their prey by first learning to open their mouth again and then lunging their body further and increasing their head angle. In this paper we present a new learning framework, called schema-based learning (SBL). SBL emphasizes the importance of the current existent structure (schemas), that defines a functioning system, for the incremental and autonomous construction of ever more complex structure to achieve ever more complex levels of functioning. We may rephrase this statement into the language of Schema Theory (Arbib 1992, for a comprehensive review) as the learning of new schemas based on the stock of current schemas. SBL emphasizes a fundamental principle of organization called coherence maximization, that deals with the maximization of congruence between the results of an interaction (external or internal) and the expectations generated for that interaction. A central hypothesis consists of the existence of a hierarchy of predictive internal models (predictive schemas) all over the control center-brain-of the agent. Hence, we will include predictive models in the perceptual, sensorimotor, and motor components of the autonomous agent architecture. We will then show that predictive models are fundamental for structural learning. In particular we will show how a system can learn a new structural component (augment the overall network topology) after being lesioned in order to recover (or even improve) its original functionality. Learning after lesioning is a special case of structural

  18. Multiple Maximum Exposure Rates in Computerized Adaptive Testing

    ERIC Educational Resources Information Center

    Ramon Barrada, Juan; Veldkamp, Bernard P.; Olea, Julio

    2009-01-01

    Computerized adaptive testing is subject to security problems, as the item bank content remains operative over long periods and administration time is flexible for examinees. Spreading the content of a part of the item bank could lead to an overestimation of the examinees' trait level. The most common way of reducing this risk is to impose a…

  19. Controlling Item Exposure Rates in a Realistic Adaptive Testing Paradigm.

    ERIC Educational Resources Information Center

    Stocking, Martha L.

    In the context of paper and pencil testing, the frequency of the exposure of items is usually controlled through policies that regulate both the reuse of test forms and the frequency with which a candidate may retake the test. In the context of computerized adaptive testing, where item pools are large and expensive to produce and testing can be on…

  20. The Classroom Adaptation Scale: A Behavior Rating Scale Designed to Screen Primary Grade Children for School Adaptation Problems.

    ERIC Educational Resources Information Center

    Virbickis, Joseph A.

    After a brief historical review of the background and research, the paper focuses on development of a teacher-administered behavior rating scale to screen for school adaptation problems on a large scale basis using as Ss 15 primary grade teachers and their ratings of 315 primary grade children (ages 6-to-10 years) in their classes. A 16-item…

  1. Online Learning and Recidivism Rates: Commentary

    ERIC Educational Resources Information Center

    Sellers, Martin P.

    2016-01-01

    Return-to-prison rates are high. This indicates that imprisonment is not succeeding at rehabilitation, however return to prison is significantly reduced when prisoners receive education while in prison, according to the Federal Bureau of Prisons and other related research (Aos et al., 1999; Brown, Forrester, Hull, Jobe, & McCullen, 2000; Clark…

  2. Learning about stress: neural, endocrine and behavioral adaptations.

    PubMed

    McCarty, Richard

    2016-09-01

    In this review, nonassociative learning is advanced as an organizing principle to draw together findings from both sympathetic-adrenal medullary and hypothalamic-pituitary-adrenocortical (HPA) axis responses to chronic intermittent exposure to a variety of stressors. Studies of habituation, facilitation and sensitization of stress effector systems are reviewed and linked to an animal's prior experience with a given stressor, the intensity of the stressor and the appraisal by the animal of its ability to mobilize physiological systems to adapt to the stressor. Brain pathways that regulate physiological and behavioral responses to stress are discussed, especially in light of their regulation of nonassociative processes in chronic intermittent stress. These findings may have special relevance to various psychiatric diseases, including depression and post-traumatic stress disorder (PTSD). PMID:27294884

  3. Learning about stress: neural, endocrine and behavioral adaptations.

    PubMed

    McCarty, Richard

    2016-09-01

    In this review, nonassociative learning is advanced as an organizing principle to draw together findings from both sympathetic-adrenal medullary and hypothalamic-pituitary-adrenocortical (HPA) axis responses to chronic intermittent exposure to a variety of stressors. Studies of habituation, facilitation and sensitization of stress effector systems are reviewed and linked to an animal's prior experience with a given stressor, the intensity of the stressor and the appraisal by the animal of its ability to mobilize physiological systems to adapt to the stressor. Brain pathways that regulate physiological and behavioral responses to stress are discussed, especially in light of their regulation of nonassociative processes in chronic intermittent stress. These findings may have special relevance to various psychiatric diseases, including depression and post-traumatic stress disorder (PTSD).

  4. Hierarchically clustered adaptive quantization CMAC and its learning convergence.

    PubMed

    Teddy, S D; Lai, E M K; Quek, C

    2007-11-01

    The cerebellar model articulation controller (CMAC) neural network (NN) is a well-established computational model of the human cerebellum. Nevertheless, there are two major drawbacks associated with the uniform quantization scheme of the CMAC network. They are the following: (1) a constant output resolution associated with the entire input space and (2) the generalization-accuracy dilemma. Moreover, the size of the CMAC network is an exponential function of the number of inputs. Depending on the characteristics of the training data, only a small percentage of the entire set of CMAC memory cells is utilized. Therefore, the efficient utilization of the CMAC memory is a crucial issue. One approach is to quantize the input space nonuniformly. For existing nonuniformly quantized CMAC systems, there is a tradeoff between memory efficiency and computational complexity. Inspired by the underlying organizational mechanism of the human brain, this paper presents a novel CMAC architecture named hierarchically clustered adaptive quantization CMAC (HCAQ-CMAC). HCAQ-CMAC employs hierarchical clustering for the nonuniform quantization of the input space to identify significant input segments and subsequently allocating more memory cells to these regions. The stability of the HCAQ-CMAC network is theoretically guaranteed by the proof of its learning convergence. The performance of the proposed network is subsequently benchmarked against the original CMAC network, as well as two other existing CMAC variants on two real-life applications, namely, automated control of car maneuver and modeling of the human blood glucose dynamics. The experimental results have demonstrated that the HCAQ-CMAC network offers an efficient memory allocation scheme and improves the generalization and accuracy of the network output to achieve better or comparable performances with smaller memory usages. Index Terms-Cerebellar model articulation controller (CMAC), hierarchical clustering, hierarchically

  5. USING AN ADAPTER TO PERFORM THE CHALFANT-STYLE CONTAINMENT VESSEL PERIODIC MAINTENANCE LEAK RATE TEST

    SciTech Connect

    Loftin, B.; Abramczyk, G.; Trapp, D.

    2011-06-03

    Recently the Packaging Technology and Pressurized Systems (PT&PS) organization at the Savannah River National Laboratory was asked to develop an adapter for performing the leak-rate test of a Chalfant-style containment vessel. The PT&PS organization collaborated with designers at the Department of Energy's Pantex Plant to develop the adapter currently in use for performing the leak-rate testing on the containment vessels. This paper will give the history of leak-rate testing of the Chalfant-style containment vessels, discuss the design concept for the adapter, give an overview of the design, and will present results of the testing done using the adapter.

  6. Learner Characteristic Based Learning Effort Curve Mode: The Core Mechanism on Developing Personalized Adaptive E-Learning Platform

    ERIC Educational Resources Information Center

    Hsu, Pi-Shan

    2012-01-01

    This study aims to develop the core mechanism for realizing the development of personalized adaptive e-learning platform, which is based on the previous learning effort curve research and takes into account the learner characteristics of learning style and self-efficacy. 125 university students from Taiwan are classified into 16 groups according…

  7. Learning without labeling: domain adaptation for ultrasound transducer localization.

    PubMed

    Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

    2013-01-01

    The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts.

  8. Learning without labeling: domain adaptation for ultrasound transducer localization.

    PubMed

    Heimann, Tobias; Mountney, Peter; John, Matthias; Ionasec, Razvan

    2013-01-01

    The fusion of image data from trans-esophageal echography (TEE) and X-ray fluoroscopy is attracting increasing interest in minimally-invasive treatment of structural heart disease. In order to calculate the needed transform between both imaging systems, we employ a discriminative learning based approach to localize the TEE transducer in X-ray images. Instead of time-consuming manual labeling, we generate the required training data automatically from a single volumetric image of the transducer. In order to adapt this system to real X-ray data, we use unlabeled fluoroscopy images to estimate differences in feature space density and correct covariate shift by instance weighting. An evaluation on more than 1900 images reveals that our approach reduces detection failures by 95% compared to cross validation on the test set and improves the localization error from 1.5 to 0.8 mm. Due to the automatic generation of training data, the proposed system is highly flexible and can be adapted to any medical device with minimal efforts. PMID:24505743

  9. Allelic Diversity and Its Implications for the Rate of Adaptation

    PubMed Central

    Caballero, Armando; García-Dorado, Aurora

    2013-01-01

    Genetic variation is usually estimated empirically from statistics based on population gene frequencies, but alternative statistics based on allelic diversity (number of allelic types) can provide complementary information. There is a lack of knowledge, however, on the evolutionary implications attached to allelic-diversity measures, particularly in structured populations. In this article we simulated multiple scenarios of single and structured populations in which a quantitative trait subject to stabilizing selection is adapted to different fitness optima. By forcing a global change in the optima we evaluated which diversity variables are more strongly correlated with both short- and long-term adaptation to the new optima. We found that quantitative genetic variance components for the trait and gene-frequency-diversity measures are generally more strongly correlated with short-term response to selection, whereas allelic-diversity measures are more correlated with long-term and total response to selection. Thus, allelic-diversity variables are better predictors of long-term adaptation than gene-frequency variables. This observation is also extended to unlinked neutral markers as a result of the information they convey on the demographic population history. Diffusion approximations for the allelic-diversity measures in a finite island model under the infinite-allele neutral mutation model are also provided. PMID:24121776

  10. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency.

  11. Extreme learning machine and adaptive sparse representation for image classification.

    PubMed

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency. PMID:27389571

  12. Radiographic skills learning: procedure simulation using adaptive hypermedia.

    PubMed

    Costaridou, L; Panayiotakis, G; Pallikarakis, N; Proimos, B

    1996-10-01

    The design and development of a simulation tool supporting learning of radiographic skills is reported. This tool has by textual, graphical and iconic resources, organized according to a building-block, adaptive hypermedia approach, which is described and supported by an image base of radiographs. It offers interactive user-controlled simulation of radiographic imaging procedures. The development is based on a commercially available environment (Toolbook 3.0, Asymetrix Corporation). The core of the system is an attributed precedence (priority) graph, which represents a task outline (concept and resources structure), which is dynamically adjusted to selected procedures. The user interface imitates a conventional radiography system, i.e. operating console, tube, table, patient and cassette. System parameters, such as patient positioning, focus-to-patient distance, magnification, field dimensions, tube voltage and mAs are under user control. Their effects on image quality are presented, by means of an image base acquired under controlled exposure conditions. Innovative use of hypermedia, computer based learning and simulation principles and technology in the development of this tool resulted in an enhanced interactive environment providing radiographic parameter control and visualization of parameter effects on image quality. PMID:9038530

  13. Examining the Relationship between Learning Organization Characteristics and Change Adaptation, Innovation, and Organizational Performance

    ERIC Educational Resources Information Center

    Kontoghiorghes, Constantine; Awbre, Susan M.; Feurig, Pamela L.

    2005-01-01

    The main purpose of this exploratory study was to examine the relationship between certain learning organization characteristics and change adaptation, innovation, and bottom-line organizational performance. The following learning organization characteristics were found to be the strongest predictors of rapid change adaptation, quick product or…

  14. The Future of Adaptive Learning: Does the Crowd Hold the Key?

    ERIC Educational Resources Information Center

    Heffernan, Neil T.; Ostrow, Korinn S.; Kelly, Kim; Selent, Douglas; Van Inwegen, Eric G.; Xiong, Xiaolu; Williams, Joseph Jay

    2016-01-01

    Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students--explanations, feedback, and other pedagogical interactions. Considering the…

  15. Effectiveness of Adaptive Assessment versus Learner Control in a Multimedia Learning System

    ERIC Educational Resources Information Center

    Chen, Ching-Huei; Chang, Shu-Wei

    2015-01-01

    The purpose of this study was to explore the effectiveness of adaptive assessment versus learner control in a multimedia learning system designed to help secondary students learn science. Unlike other systems, this paper presents a workflow of adaptive assessment following instructional materials that better align with learners' cognitive…

  16. Swarm Intelligence: New Techniques for Adaptive Systems to Provide Learning Support

    ERIC Educational Resources Information Center

    Wong, Lung-Hsiang; Looi, Chee-Kit

    2012-01-01

    The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…

  17. A Context-Aware Self-Adaptive Fractal Based Generalized Pedagogical Agent Framework for Mobile Learning

    ERIC Educational Resources Information Center

    Boulehouache, Soufiane; Maamri, Ramdane; Sahnoun, Zaidi

    2015-01-01

    The Pedagogical Agents (PAs) for Mobile Learning (m-learning) must be able not only to adapt the teaching to the learner knowledge level and profile but also to ensure the pedagogical efficiency within unpredictable changing runtime contexts. Therefore, to deal with this issue, this paper proposes a Context-aware Self-Adaptive Fractal Component…

  18. The Effects of Rapid Assessments and Adaptive Restudy Prompts in Multimedia Learning

    ERIC Educational Resources Information Center

    Renkl, Alexander; Skuballa, Irene T.; Schwonke, Rolf; Harr, Nora; Leber, Jasmin

    2015-01-01

    We investigated the effects of rapid assessment tasks and different adaptive restudy prompts in multimedia learning. The adaptivity was based on rapid assessment tasks that were interspersed throughout a multimedia learning environment. In Experiment 1 (N = 52 university students), we analyzed to which extent rapid assessment tasks were reactive…

  19. Exploring the Effects of Intercultural Learning on Cross-Cultural Adaptation in a Study Abroad Context

    ERIC Educational Resources Information Center

    Tsai, Yau

    2011-01-01

    This study targets Asian students studying abroad and explores the effects of intercultural learning on their cross-cultural adaptation by drawing upon a questionnaire survey. On the one hand, the results of this study find that under the influence of intercultural learning, students respond differently in their cross-cultural adaptation and no…

  20. An Adaptive Approach to Managing Knowledge Development in a Project-Based Learning Environment

    ERIC Educational Resources Information Center

    Tilchin, Oleg; Kittany, Mohamed

    2016-01-01

    In this paper we propose an adaptive approach to managing the development of students' knowledge in the comprehensive project-based learning (PBL) environment. Subject study is realized by two-stage PBL. It shapes adaptive knowledge management (KM) process and promotes the correct balance between personalized and collaborative learning. The…

  1. Second Graders Learn Animal Adaptations through Form and Function Analogy Object Boxes

    ERIC Educational Resources Information Center

    Rule, Audrey C.; Baldwin, Samantha; Schell, Robert

    2008-01-01

    This study examined the use of form and function analogy object boxes to teach second graders (n = 21) animal adaptations. The study used a pretest-posttest design to examine animal adaptation content learned through focused analogy activities as compared with reading and Internet searches for information about adaptations of animals followed by…

  2. Effects of ecological differentiation on Lotka-Volterra systems for species with behavioral adaptation and variable growth rates.

    PubMed

    Lacitignola, D; Tebaldi, C

    2005-03-01

    We study the properties of a n2-dimensional Lotka-Volterra system describing competing species that include behaviorally adaptive abilities. We indicate as behavioral adaptation a mechanism, based on a kind of learning, which is not viewed in the evolutionary sense but is intended to occur over shorter time scales. We consider a competitive adaptive n species Lotka-Volterra system, n > or = 3, in which one species is made ecologically differentiated with respect to the others by carrying capacity and intrinsic growth rate. The symmetry properties of the system and the existence of a certain class of invariant subspaces allow the introduction of a 7-dimensional reduced model, where n appears as a parameter, which gives full account of existence and stability of equilibria in the complete system. The reduced model is effective also in describing the time-dependent regimes for a large range of parameter values. The case in which one species has a strong ecological advantage (i.e. with a carrying capacity higher than the others), but with a varying growth rate, has been analyzed in detail, and time-dependent behaviors have been investigated in the case of adaptive competition among four species. Relevant questions, as species survival/exclusion, are addressed focusing on the role of adaptation. Interesting forms of species coexistence are found (i.e. competitive stable equilibria, periodic oscillations, strange attractors).

  3. Embedding Knowledge Management into Business Logic of E-learning Platform for Obtaining Adaptivity

    NASA Astrophysics Data System (ADS)

    Burdescu, Dumitru Dan; Mihaescu, Marian Cristian; Logofatu, Bogdan

    Obtaining adaptivity is one of the main concerns in current e-Learning development. This chapter proposes a methodology for obtaining adaptivity by embedding knowledge management into the business logic of the e-Learning platform. Naïve Bayes classifier is used as machine learning algorithm for obtaining the resources that need to be further accessed by learners. The analysis is accomplished on a discipline that is well structured according to a concept map.

  4. Context-Adaptive Learning Designs by Using Semantic Web Services

    ERIC Educational Resources Information Center

    Dietze, Stefan; Gugliotta, Alessio; Domingue, John

    2007-01-01

    IMS Learning Design (IMS-LD) is a promising technology aimed at supporting learning processes. IMS-LD packages contain the learning process metadata as well as the learning resources. However, the allocation of resources--whether data or services--within the learning design is done manually at design-time on the basis of the subjective appraisals…

  5. A Rate Adaptation Scheme According to Channel Conditions in Wireless LANs

    NASA Astrophysics Data System (ADS)

    Numoto, Daisuke; Inai, Hiroshi

    Rate adaptation in wireless LANs is to select the most suitable transmission rate automatically according to channel condition. If the channel condition is good, a station can choose a higher transmission rate, otherwise, it should choose a lower but noise-resistant transmission rate. Since IEEE 802.11 does not specify any rate adaptation scheme, several schemes have been proposed. However those schemes provide low throughput or unfair transmission opportunities among stations especially when the number of stations increases. In this paper, we propose a rate adaptation scheme under which the transmission rate quickly closes and then stays around an optimum rate even in the presence of a large number of stations. Via simulation, our scheme provides higher throughput than existing ones and almost equal fairness.

  6. Contributions to Adaptive Educational Hypermedia Systems via On-Line Learning Style Estimation

    ERIC Educational Resources Information Center

    Botsios, Sotiris; Georgiou, Demetrius; Safouris, Nikolaos

    2008-01-01

    In order to establish an online diagnostic system for Learning Style Estimation that contributes to the adaptation of learning objects, we propose an easily applicable expert system founded on Bayesian Networks. The proposed system makes use of Learning Style theories and associated diagnostic techniques, simultaneously avoiding certain error…

  7. Constructive, Self-Regulated, Situated, and Collaborative Learning: An Approach for the Acquisition of Adaptive Competence

    ERIC Educational Resources Information Center

    de Corte, Erik

    2012-01-01

    In today's learning society, education must focus on fostering adaptive competence (AC) defined as the ability to apply knowledge and skills flexibly in different contexts. In this article, four major types of learning are discussed--constructive, self-regulated, situated, and collaborative--in relation to what students must learn in order to…

  8. Pedagogy to Empower Chinese Learners to Adapt to Western Learning Circumstances: A Longitudinal Case-Study

    ERIC Educational Resources Information Center

    Saravanamuthu, Kala; Yap, Christine

    2014-01-01

    Deficit theorisations of Chinese Learners studying in western countries are criticised for dichotomising learning attributes into Surface- or Deep-learning approaches. Subsequent context-dependent, small culture studies of students transiting between cultures theorise learning as a dynamic journey of adapting to a range/continuum of learning…

  9. Features: Real-Time Adaptive Feature and Document Learning for Web Search.

    ERIC Educational Resources Information Center

    Chen, Zhixiang; Meng, Xiannong; Fowler, Richard H.; Zhu, Binhai

    2001-01-01

    Describes Features, an intelligent Web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Explains how Features learns from users' document relevance feedback and automatically extracts and suggests indexing keywords relevant to a search query, and learns from users' keyword relevance feedback…

  10. A Framework for Adaptive Learning Design in a Web-Conferencing Environment

    ERIC Educational Resources Information Center

    Bower, Matt

    2016-01-01

    Many recent technologies provide the ability to dynamically adjust the interface depending on the emerging cognitive and collaborative needs of the learning episode. This means that educators can adaptively re-design the learning environment during the lesson, rather than purely relying on preemptive learning design thinking. Based on a…

  11. Learning to Be a Community: Schools Need Adaptable Models to Create Successful Programs

    ERIC Educational Resources Information Center

    Ermeling, Bradley A.; Gallimore, Ronald

    2013-01-01

    Making schools learning places for teachers as well as students is a timeless and appealing vision. The growing number of professional learning communities is a hopeful sign that profound change is on the way. This is the challenge learning communities face: Schools and districts need implementation models flexible enough to adapt to local…

  12. The Effects of Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

    ERIC Educational Resources Information Center

    Roessger, Kevin M.

    2014-01-01

    In work-related instrumental learning contexts, the role of reflective activities is unclear. Kolb's experiential learning theory and Mezirow's transformative learning theory predict skill adaptation as an outcome. This prediction was tested by manipulating reflective activities and assessing participants' response and error rates…

  13. Heart Rate Variability During Early Adaptation to Space

    NASA Technical Reports Server (NTRS)

    Toscano, W. B.; Cowings, P. S.

    1994-01-01

    A recent report hypothesized that episodes of space motion sickness (SMS) were reliably associated with low frequency oscillations (less than 0.03 to less than 0.01 Hz) in heart rate variability. This paper archives a large data set for review of investigators in this field which may facilitate the evaluation of this hypothesis. Continuous recording of Electro-cardiography (ECG) and other measures were made for 6 to 12 hours per day (waking hours) of six Shuttle crewmembers for the first 3 mission days of two separate Shuttle flights. Spectral analyses of heart rate variability during approximately 200 hours of inflight is presented. In addition, nearly 200 hours of data collected on these same individuals during ground tests prior to the mission are presented. The Purpose of this Publication is to document the incidence of low frequency oscillations of heart rate in 4 people exposed to microgravity over a period of five days. In addition, this report contains spectral analyses of heart rate data collected on these same individuals during ground-based mission simulations. By archiving these data in this manner, it is our intention to make this information available to other investigators interested in studying this phenomena.

  14. Temporal Coordination and Adaptation to Rate Change in Music Performance

    ERIC Educational Resources Information Center

    Loehr, Janeen D.; Large, Edward W.; Palmer, Caroline

    2011-01-01

    People often coordinate their actions with sequences that exhibit temporal variability and unfold at multiple periodicities. We compared oscillator- and timekeeper-based accounts of temporal coordination by examining musicians' coordination of rhythmic musical sequences with a metronome that gradually changed rate at the end of a musical phrase…

  15. Adapting to test structure: letting testing teach what to learn.

    PubMed

    Garcia-Marques, Leonel; Nunes, Ludmila D; Marques, Pedro; Carneiro, Paula; Weinstein, Yana

    2015-01-01

    We propose that we encode and store information as a function of the particular ways we have used similar information in the past. More specifically, we contend that the experience of retrieval can serve as a powerful cue to the most effective ways to encode similar information in comparable future learning episodes. To explore these ideas, we did two studies in which all participants went through study-test cycles of single category lists while we manipulated the nature of the recognition tests. The recognition tests either included only same-category lures or only different-category lures. The experience of repeated testing leads participants to avoid conceptual-based strategies but only when conceptual knowledge was poorly diagnostic for recognition (i.e., in the same-category lures condition). In a second study with a similar manipulation, we showed that repeated testing with lures from the same category as study items improved performance in a final recall surprise test compared to conditions in which different-category lures were used. Such a difference is akin to the one obtained when encoding instructions focus on distinctive item features compared to cases in which the focus is on relational processing. We suggest that testing requirements lead to adaptive changes at encoding.

  16. Quantifying rates of evolutionary adaptation in response to ocean acidification.

    PubMed

    Sunday, Jennifer M; Crim, Ryan N; Harley, Christopher D G; Hart, Michael W

    2011-01-01

    The global acidification of the earth's oceans is predicted to impact biodiversity via physiological effects impacting growth, survival, reproduction, and immunology, leading to changes in species abundances and global distributions. However, the degree to which these changes will play out critically depends on the evolutionary rate at which populations will respond to natural selection imposed by ocean acidification, which remains largely unquantified. Here we measure the potential for an evolutionary response to ocean acidification in larval development rate in two coastal invertebrates using a full-factorial breeding design. We show that the sea urchin species Strongylocentrotus franciscanus has vastly greater levels of phenotypic and genetic variation for larval size in future CO(2) conditions compared to the mussel species Mytilus trossulus. Using these measures we demonstrate that S. franciscanus may have faster evolutionary responses within 50 years of the onset of predicted year-2100 CO(2) conditions despite having lower population turnover rates. Our comparisons suggest that information on genetic variation, phenotypic variation, and key demographic parameters, may lend valuable insight into relative evolutionary potentials across a large number of species.

  17. Evolutionary perspectives on learning: conceptual and methodological issues in the study of adaptive specializations.

    PubMed

    Krause, Mark A

    2015-07-01

    Inquiry into evolutionary adaptations has flourished since the modern synthesis of evolutionary biology. Comparative methods, genetic techniques, and various experimental and modeling approaches are used to test adaptive hypotheses. In psychology, the concept of adaptation is broadly applied and is central to comparative psychology and cognition. The concept of an adaptive specialization of learning is a proposed account for exceptions to general learning processes, as seen in studies of Pavlovian conditioning of taste aversions, sexual responses, and fear. The evidence generally consists of selective associations forming between biologically relevant conditioned and unconditioned stimuli, with conditioned responses differing in magnitude, persistence, or other measures relative to non-biologically relevant stimuli. Selective associations for biologically relevant stimuli may suggest adaptive specializations of learning, but do not necessarily confirm adaptive hypotheses as conceived of in evolutionary biology. Exceptions to general learning processes do not necessarily default to an adaptive specialization explanation, even if experimental results "make biological sense". This paper examines the degree to which hypotheses of adaptive specializations of learning in sexual and fear response systems have been tested using methodologies developed in evolutionary biology (e.g., comparative methods, quantitative and molecular genetics, survival experiments). A broader aim is to offer perspectives from evolutionary biology for testing adaptive hypotheses in psychological science.

  18. Introducing and adapting a novel method for investigating learning experiences in clinical learning environments.

    PubMed

    Lachmann, Hanna; Ponzer, Sari; Johansson, Unn-Britt; Karlgren, Klas

    2012-09-01

    The Contextual Activity Sampling System (CASS) is a novel methodology designed for collecting data of on-going learning experiences through frequent sampling by using mobile phones. This paper describes how it for the first time has been introduced to clinical learning environments. The purposes of this study were to cross-culturally adapt the CASS tool and questionnaire for use in clinical learning environments, investigate whether the methodology is suitable for collecting data and how it is experienced by students. A study was carried out with 51 students who reported about their activities and experiences five times a day during a 2-week course on an interprofessional training ward. Interviews were conducted after the course. The study showed that CASS provided a range of detailed and interesting qualitative and quantitative data, which we would not have been able to collect using traditional methods such as post-course questionnaires or interviews. Moreover, the participants reported that CASS worked well, was easy to use, helped them structure their days and reflect on their learning activities. This methodology proved to be a fruitful way of collecting information about experiences, which could be useful for not only researchers but also students, teachers and course designers.

  19. Enabling an integrated rate-temporal learning scheme on memristor.

    PubMed

    He, Wei; Huang, Kejie; Ning, Ning; Ramanathan, Kiruthika; Li, Guoqi; Jiang, Yu; Sze, Jiayin; Shi, Luping; Zhao, Rong; Pei, Jing

    2014-04-23

    Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel approach in neural coding implementation, which facilitates the development of bio-inspired computing systems.

  20. Enabling an Integrated Rate-temporal Learning Scheme on Memristor

    PubMed Central

    He, Wei; Huang, Kejie; Ning, Ning; Ramanathan, Kiruthika; Li, Guoqi; Jiang, Yu; Sze, JiaYin; Shi, Luping; Zhao, Rong; Pei, Jing

    2014-01-01

    Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel approach in neural coding implementation, which facilitates the development of bio-inspired computing systems. PMID:24755608

  1. Adaptation of Conceptions of Learning Science Questionnaire into Turkish and Science Teacher Candidates' Conceptions of Learning Science

    ERIC Educational Resources Information Center

    Bahçivan, Eralp; Kapucu, Serkan

    2014-01-01

    The purposes of this study were to (1) adapt an instrument "The Conceptions of Learning Science (COLS) questionnaire" into Turkish, and (2) to determine Turkish science teacher candidates' COLS. Adapting the instrument four steps were followed. Firstly, COLS questionnaire was translated into Turkish. Secondly, COLS questionnaire was…

  2. Cases on Technological Adaptability and Transnational Learning: Issues and Challenges

    ERIC Educational Resources Information Center

    Mukerji, Siran, Ed.; Tripathi, Purnendu, Ed.

    2010-01-01

    Technology holds the key for bridging the gap between access to quality education and the need for enhanced learning experiences. This book contains case studies on divergent themes of personalized learning environments, inclusive learning for social change, innovative learning and assessment techniques, technology and international partnership…

  3. Design Framework for an Adaptive MOOC Enhanced by Blended Learning: Supplementary Training and Personalized Learning for Teacher Professional Development

    ERIC Educational Resources Information Center

    Gynther, Karsten

    2016-01-01

    The research project has developed a design framework for an adaptive MOOC that complements the MOOC format with blended learning. The design framework consists of a design model and a series of learning design principles which can be used to design in-service courses for teacher professional development. The framework has been evaluated by…

  4. Adapting the Speed of Reproduction of Audio Content and Using Text Reinforcement for Maximizing the Learning Outcome though Mobile Phones

    ERIC Educational Resources Information Center

    Munoz-Organero, M.; Munoz-Merino, P. J.; Kloos, Carlos Delgado

    2011-01-01

    The use of technology in learning environments should be targeted at improving the learning outcome of the process. Several technology enhanced techniques can be used for maximizing the learning gain of particular students when having access to learning resources. One of them is content adaptation. Adapting content is especially important when…

  5. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

    PubMed

    Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

    2014-01-01

    The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management.

  6. Fast Back Propagation Learning Using Optimization of Learning Rate for Pulsed Neural Networks

    NASA Astrophysics Data System (ADS)

    Yamamoto, Kenji; Koakutsu, Seiichi; Okamoto, Takashi; Hirata, Hironori

    Neural Networks (NN) are widely applied to information processing because of its nonlinear processing capability. Digital hardware implementation of NN seems to be effective in construction of NN systems in which real-time operation and much further wide applications are possible. However, the digital hardware implementation of analogue NN is very difficult because we have to fulfill the restrictions about circuit resource, such as circuit scale, arrangement, and wiring. A technique that uses pulsed neuron model instead of analogue neuron model as a method of solving this problem has been proposed, and its effectiveness has been confirmed. To construct Pulsed Neural Networks (PNN), Back Propagation (BP) learning has been proposed. However, BP learning takes much time to construct PNN compared with the learning of analogue NN. Therefore some method to speed up BP learning of PNN is necessary. In this paper, we propose a fast BP learning using optimization of learning rate for PNN. In the proposed method, the learning rate is optimized so as to speed up the learning at every learning epoch. To evaluate the proposed method, we apply it to some pattern recognition problems, such as XOR, 3-bits parity, and digit recognition. Results of computational experiments indicate the validity of the proposed method.

  7. Improved Adaptive-Reinforcement Learning Control for morphing unmanned air vehicles.

    PubMed

    Valasek, John; Doebbler, James; Tandale, Monish D; Meade, Andrew J

    2008-08-01

    This paper presents an improved Adaptive-Reinforcement Learning Control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate Actor-Critic algorithm. Sequential Function Approximation, a Galerkin-based scattered data approximation scheme, replaces a K-Nearest Neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN. PMID:18632393

  8. Improved Adaptive-Reinforcement Learning Control for morphing unmanned air vehicles.

    PubMed

    Valasek, John; Doebbler, James; Tandale, Monish D; Meade, Andrew J

    2008-08-01

    This paper presents an improved Adaptive-Reinforcement Learning Control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate Actor-Critic algorithm. Sequential Function Approximation, a Galerkin-based scattered data approximation scheme, replaces a K-Nearest Neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN.

  9. Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.

    PubMed

    Zhang, Zhen; Ma, Yaopeng

    2016-02-06

    A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively.

  10. Modeling of Rate-Dependent Hysteresis Using a GPO-Based Adaptive Filter.

    PubMed

    Zhang, Zhen; Ma, Yaopeng

    2016-01-01

    A novel generalized play operator-based (GPO-based) nonlinear adaptive filter is proposed to model rate-dependent hysteresis nonlinearity for smart actuators. In the proposed filter, the input signal vector consists of the output of a tapped delay line. GPOs with various thresholds are used to construct a nonlinear network and connected with the input signals. The output signal of the filter is composed of a linear combination of signals from the output of GPOs. The least-mean-square (LMS) algorithm is used to adjust the weights of the nonlinear filter. The modeling results of four adaptive filter methods are compared: GPO-based adaptive filter, Volterra filter, backlash filter and linear adaptive filter. Moreover, a phenomenological operator-based model, the rate-dependent generalized Prandtl-Ishlinskii (RDGPI) model, is compared to the proposed adaptive filter. The various rate-dependent modeling methods are applied to model the rate-dependent hysteresis of a giant magnetostrictive actuator (GMA). It is shown from the modeling results that the GPO-based adaptive filter can describe the rate-dependent hysteresis nonlinear of the GMA more accurately and effectively. PMID:26861349

  11. Adaptive Learning and Reduced Cognitive Uncertainty in a Financial Organization

    ERIC Educational Resources Information Center

    Antonsen, Yngve; Thunberg, Odd Arne; Tiller, Tom

    2010-01-01

    Purpose: This paper analyses and discusses the "learning activities" that comprise obligatory learning at work by employees each month. The management strategy is to use these learning activities to spread knowledge, exchange experience and implement new skills within the organisation. The purpose of this paper is to answer the question: to what…

  12. Lost in Translation: Adapting a Face-to-Face Course Into an Online Learning Experience.

    PubMed

    Kenzig, Melissa J

    2015-09-01

    Online education has grown dramatically over the past decade. Instructors who teach face-to-face courses are being called on to adapt their courses to the online environment. Many instructors do not have sufficient training to be able to effectively move courses to an online format. This commentary discusses the growth of online learning, common challenges faced by instructors adapting courses from face-to-face to online, and best practices for translating face-to-face courses into online learning opportunities.

  13. Recovery Act: Energy Efficiency of Data Networks through Rate Adaptation (EEDNRA) - Final Technical Report

    SciTech Connect

    Matthew Andrews; Spyridon Antonakopoulos; Steve Fortune; Andrea Francini; Lisa Zhang

    2011-07-12

    This Concept Definition Study focused on developing a scientific understanding of methods to reduce energy consumption in data networks using rate adaptation. Rate adaptation is a collection of techniques that reduce energy consumption when traffic is light, and only require full energy when traffic is at full provisioned capacity. Rate adaptation is a very promising technique for saving energy: modern data networks are typically operated at average rates well below capacity, but network equipment has not yet been designed to incorporate rate adaptation. The Study concerns packet-switching equipment, routers and switches; such equipment forms the backbone of the modern Internet. The focus of the study is on algorithms and protocols that can be implemented in software or firmware to exploit hardware power-control mechanisms. Hardware power-control mechanisms are widely used in the computer industry, and are beginning to be available for networking equipment as well. Network equipment has different performance requirements than computer equipment because of the very fast rate of packet arrival; hence novel power-control algorithms are required for networking. This study resulted in five published papers, one internal report, and two patent applications, documented below. The specific technical accomplishments are the following: • A model for the power consumption of switching equipment used in service-provider telecommunication networks as a function of operating state, and measured power-consumption values for typical current equipment. • An algorithm for use in a router that adapts packet processing rate and hence power consumption to traffic load while maintaining performance guarantees on delay and throughput. • An algorithm that performs network-wide traffic routing with the objective of minimizing energy consumption, assuming that routers have less-than-ideal rate adaptivity. • An estimate of the potential energy savings in service-provider networks

  14. Stimulating the cerebellum affects visuomotor adaptation but not intermanual transfer of learning

    PubMed Central

    Block, Hannah; Celnik, Pablo

    2013-01-01

    When systematic movement errors occur, the brain responds with a systematic change in motor behavior. This type of adaptive motor learning can transfer intermanually; adaptation of movements of the right hand in response to training with a perturbed visual signal (visuomotor adaptation) may carry over to the left hand. While visuomotor adaptation has been studied extensively, it is unclear whether the cerebellum, a structure involved in adaptation, is important for intermanual transfer as well. We addressed this question with three experiments in which subjects reached with their right hands as a 30° visuomotor rotation was introduced. Subjects received anodal or sham transcranial direct current stimulation (tDCS) on the trained (Experiment 1) or untrained (Experiment 2) hemisphere of the cerebellum, or, for comparison, motor cortex (M1). After the training period, subjects reached with their left hand, without visual feedback, to assess intermanual transfer of learning aftereffects. Stimulation of the right cerebellum caused faster adaptation, but none of the stimulation sites affected transfer. To ascertain whether cerebellar stimulation would increase transfer if subjects learned faster as well as a larger amount, in Experiment 3 anodal and sham cerebellar groups experienced a shortened training block such that the anodal group learned more than sham. Despite the difference in adaptation magnitude, transfer was similar across these groups, although smaller than in Experiment 1. Our results suggest that intermanual transfer of visuomotor learning does not depend on cerebellar activity, and that the number of movements performed at plateau is an important predictor of transfer. PMID:23625383

  15. Learning to push and learning to move: the adaptive control of contact forces

    PubMed Central

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A.

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in “compatible pairs” connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions

  16. Learning to push and learning to move: the adaptive control of contact forces.

    PubMed

    Casadio, Maura; Pressman, Assaf; Mussa-Ivaldi, Ferdinando A

    2015-01-01

    To be successful at manipulating objects one needs to apply simultaneously well controlled movements and contact forces. We present a computational theory of how the brain may successfully generate a vast spectrum of interactive behaviors by combining two independent processes. One process is competent to control movements in free space and the other is competent to control contact forces against rigid constraints. Free space and rigid constraints are singularities at the boundaries of a continuum of mechanical impedance. Within this continuum, forces and motions occur in "compatible pairs" connected by the equations of Newtonian dynamics. The force applied to an object determines its motion. Conversely, inverse dynamics determine a unique force trajectory from a movement trajectory. In this perspective, we describe motor learning as a process leading to the discovery of compatible force/motion pairs. The learned compatible pairs constitute a local representation of the environment's mechanics. Experiments on force field adaptation have already provided us with evidence that the brain is able to predict and compensate the forces encountered when one is attempting to generate a motion. Here, we tested the theory in the dual case, i.e., when one attempts at applying a desired contact force against a simulated rigid surface. If the surface becomes unexpectedly compliant, the contact point moves as a function of the applied force and this causes the applied force to deviate from its desired value. We found that, through repeated attempts at generating the desired contact force, subjects discovered the unique compatible hand motion. When, after learning, the rigid contact was unexpectedly restored, subjects displayed after effects of learning, consistent with the concurrent operation of a motion control system and a force control system. Together, theory and experiment support a new and broader view of modularity in the coordinated control of forces and motions. PMID

  17. Perceptual Learning of Time-Compressed Speech: More than Rapid Adaptation

    PubMed Central

    Banai, Karen; Lavner, Yizhar

    2012-01-01

    Background Time-compressed speech, a form of rapidly presented speech, is harder to comprehend than natural speech, especially for non-native speakers. Although it is possible to adapt to time-compressed speech after a brief exposure, it is not known whether additional perceptual learning occurs with further practice. Here, we ask whether multiday training on time-compressed speech yields more learning than that observed during the initial adaptation phase and whether the pattern of generalization following successful learning is different than that observed with initial adaptation only. Methodology/Principal Findings Two groups of non-native Hebrew speakers were tested on five different conditions of time-compressed speech identification in two assessments conducted 10–14 days apart. Between those assessments, one group of listeners received five practice sessions on one of the time-compressed conditions. Between the two assessments, trained listeners improved significantly more than untrained listeners on the trained condition. Furthermore, the trained group generalized its learning to two untrained conditions in which different talkers presented the trained speech materials. In addition, when the performance of the non-native speakers was compared to that of a group of naïve native Hebrew speakers, performance of the trained group was equivalent to that of the native speakers on all conditions on which learning occurred, whereas performance of the untrained non-native listeners was substantially poorer. Conclusions/Significance Multiday training on time-compressed speech results in significantly more perceptual learning than brief adaptation. Compared to previous studies of adaptation, the training induced learning is more stimulus specific. Taken together, the perceptual learning of time-compressed speech appears to progress from an initial, rapid adaptation phase to a subsequent prolonged and more stimulus specific phase. These findings are consistent with

  18. Designing an Adaptive Web-Based Learning System Based on Students' Cognitive Styles Identified Online

    ERIC Educational Resources Information Center

    Lo, Jia-Jiunn; Chan, Ya-Chen; Yeh, Shiou-Wen

    2012-01-01

    This study developed an adaptive web-based learning system focusing on students' cognitive styles. The system is composed of a student model and an adaptation model. It collected students' browsing behaviors to update the student model for unobtrusively identifying student cognitive styles through a multi-layer feed-forward neural network (MLFF).…

  19. Higher-Order Thinking Development through Adaptive Problem-Based Learning

    ERIC Educational Resources Information Center

    Raiyn, Jamal; Tilchin, Oleg

    2015-01-01

    In this paper we propose an approach to organizing Adaptive Problem-Based Learning (PBL) leading to the development of Higher-Order Thinking (HOT) skills and collaborative skills in students. Adaptability of PBL is expressed by changes in fixed instructor assessments caused by the dynamics of developing HOT skills needed for problem solving,…

  20. ActiveTutor: Towards More Adaptive Features in an E-Learning Framework

    ERIC Educational Resources Information Center

    Fournier, Jean-Pierre; Sansonnet, Jean-Paul

    2008-01-01

    Purpose: This paper aims to sketch the emerging notion of auto-adaptive software when applied to e-learning software. Design/methodology/approach: The study and the implementation of the auto-adaptive architecture are based on the operational framework "ActiveTutor" that is used for teaching the topic of computer science programming in first-grade…

  1. The older person has a stroke: Learning to adapt using the Feldenkrais® Method.

    PubMed

    Jackson-Wyatt, O

    1995-01-01

    The older person with a stroke requires adapted therapeutic interventions to take into account normal age-related changes. The Feldenkrais® Method presents a model for learning to promote adaptability that addresses key functional changes seen with normal aging. Clinical examples related to specific functional tasks are discussed to highlight major treatment modifications and neuromuscular, psychological, emotional, and sensory considerations. PMID:27619899

  2. Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices

    ERIC Educational Resources Information Center

    Hsu, Ching-Kun

    2015-01-01

    The aim of this study was to provide adaptive assistance to improve the listening comprehension of eleventh grade students. This study developed a video-based language learning system for handheld devices, using three levels of caption filtering adapted to student needs. Elementary level captioning excluded 220 English sight words (see Section 1…

  3. A User-Centric Adaptive Learning System for E-Learning 2.0

    ERIC Educational Resources Information Center

    Huang, Shiu-Li; Shiu, Jung-Hung

    2012-01-01

    The success of Web 2.0 inspires e-learning to evolve into e-learning 2.0, which exploits collective intelligence to achieve user-centric learning. However, searching for suitable learning paths and content for achieving a learning goal is time consuming and troublesome on e-learning 2.0 platforms. Therefore, introducing formal learning in these…

  4. Adaptive Semantic and Social Web-based learning and assessment environment for the STEM

    NASA Astrophysics Data System (ADS)

    Babaie, Hassan; Atchison, Chris; Sunderraman, Rajshekhar

    2014-05-01

    We are building a cloud- and Semantic Web-based personalized, adaptive learning environment for the STEM fields that integrates and leverages Social Web technologies to allow instructors and authors of learning material to collaborate in semi-automatic development and update of their common domain and task ontologies and building their learning resources. The semi-automatic ontology learning and development minimize issues related to the design and maintenance of domain ontologies by knowledge engineers who do not have any knowledge of the domain. The social web component of the personal adaptive system will allow individual and group learners to interact with each other and discuss their own learning experience and understanding of course material, and resolve issues related to their class assignments. The adaptive system will be capable of representing key knowledge concepts in different ways and difficulty levels based on learners' differences, and lead to different understanding of the same STEM content by different learners. It will adapt specific pedagogical strategies to individual learners based on their characteristics, cognition, and preferences, allow authors to assemble remotely accessed learning material into courses, and provide facilities for instructors to assess (in real time) the perception of students of course material, monitor their progress in the learning process, and generate timely feedback based on their understanding or misconceptions. The system applies a set of ontologies that structure the learning process, with multiple user friendly Web interfaces. These include the learning ontology (models learning objects, educational resources, and learning goal); context ontology (supports adaptive strategy by detecting student situation), domain ontology (structures concepts and context), learner ontology (models student profile, preferences, and behavior), task ontologies, technological ontology (defines devices and places that surround the

  5. Optimal asymptotic learning rate: Macroscopic versus microscopic dynamics

    NASA Astrophysics Data System (ADS)

    Leen, Todd K.; Schottky, Bernhard; Saad, David

    1999-01-01

    We investigate the asymptotic dynamics of on-line learning for neural networks, and provide an exact solution to the network dynamics at late times under various annealing schedules. The dynamics is solved using two different frameworks: the master equation and order parameter dynamics, which concentrate on microscopic and macroscopic parameters, respectively. The two approaches provide complementary descriptions of the dynamics. Optimal annealing rates and the corresponding prefactors are derived for soft committee machine networks with hidden layers of arbitrary size.

  6. International Students' Culture Learning and Cultural Adaptation in China

    ERIC Educational Resources Information Center

    An, Ran; Chiang, Shiao-Yun

    2015-01-01

    This article examines international students' cultural adaptation at a major national university in China. A survey was designed to measure international students' adaptation to the Chinese sociocultural and educational environments in terms of five dimensions: (1) cultural empathy, (2) open-mindedness, (3) emotional stability, (4) social…

  7. Management Strategies for Complex Adaptive Systems: Sensemaking, Learning, and Improvisation

    ERIC Educational Resources Information Center

    McDaniel, Reuben R., Jr.

    2007-01-01

    Misspecification of the nature of organizations may be a major reason for difficulty in achieving performance improvement. Organizations are often viewed as machine-like, but complexity science suggests that organizations should be viewed as complex adaptive systems. I identify the characteristics of complex adaptive systems and give examples of…

  8. Academic Accountability and University Adaptation: The Architecture of an Academic Learning Organization.

    ERIC Educational Resources Information Center

    Dill, David D.

    1999-01-01

    Discussses various adaptations in organizational structure and governance of academic learning institutions, using case studies of universities that are attempting to improve the quality of teaching and the learning process. Identifies five characteristics typical of such organizations: (1) a culture of evidence; (2) improved coordination of…

  9. Impact of Nursing Learning Environments on Adaptive Competency Development in Baccalaureate Nursing Students.

    ERIC Educational Resources Information Center

    Laschinger, Heather K. Spence

    1992-01-01

    Kolb's experiential learning theory was used as a framework to study 179 generic baccalaureate students' perceptions of the different types of learning environments and adaptive competencies. Clinical experience and preceptorships contributed more to competency development than did nursing or nonnursing classes. (JOW)

  10. A Standard-Based Model for Adaptive E-Learning Platform for Mauritian Academic Institutions

    ERIC Educational Resources Information Center

    Kanaksabee, P.; Odit, M. P.; Ramdoyal, A.

    2011-01-01

    The key aim of this paper is to introduce a standard-based model for adaptive e-learning platform for Mauritian academic institutions and to investigate the conditions and tools required to implement this model. The main forces of the system are that it allows collaborative learning, communication among user, and reduce considerable paper work.…

  11. Educational Multimedia Profiling Recommendations for Device-Aware Adaptive Mobile Learning

    ERIC Educational Resources Information Center

    Moldovan, Arghir-Nicolae; Ghergulescu, Ioana; Muntean, Cristina Hava

    2014-01-01

    Mobile learning is seeing a fast adoption with the increasing availability and affordability of mobile devices such as smartphones and tablets. As the creation and consumption of educational multimedia content on mobile devices is also increasing fast, educators and mobile learning providers are faced with the challenge to adapt multimedia type…

  12. Partner Knowledge Awareness in Knowledge Communication: Learning by Adapting to the Partner

    ERIC Educational Resources Information Center

    Dehler Zufferey, Jessica; Bodemer, Daniel; Buder, Jurgen; Hesse, Friedrich W.

    2011-01-01

    Awareness of the knowledge of learning partners is not always sufficiently available in collaborative learning scenarios. To compensate, the authors propose to provide collaborators with partner knowledge awareness by means of a visualization tool. Partner knowledge awareness can be used to adapt messages toward the partner. This study…

  13. An Online Adaptive Learning Environment for Critical-Thinking-Infused English Literacy Instruction

    ERIC Educational Resources Information Center

    Yang, Ya-Ting Carolyn; Gamble, Jeffrey Hugh; Hung, Yu-Wan; Lin, Tzu-Yun

    2014-01-01

    Critical thinking (CT) and English literacy are two essential 21st century competencies that are a priority for teaching and learning in an increasingly digital learning environment. Taking advantage of innovations in educational technology, this study empirically investigates the effectiveness of CT-infused adaptive English literacy instruction…

  14. Towards Increased Relevance: Context-Adapted Models of the Learning Organization

    ERIC Educational Resources Information Center

    Örtenblad, Anders

    2015-01-01

    Purpose: The purposes of this paper are to take a closer look at the relevance of the idea of the learning organization for organizations in different generalized organizational contexts; to open up for the existence of multiple, context-adapted models of the learning organization; and to suggest a number of such models.…

  15. Ontology-Based Multimedia Authoring Tool for Adaptive E-Learning

    ERIC Educational Resources Information Center

    Deng, Lawrence Y.; Keh, Huan-Chao; Liu, Yi-Jen

    2010-01-01

    More video streaming technologies supporting distance learning systems are becoming popular among distributed network environments. In this paper, the authors develop a multimedia authoring tool for adaptive e-learning by using characterization of extended media streaming technologies. The distributed approach is based on an ontology-based model.…

  16. Perceived Control and Adaptive Coping: Programs for Adolescent Students Who Have Learning Disabilities

    ERIC Educational Resources Information Center

    Firth, Nola; Frydenberg, Erica; Greaves, Daryl

    2008-01-01

    This study explored the effect of a coping program and a teacher feedback intervention on perceived control and adaptive coping for 98 adolescent students who had specific learning disabilities. The coping program was modified to build personal control and to address the needs of students who have specific learning disabilities. The teacher…

  17. The Effect of Adaptive Confidence Strategies in Computer-Assisted Instruction on Learning and Learner Confidence

    ERIC Educational Resources Information Center

    Warren, Richard Daniel

    2012-01-01

    The purpose of this research was to investigate the effects of including adaptive confidence strategies in instructionally sound computer-assisted instruction (CAI) on learning and learner confidence. Seventy-one general educational development (GED) learners recruited from various GED learning centers at community colleges in the southeast United…

  18. Adaptation of the Students' Motivation towards Science Learning (SMTSL) Questionnaire in the Greek Language

    ERIC Educational Resources Information Center

    Dermitzaki, Irini; Stavroussi, Panayiota; Vavougios, Denis; Kotsis, Konstantinos T.

    2013-01-01

    The present study aimed at adapting in the Greek language the Students' Motivation Towards Science Learning (SMTSL) questionnaire developed by Tuan, Chin, and Shieh ("INT J SCI EDUC" 27(6): 639-654, 2005a) into a different cultural context, a different age group, that is, in university students and with a focus on physics learning.…

  19. Adaptive Human Scaffolding Facilitates Adolescents' Self-Regulated Learning with Hypermedia

    ERIC Educational Resources Information Center

    Azevedo, Roger; Cromley, Jennifer G.; Winters, Fielding I.; Moos, Daniel C.; Greene, Jeffrey A.

    2005-01-01

    This study examines the effectiveness of three scaffolding conditions on adolescents' learning about the circulatory system with a hypermedia learning environment. One hundred and eleven adolescents (n = 111) were randomly assigned to one of three scaffolding conditions (adaptive scaffolding (AS), fixed scaffolding (FS), or no scaffolding (NS))…

  20. The effect of retinal image error update rate on human vestibulo-ocular reflex gain adaptation.

    PubMed

    Fadaee, Shannon B; Migliaccio, Americo A

    2016-04-01

    The primary function of the angular vestibulo-ocular reflex (VOR) is to stabilise images on the retina during head movements. Retinal image movement is the likely feedback signal that drives VOR modification/adaptation for different viewing contexts. However, it is not clear whether a retinal image position or velocity error is used primarily as the feedback signal. Recent studies examining this signal are limited because they used near viewing to modify the VOR. However, it is not known whether near viewing drives VOR adaptation or is a pre-programmed contextual cue that modifies the VOR. Our study is based on analysis of the VOR evoked by horizontal head impulses during an established adaptation task. Fourteen human subjects underwent incremental unilateral VOR adaptation training and were tested using the scleral search coil technique over three separate sessions. The update rate of the laser target position (source of the retinal image error signal) used to drive VOR adaptation was different for each session [50 (once every 20 ms), 20 and 15/35 Hz]. Our results show unilateral VOR adaptation occurred at 50 and 20 Hz for both the active (23.0 ± 9.6 and 11.9 ± 9.1% increase on adapting side, respectively) and passive VOR (13.5 ± 14.9, 10.4 ± 12.2%). At 15 Hz, unilateral adaptation no longer occurred in the subject group for both the active and passive VOR, whereas individually, 4/9 subjects tested at 15 Hz had significant adaptation. Our findings suggest that 1-2 retinal image position error signals every 100 ms (i.e. target position update rate 15-20 Hz) are sufficient to drive VOR adaptation.

  1. The effect of retinal image error update rate on human vestibulo-ocular reflex gain adaptation.

    PubMed

    Fadaee, Shannon B; Migliaccio, Americo A

    2016-04-01

    The primary function of the angular vestibulo-ocular reflex (VOR) is to stabilise images on the retina during head movements. Retinal image movement is the likely feedback signal that drives VOR modification/adaptation for different viewing contexts. However, it is not clear whether a retinal image position or velocity error is used primarily as the feedback signal. Recent studies examining this signal are limited because they used near viewing to modify the VOR. However, it is not known whether near viewing drives VOR adaptation or is a pre-programmed contextual cue that modifies the VOR. Our study is based on analysis of the VOR evoked by horizontal head impulses during an established adaptation task. Fourteen human subjects underwent incremental unilateral VOR adaptation training and were tested using the scleral search coil technique over three separate sessions. The update rate of the laser target position (source of the retinal image error signal) used to drive VOR adaptation was different for each session [50 (once every 20 ms), 20 and 15/35 Hz]. Our results show unilateral VOR adaptation occurred at 50 and 20 Hz for both the active (23.0 ± 9.6 and 11.9 ± 9.1% increase on adapting side, respectively) and passive VOR (13.5 ± 14.9, 10.4 ± 12.2%). At 15 Hz, unilateral adaptation no longer occurred in the subject group for both the active and passive VOR, whereas individually, 4/9 subjects tested at 15 Hz had significant adaptation. Our findings suggest that 1-2 retinal image position error signals every 100 ms (i.e. target position update rate 15-20 Hz) are sufficient to drive VOR adaptation. PMID:26715411

  2. Rule-based mechanisms of learning for intelligent adaptive flight control

    NASA Technical Reports Server (NTRS)

    Handelman, David A.; Stengel, Robert F.

    1990-01-01

    How certain aspects of human learning can be used to characterize learning in intelligent adaptive control systems is investigated. Reflexive and declarative memory and learning are described. It is shown that model-based systems-theoretic adaptive control methods exhibit attributes of reflexive learning, whereas the problem-solving capabilities of knowledge-based systems of artificial intelligence are naturally suited for implementing declarative learning. Issues related to learning in knowledge-based control systems are addressed, with particular attention given to rule-based systems. A mechanism for real-time rule-based knowledge acquisition is suggested, and utilization of this mechanism within the context of failure diagnosis for fault-tolerant flight control is demonstrated.

  3. A service based adaptive U-learning system using UX.

    PubMed

    Jeong, Hwa-Young; Yi, Gangman

    2014-01-01

    In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

  4. A Service Based Adaptive U-Learning System Using UX

    PubMed Central

    Jeong, Hwa-Young

    2014-01-01

    In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users' tailored materials according to their learning style. That is, we analyzed the user's data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques. PMID:25147832

  5. Adaptive learning and decision-making under uncertainty by metaplastic synapses guided by a surprise detection system

    PubMed Central

    Iigaya, Kiyohito

    2016-01-01

    Recent experiments have shown that animals and humans have a remarkable ability to adapt their learning rate according to the volatility of the environment. Yet the neural mechanism responsible for such adaptive learning has remained unclear. To fill this gap, we investigated a biophysically inspired, metaplastic synaptic model within the context of a well-studied decision-making network, in which synapses can change their rate of plasticity in addition to their efficacy according to a reward-based learning rule. We found that our model, which assumes that synaptic plasticity is guided by a novel surprise detection system, captures a wide range of key experimental findings and performs as well as a Bayes optimal model, with remarkably little parameter tuning. Our results further demonstrate the computational power of synaptic plasticity, and provide insights into the circuit-level computation which underlies adaptive decision-making. DOI: http://dx.doi.org/10.7554/eLife.18073.001 PMID:27504806

  6. Usability of clinical decision support system as a facilitator for learning the assistive technology adaptation process.

    PubMed

    Danial-Saad, Alexandra; Kuflik, Tsvi; Weiss, Patrice L Tamar; Schreuer, Naomi

    2016-01-01

    The aim of this study was to evaluate the usability of Ontology Supported Computerized Assistive Technology Recommender (OSCAR), a Clinical Decision Support System (CDSS) for the assistive technology adaptation process, its impact on learning the matching process, and to determine the relationship between its usability and learnability. Two groups of expert and novice clinicians (total, n = 26) took part in this study. Each group filled out system usability scale (SUS) to evaluate OSCAR's usability. The novice group completed a learning questionnaire to assess OSCAR's effect on their ability to learn the matching process. Both groups rated OSCAR's usability as "very good", (M [SUS] = 80.7, SD = 11.6, median = 83.7) by the novices, and (M [SUS] = 81.2, SD = 6.8, median = 81.2) by the experts. The Mann-Whitney results indicated that no significant differences were found between the expert and novice groups in terms of OSCAR's usability. A significant positive correlation existed between the usability of OSCAR and the ability to learn the adaptation process (rs = 0.46, p = 0.04). Usability is an important factor in the acceptance of a system. The successful application of user-centered design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically in developing other systems. Implications for Rehabilitation Creating a CDSS with a focus on its usability is an important factor for its acceptance by its users. Successful usability outcomes can impact the learning process of the subject matter in general, and the AT prescription process in particular. The successful application of User-Centered Design principles during the development of OSCAR may serve as a case study that models the significant elements to be considered, theoretically and practically. The study emphasizes the importance of close collaboration between the developers and

  7. Adaptive social learning strategies in temporally and spatially varying environments : how temporal vs. spatial variation, number of cultural traits, and costs of learning influence the evolution of conformist-biased transmission, payoff-biased transmission, and individual learning.

    PubMed

    Nakahashi, Wataru; Wakano, Joe Yuichiro; Henrich, Joseph

    2012-12-01

    Long before the origins of agriculture human ancestors had expanded across the globe into an immense variety of environments, from Australian deserts to Siberian tundra. Survival in these environments did not principally depend on genetic adaptations, but instead on evolved learning strategies that permitted the assembly of locally adaptive behavioral repertoires. To develop hypotheses about these learning strategies, we have modeled the evolution of learning strategies to assess what conditions and constraints favor which kinds of strategies. To build on prior work, we focus on clarifying how spatial variability, temporal variability, and the number of cultural traits influence the evolution of four types of strategies: (1) individual learning, (2) unbiased social learning, (3) payoff-biased social learning, and (4) conformist transmission. Using a combination of analytic and simulation methods, we show that spatial-but not temporal-variation strongly favors the emergence of conformist transmission. This effect intensifies when migration rates are relatively high and individual learning is costly. We also show that increasing the number of cultural traits above two favors the evolution of conformist transmission, which suggests that the assumption of only two traits in many models has been conservative. We close by discussing how (1) spatial variability represents only one way of introducing the low-level, nonadaptive phenotypic trait variation that so favors conformist transmission, the other obvious way being learning errors, and (2) our findings apply to the evolution of conformist transmission in social interactions. Throughout we emphasize how our models generate empirical predictions suitable for laboratory testing.

  8. Adapting Cognitive Walkthrough to Support Game Based Learning Design

    ERIC Educational Resources Information Center

    Farrell, David; Moffat, David C.

    2014-01-01

    For any given Game Based Learning (GBL) project to be successful, the player must learn something. Designers may base their work on pedagogical research, but actual game design is still largely driven by intuition. People are famously poor at unsupported methodical thinking and relying so much on instinct is an obvious weak point in GBL design…

  9. Psychosocial and Adaptive Deficits Associated with Learning Disability Subtypes

    ERIC Educational Resources Information Center

    Backenson, Erica M.; Holland, Sara C.; Kubas, Hanna A.; Fitzer, Kim R.; Wilcox, Gabrielle; Carmichael, Jessica A.; Fraccaro, Rebecca L.; Smith, Amanda D.; Macoun, Sarah J.; Harrison, Gina L.; Hale, James B.

    2015-01-01

    Children with specific learning disabilities (SLD) have deficits in the basic psychological processes that interfere with learning and academic achievement, and for some SLD subtypes, these deficits can also lead to emotional and/or behavior problems. This study examined psychosocial functioning in 123 students, aged 6 to 11, who underwent…

  10. Adaptative Peer to Peer Data Sharing for Technology Enhanced Learning

    NASA Astrophysics Data System (ADS)

    Angelaccio, Michele; Buttarazzi, Berta

    Starting from the hypothesis that P2P Data Sharing in a direct teaching scenario (e.g.: a classroom lesson) may lead to relevant benefits, this paper explores the features of EduSHARE a Collaborative Learning System useful for Enhanced Learning Process.

  11. Diminishing Returns of Population Size in the Rate of RNA Virus Adaptation

    PubMed Central

    Miralles, Rosario; Moya, Andrés; Elena, Santiago F.

    2000-01-01

    Whenever an asexual viral population evolves by adapting to new environmental conditions, beneficial mutations, the ultimate cause of adaptation, are randomly produced and then fixed in the population. The larger the population size and the higher the mutation rate, the more beneficial mutations can be produced per unit time. With the usually high mutation rate of RNA viruses and in a large enough population, several beneficial mutations could arise at the same time but in different genetic backgrounds, and if the virus is asexual, they will never be brought together through recombination. Thus, the best of these genotypes must outcompete each other on their way to fixation. This competition among beneficial mutations has the effect of slowing the overall rate of adaptation. This phenomenon is known as clonal interference. Clonal interference predicts a speed limit for adaptation as the population size increases. In the present report, by varying the size of evolving vesicular stomatitis virus populations, we found evidence clearly demonstrating this speed limit and thus indicating that clonal interference might be an important factor modulating the rate of adaptation to an in vitro cell system. Several evolutionary and epidemiological implications of the clonal interference model applied to RNA viruses are discussed. PMID:10729131

  12. Lessons Learned from the Everglades Collaborative Adaptive Management Program

    EPA Science Inventory

    Recent technical papers explore whether adaptive management (AM) is useful for environmental management and restoration efforts and discuss the many challenges to overcome for successful implementation, especially for large-scale restoration programs (McLain and Lee 1996; Levine ...

  13. Potentiating mGluR5 function with a positive allosteric modulator enhances adaptive learning.

    PubMed

    Xu, Jian; Zhu, Yongling; Kraniotis, Stephen; He, Qionger; Marshall, John J; Nomura, Toshihiro; Stauffer, Shaun R; Lindsley, Craig W; Conn, P Jeffrey; Contractor, Anis

    2013-08-01

    Metabotropic glutamate receptor 5 (mGluR5) plays important roles in modulating neural activity and plasticity and has been associated with several neuropathological disorders. Previous work has shown that genetic ablation or pharmacological inhibition of mGluR5 disrupts fear extinction and spatial reversal learning, suggesting that mGluR5 signaling is required for different forms of adaptive learning. Here, we tested whether ADX47273, a selective positive allosteric modulator (PAM) of mGluR5, can enhance adaptive learning in mice. We found that systemic administration of the ADX47273 enhanced reversal learning in the Morris Water Maze, an adaptive task. In addition, we found that ADX47273 had no effect on single-session and multi-session extinction, but administration of ADX47273 after a single retrieval trial enhanced subsequent fear extinction learning. Together these results demonstrate a role for mGluR5 signaling in adaptive learning, and suggest that mGluR5 PAMs represent a viable strategy for treatment of maladaptive learning and for improving behavioral flexibility.

  14. Potentiating mGluR5 function with a positive allosteric modulator enhances adaptive learning

    PubMed Central

    Xu, Jian; Zhu, Yongling; Kraniotis, Stephen; He, Qionger; Marshall, John J.; Nomura, Toshihiro; Stauffer, Shaun R.; Lindsley, Craig W.; Conn, P. Jeffrey; Contractor, Anis

    2013-01-01

    Metabotropic glutamate receptor 5 (mGluR5) plays important roles in modulating neural activity and plasticity and has been associated with several neuropathological disorders. Previous work has shown that genetic ablation or pharmacological inhibition of mGluR5 disrupts fear extinction and spatial reversal learning, suggesting that mGluR5 signaling is required for different forms of adaptive learning. Here, we tested whether ADX47273, a selective positive allosteric modulator (PAM) of mGluR5, can enhance adaptive learning in mice. We found that systemic administration of the ADX47273 enhanced reversal learning in the Morris Water Maze, an adaptive task. In addition, we found that ADX47273 had no effect on single-session and multi-session extinction, but administration of ADX47273 after a single retrieval trial enhanced subsequent fear extinction learning. Together these results demonstrate a role for mGluR5 signaling in adaptive learning, and suggest that mGluR5 PAMs represent a viable strategy for treatment of maladaptive learning and for improving behavioral flexibility. PMID:23869026

  15. Seeing is believing: effects of visual contextual cues on learning and transfer of locomotor adaptation.

    PubMed

    Torres-Oviedo, Gelsy; Bastian, Amy J

    2010-12-15

    Devices such as robots or treadmills are often used to drive motor learning because they can create novel physical environments. However, the learning (i.e., adaptation) acquired on these devices only partially generalizes to natural movements. What determines the specificity of motor learning, and can this be reliably made more general? Here we investigated the effect of visual cues on the specificity of split-belt walking adaptation. We systematically removed vision to eliminate the visual-proprioceptive mismatch that is a salient cue specific to treadmills: vision indicates that we are not moving while leg proprioception indicates that we are. We evaluated the adaptation of temporal and spatial features of gait (i.e., timing and location of foot landing), their transfer to walking over ground, and washout of adaptation when subjects returned to the treadmill. Removing vision during both training (i.e., on the treadmill) and testing (i.e., over ground) strongly improved the transfer of treadmill adaptation to natural walking. Removing vision only during training increased transfer of temporal adaptation, whereas removing vision only during testing increased the transfer of spatial adaptation. This dissociation reveals differences in adaptive mechanisms for temporal and spatial features of walking. Finally training without vision increased the amount that was learned and was linked to the variability in the behavior during adaptation. In conclusion, contextual cues can be manipulated to modulate the magnitude, transfer, and washout of device-induced learning in humans. These results bring us closer to our ultimate goal of developing rehabilitation strategies that improve movements beyond the clinical setting.

  16. Costs and Benefits of High Mutation Rates: Adaptive Evolution of Bacteria in the Mouse Gut

    NASA Astrophysics Data System (ADS)

    Giraud, Antoine; Matic, Ivan; Tenaillon, Olivier; Clara, Antonio; Radman, Miroslav; Fons, Michel; Taddei, François

    2001-03-01

    We have shown that bacterial mutation rates change during the experimental colonization of the mouse gut. A high mutation rate was initially beneficial because it allowed faster adaptation, but this benefit disappeared once adaptation was achieved. Mutator bacteria accumulated mutations that, although neutral in the mouse gut, are often deleterious in secondary environments. Consistently, the competitiveness of mutator bacteria is reduced during transmission to and re-colonization of similar hosts. The short-term advantages and long-term disadvantages of mutator bacteria could account for their frequency in nature.

  17. Reinforcement learning by Hebbian synapses with adaptive thresholds.

    PubMed

    Pennartz, C M

    1997-11-01

    A central problem in learning theory is how the vertebrate brain processes reinforcing stimuli in order to master complex sensorimotor tasks. This problem belongs to the domain of supervised learning, in which errors in the response of a neural network serve as the basis for modification of synaptic connectivity in the network and thereby train it on a computational task. The model presented here shows how a reinforcing feedback can modify synapses in a neuronal network according to the principles of Hebbian learning. The reinforcing feedback steers synapses towards long-term potentiation or depression by critically influencing the rise in postsynaptic calcium, in accordance with findings on synaptic plasticity in mammalian brain. An important feature of the model is the dependence of modification thresholds on the previous history of reinforcing feedback processed by the network. The learning algorithm trained networks successfully on a task in which a population vector in the motor output was required to match a sensory stimulus vector presented shortly before. In another task, networks were trained to compute coordinate transformations by combining different visual inputs. The model continued to behave well when simplified units were replaced by single-compartment neurons equipped with several conductances and operating in continuous time. This novel form of reinforcement learning incorporates essential properties of Hebbian synaptic plasticity and thereby shows that supervised learning can be accomplished by a learning rule similar to those used in physiologically plausible models of unsupervised learning. The model can be crudely correlated to the anatomy and electrophysiology of the amygdala, prefrontal and cingulate cortex and has predictive implications for further experiments on synaptic plasticity and learning processes mediated by these areas.

  18. Evaluation of Neural Response Telemetry (NRT™) with focus on long-term rate adaptation over a wide range of stimulation rates.

    PubMed

    Huarte, Alicia; Ramos, Angel; Morera, Constantino; Garcia-Ibáñez, Luis; Battmer, Rolf; Dillier, Norbert; Wesarg, Thomas; Müller-Deile, Joachim; Hey, Mattias; Offeciers, Erwin; von Wallenberg, Ernst; Coudert, Chrystelle; Killian, Matthijs

    2014-05-01

    Custom Sound EP™ (CSEP) is an advanced flexible software tool dedicated to recording of electrically evoked compound action potentials (ECAPs) in Nucleus® recipients using Neural Response Telemetry™ (NRT™). European multi-centre studies of the Freedom™ cochlear implant system confirmed that CSEP offers tools to effectively record ECAP thresholds, amplitude growth functions, recovery functions, spread of excitation functions, and rate adaptation functions and an automated algorithm (AutoNRT™) to measure threshold profiles. This paper reports on rate adaptation measurements. Rate adaptation of ECAP amplitudes can successfully be measured up to rates of 495 pulses per second (pps) by repeating conventional ECAP measurements and over a wide range of rates up to 8000 pps using the masked response extraction technique. Rate adaptation did not show a predictable relationship with speech perception and coding strategy channel rate preference. The masked response extraction method offers opportunities to study long-term rate adaptation with well-defined and controlled stimulation paradigms. PMID:24559068

  19. Vocal learning in elephants: neural bases and adaptive context.

    PubMed

    Stoeger, Angela S; Manger, Paul

    2014-10-01

    In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants.

  20. Vocal learning in elephants: neural bases and adaptive context

    PubMed Central

    Stoeger, Angela S; Manger, Paul

    2014-01-01

    In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants. PMID:25062469

  1. Vocal learning in elephants: neural bases and adaptive context.

    PubMed

    Stoeger, Angela S; Manger, Paul

    2014-10-01

    In the last decade clear evidence has accumulated that elephants are capable of vocal production learning. Examples of vocal imitation are documented in African (Loxodonta africana) and Asian (Elephas maximus) elephants, but little is known about the function of vocal learning within the natural communication systems of either species. We are also just starting to identify the neural basis of elephant vocalizations. The African elephant diencephalon and brainstem possess specializations related to aspects of neural information processing in the motor system (affecting the timing and learning of trunk movements) and the auditory and vocalization system. Comparative interdisciplinary (from behavioral to neuroanatomical) studies are strongly warranted to increase our understanding of both vocal learning and vocal behavior in elephants. PMID:25062469

  2. Cold adaptation increases rates of nutrient flow and metabolic plasticity during cold exposure in Drosophila melanogaster.

    PubMed

    Williams, Caroline M; McCue, Marshall D; Sunny, Nishanth E; Szejner-Sigal, Andre; Morgan, Theodore J; Allison, David B; Hahn, Daniel A

    2016-09-14

    Metabolic flexibility is an important component of adaptation to stressful environments, including thermal stress and latitudinal adaptation. A long history of population genetic studies suggest that selection on core metabolic enzymes may shape life histories by altering metabolic flux. However, the direct relationship between selection on thermal stress hardiness and metabolic flux has not previously been tested. We investigated flexibility of nutrient catabolism during cold stress in Drosophila melanogaster artificially selected for fast or slow recovery from chill coma (i.e. cold-hardy or -susceptible), specifically testing the hypothesis that stress adaptation increases metabolic turnover. Using (13)C-labelled glucose, we first showed that cold-hardy flies more rapidly incorporate ingested carbon into amino acids and newly synthesized glucose, permitting rapid synthesis of proline, a compound shown elsewhere to improve survival of cold stress. Second, using glucose and leucine tracers we showed that cold-hardy flies had higher oxidation rates than cold-susceptible flies before cold exposure, similar oxidation rates during cold exposure, and returned to higher oxidation rates during recovery. Additionally, cold-hardy flies transferred compounds among body pools more rapidly during cold exposure and recovery. Increased metabolic turnover may allow cold-adapted flies to better prepare for, resist and repair/tolerate cold damage. This work illustrates for the first time differences in nutrient fluxes associated with cold adaptation, suggesting that metabolic costs associated with cold hardiness could invoke resource-based trade-offs that shape life histories. PMID:27605506

  3. Cold adaptation increases rates of nutrient flow and metabolic plasticity during cold exposure in Drosophila melanogaster.

    PubMed

    Williams, Caroline M; McCue, Marshall D; Sunny, Nishanth E; Szejner-Sigal, Andre; Morgan, Theodore J; Allison, David B; Hahn, Daniel A

    2016-09-14

    Metabolic flexibility is an important component of adaptation to stressful environments, including thermal stress and latitudinal adaptation. A long history of population genetic studies suggest that selection on core metabolic enzymes may shape life histories by altering metabolic flux. However, the direct relationship between selection on thermal stress hardiness and metabolic flux has not previously been tested. We investigated flexibility of nutrient catabolism during cold stress in Drosophila melanogaster artificially selected for fast or slow recovery from chill coma (i.e. cold-hardy or -susceptible), specifically testing the hypothesis that stress adaptation increases metabolic turnover. Using (13)C-labelled glucose, we first showed that cold-hardy flies more rapidly incorporate ingested carbon into amino acids and newly synthesized glucose, permitting rapid synthesis of proline, a compound shown elsewhere to improve survival of cold stress. Second, using glucose and leucine tracers we showed that cold-hardy flies had higher oxidation rates than cold-susceptible flies before cold exposure, similar oxidation rates during cold exposure, and returned to higher oxidation rates during recovery. Additionally, cold-hardy flies transferred compounds among body pools more rapidly during cold exposure and recovery. Increased metabolic turnover may allow cold-adapted flies to better prepare for, resist and repair/tolerate cold damage. This work illustrates for the first time differences in nutrient fluxes associated with cold adaptation, suggesting that metabolic costs associated with cold hardiness could invoke resource-based trade-offs that shape life histories.

  4. Adapted Lethality: What We Can Learn from Guinea Pig-Adapted Ebola Virus Infection Model

    PubMed Central

    Cheresiz, S. V.; Semenova, E. A.; Chepurnov, A. A.

    2016-01-01

    Establishment of small animal models of Ebola virus (EBOV) infection is important both for the study of genetic determinants involved in the complex pathology of EBOV disease and for the preliminary screening of antivirals, production of therapeutic heterologic immunoglobulins, and experimental vaccine development. Since the wild-type EBOV is avirulent in rodents, the adaptation series of passages in these animals are required for the virulence/lethality to emerge in these models. Here, we provide an overview of our several adaptation series in guinea pigs, which resulted in the establishment of guinea pig-adapted EBOV (GPA-EBOV) variants different in their characteristics, while uniformly lethal for the infected animals, and compare the virologic, genetic, pathomorphologic, and immunologic findings with those obtained in the adaptation experiments of the other research groups. PMID:26989413

  5. Adaptive data rate control TDMA systems as a rain attenuation compensation technique

    NASA Technical Reports Server (NTRS)

    Sato, Masaki; Wakana, Hiromitsu; Takahashi, Takashi; Takeuchi, Makoto; Yamamoto, Minoru

    1993-01-01

    Rainfall attenuation has a severe effect on signal strength and impairs communication links for future mobile and personal satellite communications using Ka-band and millimeter wave frequencies. As rain attenuation compensation techniques, several methods such as uplink power control, site diversity, and adaptive control of data rate or forward error correction have been proposed. In this paper, we propose a TDMA system that can compensate rain attenuation by adaptive control of transmission rates. To evaluate the performance of this TDMA terminal, we carried out three types of experiments: experiments using a Japanese CS-3 satellite with Ka-band transponders, in house IF loop-back experiments, and computer simulations. Experimental results show that this TDMA system has advantages over the conventional constant-rate TDMA systems, as resource sharing technique, in both bit error rate and total TDMA burst lengths required for transmitting given information.

  6. Speed-invariant encoding of looming object distance requires power law spike rate adaptation.

    PubMed

    Clarke, Stephen E; Naud, Richard; Longtin, André; Maler, Leonard

    2013-08-13

    Neural representations of a moving object's distance and approach speed are essential for determining appropriate orienting responses, such as those observed in the localization behaviors of the weakly electric fish, Apteronotus leptorhynchus. We demonstrate that a power law form of spike rate adaptation transforms an electroreceptor afferent's response to "looming" object motion, effectively parsing information about distance and approach speed into distinct measures of the firing rate. Neurons with dynamics characterized by fixed time scales are shown to confound estimates of object distance and speed. Conversely, power law adaptation modifies an electroreceptor afferent's response according to the time scales present in the stimulus, generating a rate code for looming object distance that is invariant to speed and acceleration. Consequently, estimates of both object distance and approach speed can be uniquely determined from an electroreceptor afferent's firing rate, a multiplexed neural code operating over the extended time scales associated with behaviorally relevant stimuli.

  7. Comparing the Effects of Unknown-Known Ratios on Word Reading Learning versus Learning Rates

    ERIC Educational Resources Information Center

    Joseph, Laurice M.; Nist, Lindsay M.

    2006-01-01

    An extension of G. L. Cates et al. (2003) investigation was conducted to determine if students' cumulative learning rates would be superior for words read under a traditional drill and practice condition (as they were for spelling in the previous study) than under interspersal conditions of varying ratios of unknown to known words. Participants…

  8. Modeling the time--varying subjective quality of HTTP video streams with rate adaptations.

    PubMed

    Chen, Chao; Choi, Lark Kwon; de Veciana, Gustavo; Caramanis, Constantine; Heath, Robert W; Bovik, Alan C

    2014-05-01

    Newly developed hypertext transfer protocol (HTTP)-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' quality of experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for online TVSQ prediction in HTTP-based streaming.

  9. Modeling the Time—Varying Subjective Quality of HTTP Video Streams With Rate Adaptations

    NASA Astrophysics Data System (ADS)

    Chen, Chao; Choi, Lark Kwon; de Veciana, Gustavo; Caramanis, Constantine; Heath, Robert W.; Bovik, Alan C.

    2014-05-01

    Newly developed HTTP-based video streaming technologies enable flexible rate-adaptation under varying channel conditions. Accurately predicting the users' Quality of Experience (QoE) for rate-adaptive HTTP video streams is thus critical to achieve efficiency. An important aspect of understanding and modeling QoE is predicting the up-to-the-moment subjective quality of a video as it is played, which is difficult due to hysteresis effects and nonlinearities in human behavioral responses. This paper presents a Hammerstein-Wiener model for predicting the time-varying subjective quality (TVSQ) of rate-adaptive videos. To collect data for model parameterization and validation, a database of longer-duration videos with time-varying distortions was built and the TVSQs of the videos were measured in a large-scale subjective study. The proposed method is able to reliably predict the TVSQ of rate adaptive videos. Since the Hammerstein-Wiener model has a very simple structure, the proposed method is suitable for on-line TVSQ prediction in HTTP based streaming.

  10. Testing Theories of Transfer Using Error Rate Learning Curves.

    PubMed

    Koedinger, Kenneth R; Yudelson, Michael V; Pavlik, Philip I

    2016-07-01

    We analyze naturally occurring datasets from student use of educational technologies to explore a long-standing question of the scope of transfer of learning. We contrast a faculty theory of broad transfer with a component theory of more constrained transfer. To test these theories, we develop statistical models of them. These models use latent variables to represent mental functions that are changed while learning to cause a reduction in error rates for new tasks. Strong versions of these models provide a common explanation for the variance in task difficulty and transfer. Weak versions decouple difficulty and transfer explanations by describing task difficulty with parameters for each unique task. We evaluate these models in terms of both their prediction accuracy on held-out data and their power in explaining task difficulty and learning transfer. In comparisons across eight datasets, we find that the component models provide both better predictions and better explanations than the faculty models. Weak model variations tend to improve generalization across students, but hurt generalization across items and make a sacrifice to explanatory power. More generally, the approach could be used to identify malleable components of cognitive functions, such as spatial reasoning or executive functions.

  11. Testing Theories of Transfer Using Error Rate Learning Curves.

    PubMed

    Koedinger, Kenneth R; Yudelson, Michael V; Pavlik, Philip I

    2016-07-01

    We analyze naturally occurring datasets from student use of educational technologies to explore a long-standing question of the scope of transfer of learning. We contrast a faculty theory of broad transfer with a component theory of more constrained transfer. To test these theories, we develop statistical models of them. These models use latent variables to represent mental functions that are changed while learning to cause a reduction in error rates for new tasks. Strong versions of these models provide a common explanation for the variance in task difficulty and transfer. Weak versions decouple difficulty and transfer explanations by describing task difficulty with parameters for each unique task. We evaluate these models in terms of both their prediction accuracy on held-out data and their power in explaining task difficulty and learning transfer. In comparisons across eight datasets, we find that the component models provide both better predictions and better explanations than the faculty models. Weak model variations tend to improve generalization across students, but hurt generalization across items and make a sacrifice to explanatory power. More generally, the approach could be used to identify malleable components of cognitive functions, such as spatial reasoning or executive functions. PMID:27230694

  12. Fast adaptation of the internal model of gravity for manual interceptions: evidence for event-dependent learning.

    PubMed

    Zago, Myrka; Bosco, Gianfranco; Maffei, Vincenzo; Iosa, Marco; Ivanenko, Yuri P; Lacquaniti, Francesco

    2005-02-01

    We studied how subjects learn to deal with two conflicting sensory environments as a function of the probability of each environment and the temporal distance between repeated events. Subjects were asked to intercept a visual target moving downward on a screen with randomized laws of motion. We compared five protocols that differed in the probability of constant speed (0g) targets and accelerated (1g) targets. Probability ranged from 9 to 100%, and the time interval between consecutive repetitions of the same target ranged from about 1 to 20 min. We found that subjects systematically timed their responses consistent with the assumption of gravity effects, for both 1 and 0g trials. With training, subjects rapidly adapted to 0g targets by shifting the time of motor activation. Surprisingly, the adaptation rate was independent of both the probability of 0g targets and their temporal distance. Very few 0g trials sporadically interspersed as catch trials during immersive practice with 1g trials were sufficient for learning and consolidation in long-term memory, as verified by retesting after 24 h. We argue that the memory store for adapted states of the internal gravity model is triggered by individual events and can be sustained for prolonged periods of time separating sporadic repetitions. This form of event-related learning could depend on multiple-stage memory, with exponential rise and decay in the initial stages followed by a sample-and-hold module. PMID:15456796

  13. Profiling Students' Adaptation Styles in Web-based Learning.

    ERIC Educational Resources Information Center

    Lee, Myung-Geun

    2001-01-01

    Discussion of Web-based instruction (WBI) focuses on a study of Korean universities that analyzed learners' adaptation styles and characteristics by retrospectively assessing the perceptions of various aspects of WBI. Considers computer literacy, interaction with instructor and students, difficulty of contents, and learners' perception of academic…

  14. Collaborative Learning with Multi-Touch Technology: Developing Adaptive Expertise

    ERIC Educational Resources Information Center

    Mercier, Emma M.; Higgins, Steven E.

    2013-01-01

    Developing fluency and flexibility in mathematics is a key goal of upper primary schooling, however, while fluency can be developed with practice, designing activities that support the development of flexibility is more difficult. Drawing on concepts of adaptive expertise, we developed a task for a multi-touch classroom, NumberNet, that aimed to…

  15. Children's Ability to Learn Evolutionary Explanations for Biological Adaptation

    ERIC Educational Resources Information Center

    Shtulman, Andrew; Neal, Cara; Lindquist, Gabrielle

    2016-01-01

    Research Findings: Evolution by natural selection is often relegated to the high school curriculum on the assumption that younger students cannot grasp its complexity. We sought to test that assumption by teaching children ages 4-12 (n = 96) a selection-based explanation for biological adaptation and comparing their success to that of adults…

  16. Adaptive learning can result in a failure to profit from good conditions: implications for understanding depression

    PubMed Central

    Trimmer, Pete C.; Higginson, Andrew D.; Fawcett, Tim W.; McNamara, John M.; Houston, Alasdair I.

    2015-01-01

    Background and objectives: Depression is a major medical problem diagnosed in an increasing proportion of people and for which commonly prescribed psychoactive drugs are frequently ineffective. Development of treatment options may be facilitated by an evolutionary perspective; several adaptive reasons for proneness to depression have been proposed. A common feature of many explanations is that depressive behaviour is a way to avoid costly effort where benefits are small and/or unlikely. However, this viewpoint fails to explain why low mood persists when the situation improves. We investigate whether a behavioural rule that is adapted to a stochastically changing world can cause inactivity which appears similar to the effect of depression, in that it persists after the situation has improved. Methodology: We develop an adaptive learning model in which an individual has repeated choices of whether to invest costly effort that may result in a net benefit. Investing effort also provides information about the current conditions and rates of change of the conditions. Results: An individual following the optimal behavioural strategy may sometimes remain inactive when conditions are favourable (i.e. when it would be better to invest effort) when it is poorly informed about the current environmental state. Initially benign conditions can predispose an individual to inactivity after a relatively brief period of negative experiences. Conclusions and implications: Our approach suggests that the antecedent factors causing depressed behaviour could go much further back in an individual s history than is currently appreciated. The insights from our approach have implications for the ongoing debate about best treatment options for patients with depressive symptoms. PMID:25916884

  17. Adaptive memory: animacy effects persist in paired-associate learning.

    PubMed

    VanArsdall, Joshua E; Nairne, James S; Pandeirada, Josefa N S; Cogdill, Mindi

    2015-01-01

    Recent evidence suggests that animate stimuli are remembered better than matched inanimate stimuli. Two experiments tested whether this animacy effect persists in paired-associate learning of foreign words. Experiment 1 randomly paired Swahili words with matched animate and inanimate English words. Participants were told simply to learn the English "translations" for a later test. Replicating earlier findings using free recall, a strong animacy advantage was found in this cued-recall task. Concerned that the effect might be due to enhanced accessibility of the individual responses (e.g., animates represent a more accessible category), Experiment 2 selected animate and inanimate English words from two more constrained categories (four-legged animals and furniture). Once again, an advantage was found for pairs using animate targets. These results argue against organisational accounts of the animacy effect and potentially have implications for foreign language vocabulary learning. PMID:24813366

  18. Adaptive memory: animacy effects persist in paired-associate learning.

    PubMed

    VanArsdall, Joshua E; Nairne, James S; Pandeirada, Josefa N S; Cogdill, Mindi

    2015-01-01

    Recent evidence suggests that animate stimuli are remembered better than matched inanimate stimuli. Two experiments tested whether this animacy effect persists in paired-associate learning of foreign words. Experiment 1 randomly paired Swahili words with matched animate and inanimate English words. Participants were told simply to learn the English "translations" for a later test. Replicating earlier findings using free recall, a strong animacy advantage was found in this cued-recall task. Concerned that the effect might be due to enhanced accessibility of the individual responses (e.g., animates represent a more accessible category), Experiment 2 selected animate and inanimate English words from two more constrained categories (four-legged animals and furniture). Once again, an advantage was found for pairs using animate targets. These results argue against organisational accounts of the animacy effect and potentially have implications for foreign language vocabulary learning.

  19. An application of adaptive learning to malfunction recovery

    NASA Technical Reports Server (NTRS)

    Cruz, R. E.

    1986-01-01

    A self-organizing controller is developed for a simplified two-dimensional aircraft model. The Controller learns how to pilot the aircraft through a navigational mission without exceeding pre-established position and velocity limits. The controller pilots the aircraft by activating one of eight directional actuators at all times. By continually monitoring the aircraft's position and velocity with respect to the mission, the controller progressively modifies its decision rules to improve the aircraft's performance. When the controller has learned how to pilot the aircraft, two actuators fail permanently. Despite this malfunction, the controller regains proficiency at its original task. The experimental results reported show the controller's capabilities for self-organizing control, learning, and malfunction recovery.

  20. Learning and adaptation in the management of waterfowl harvests

    USGS Publications Warehouse

    Johnson, Fred A.

    2011-01-01

    A formal framework for the adaptive management of waterfowl harvests was adopted by the U.S. Fish and Wildlife Service in 1995. The process admits competing models of waterfowl population dynamics and harvest impacts, and relies on model averaging to compute optimal strategies for regulating harvest. Model weights, reflecting the relative ability of the alternative models to predict changes in population size, are used in the model averaging and are updated each year based on a comparison of model predictions and observations of population size. Since its inception the adaptive harvest program has focused principally on mallards (Anas platyrhynchos), which constitute a large portion of the U.S. waterfowl harvest. Four competing models, derived from a combination of two survival and two reproductive hypotheses, were originally assigned equal weights. In the last year of available information (2007), model weights favored the weakly density-dependent reproductive hypothesis over the strongly density-dependent one, and the additive mortality hypothesis over the compensatory one. The change in model weights led to a more conservative harvesting policy than what was in effect in the early years of the program. Adaptive harvest management has been successful in many ways, but nonetheless has exposed the difficulties in defining management objectives, in predicting and regulating harvests, and in coping with the tradeoffs inherent in managing multiple waterfowl stocks exposed to a common harvest. The key challenge now facing managers is whether adaptive harvest management as an institution can be sufficiently adaptive, and whether the knowledge and experience gained from the process can be reflected in higher-level policy decisions.

  1. Qualitative adaptive reward learning with success failure maps: applied to humanoid robot walking.

    PubMed

    Nassour, John; Hugel, Vincent; Ben Ouezdou, Fethi; Cheng, Gordon

    2013-01-01

    In the human brain, rewards are encoded in a flexible and adaptive way after each novel stimulus. Neurons of the orbitofrontal cortex are the key reward structure of the brain. Neurobiological studies show that the anterior cingulate cortex of the brain is primarily responsible for avoiding repeated mistakes. According to vigilance threshold, which denotes the tolerance to risks, we can differentiate between a learning mechanism that takes risks and one that averts risks. The tolerance to risk plays an important role in such a learning mechanism. Results have shown the differences in learning capacity between risk-taking and risk-avert behaviors. These neurological properties provide promising inspirations for robot learning based on rewards. In this paper, we propose a learning mechanism that is able to learn from negative and positive feedback with reward coding adaptively. It is composed of two phases: evaluation and decision making. In the evaluation phase, we use a Kohonen self-organizing map technique to represent success and failure. Decision making is based on an early warning mechanism that enables avoiding repeating past mistakes. The behavior to risk is modulated in order to gain experiences for success and for failure. Success map is learned with adaptive reward that qualifies the learned task in order to optimize the efficiency. Our approach is presented with an implementation on the NAO humanoid robot, controlled by a bioinspired neural controller based on a central pattern generator. The learning system adapts the oscillation frequency and the motor neuron gain in pitch and roll in order to walk on flat and sloped terrain, and to switch between them.

  2. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    PubMed

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

  3. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

    PubMed Central

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J.

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively “hiding” its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

  4. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.

    PubMed

    Afshar, Saeed; George, Libin; Tapson, Jonathan; van Schaik, André; Hamilton, Tara J

    2014-01-01

    This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research. PMID:25505378

  5. A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

    PubMed Central

    Wen, Hui; Xie, Weixin; Pei, Jihong

    2016-01-01

    This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

  6. OAEditor--A Framework for Editing Adaptive Learning Objects

    ERIC Educational Resources Information Center

    Pereira, Joao Carlos Rodrigues; Cabral, Lucidio dos Anjos Formiga; Oiveira, Ronei dos Santos; Bezerra, Lucimar Leandro; de Melo, Nisston Moraes Tavares

    2012-01-01

    Distance Learning supported by the WEB is a reality which is growing fast and, like any technological or empirical innovation, it reveals positive and negative aspects. An important aspect is in relation to the monitoring of the activities done by the students since an accurate online assessment of the knowledge acquired is an open and, therefore,…

  7. Multimodal and Adaptive Learning Management: An Iterative Design

    ERIC Educational Resources Information Center

    Squires, David R.; Orey, Michael A.

    2015-01-01

    The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

  8. Primary Motor Cortex Involvement in Initial Learning during Visuomotor Adaptation

    ERIC Educational Resources Information Center

    Riek, Stephan; Hinder, Mark R.; Carson, Richard G.

    2012-01-01

    Human motor behaviour is continually modified on the basis of errors between desired and actual movement outcomes. It is emerging that the role played by the primary motor cortex (M1) in this process is contingent upon a variety of factors, including the nature of the task being performed, and the stage of learning. Here we used repetitive TMS to…

  9. Adapting to Change: What Motivates Manitoban Schools to Learn

    ERIC Educational Resources Information Center

    Lam, Y. L. Jack

    2004-01-01

    This study assesses the relative importance of environmental, intraorganizational, and contextual factors that explain the process and outcomes of organizational learning in six Manitoba schools. Based on the data provided by 265 teaching staff and their principals, the present findings verified that transformational leadership, supportive school…

  10. Cooperative Learning and Adaptive Instruction in a Mathematics Curriculum.

    ERIC Educational Resources Information Center

    Terwel, Jan; And Others

    1994-01-01

    Maintains that current research suggests that heterogeneous grouping is preferable. Reports on a study of a new mathematics curriculum using 600 students in 6 Dutch schools. Finds that students in heterogeneous classes taught with cooperative-learning techniques achieved more than students in traditional ability-grouped classrooms. (CFR)

  11. Adaptive Animation of Human Motion for E-Learning Applications

    ERIC Educational Resources Information Center

    Li, Frederick W. B.; Lau, Rynson W. H.; Komura, Taku; Wang, Meng; Siu, Becky

    2007-01-01

    Human motion animation has been one of the major research topics in the field of computer graphics for decades. Techniques developed in this area help present human motions in various applications. This is crucial for enhancing the realism as well as promoting the user interest in the applications. To carry this merit to e-learning applications,…

  12. Adaptation Provisioning with Respect to Learning Styles in a Web-Based Educational System: An Experimental Study

    ERIC Educational Resources Information Center

    Popescu, E.

    2010-01-01

    Personalized instruction is seen as a desideratum of today's e-learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style-based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates…

  13. Oscillatory dynamics in an attractor neural network with firing rate adaptation

    NASA Astrophysics Data System (ADS)

    Rathore, S.; Bush, D.; Latham, P.; Burgess, N.

    2013-01-01

    We develop a framework for generating oscillations in ring attractor networks with firing rate adaptation. We show the relationship between the frequency of rotation around the ring of the shifting bump of activity, the adaptation variable and other model parameters using perturbation theory. The analytic solutions are validated against simulations of such networks. Further preliminary findings indicate that the frequency of these networks can be simply controlled using an external stimulus. The mechanism developed here could potentially be used for temporal coding of position through interference of oscillators of different frequencies.

  14. Selection for increased mass-independent maximal metabolic rate suppresses innate but not adaptive immune function

    PubMed Central

    Downs, Cynthia J.; Brown, Jessi L.; Wone, Bernard; Donovan, Edward R.; Hunter, Kenneth; Hayes, Jack P.

    2013-01-01

    Both appropriate metabolic rates and sufficient immune function are essential for survival. Consequently, eco-immunologists have hypothesized that animals may experience trade-offs between metabolic rates and immune function. Previous work has focused on how basal metabolic rate (BMR) may trade-off with immune function, but maximal metabolic rate (MMR), the upper limit to aerobic activity, might also trade-off with immune function. We used mice artificially selected for high mass-independent MMR to test for trade-offs with immune function. We assessed (i) innate immune function by quantifying cytokine production in response to injection with lipopolysaccharide and (ii) adaptive immune function by measuring antibody production in response to injection with keyhole limpet haemocyanin. Selection for high mass-independent MMR suppressed innate immune function, but not adaptive immune function. However, analyses at the individual level also indicate a negative correlation between MMR and adaptive immune function. By contrast BMR did not affect immune function. Evolutionarily, natural selection may favour increasing MMR to enhance aerobic performance and endurance, but the benefits of high MMR may be offset by impaired immune function. This result could be important in understanding the selective factors acting on the evolution of metabolic rates. PMID:23303541

  15. Becoming a Coach in Developmental Adaptive Sailing: A Lifelong Learning Perspective

    PubMed Central

    Duarte, Tiago; Culver, Diane M.

    2014-01-01

    Life-story methodology and innovative methods were used to explore the process of becoming a developmental adaptive sailing coach. Jarvis's (2009) lifelong learning theory framed the thematic analysis. The findings revealed that the coach, Jenny, was exposed from a young age to collaborative environments. Social interactions with others such as mentors, colleagues, and athletes made major contributions to her coaching knowledge. As Jenny was exposed to a mixture of challenges and learning situations, she advanced from recreational para-swimming instructor to developmental adaptive sailing coach. The conclusions inform future research in disability sport coaching, coach education, and applied sport psychology. PMID:25210408

  16. Becoming a Coach in Developmental Adaptive Sailing: A Lifelong Learning Perspective.

    PubMed

    Duarte, Tiago; Culver, Diane M

    2014-10-01

    Life-story methodology and innovative methods were used to explore the process of becoming a developmental adaptive sailing coach. Jarvis's (2009) lifelong learning theory framed the thematic analysis. The findings revealed that the coach, Jenny, was exposed from a young age to collaborative environments. Social interactions with others such as mentors, colleagues, and athletes made major contributions to her coaching knowledge. As Jenny was exposed to a mixture of challenges and learning situations, she advanced from recreational para-swimming instructor to developmental adaptive sailing coach. The conclusions inform future research in disability sport coaching, coach education, and applied sport psychology.

  17. Becoming a Coach in Developmental Adaptive Sailing: A Lifelong Learning Perspective.

    PubMed

    Duarte, Tiago; Culver, Diane M

    2014-10-01

    Life-story methodology and innovative methods were used to explore the process of becoming a developmental adaptive sailing coach. Jarvis's (2009) lifelong learning theory framed the thematic analysis. The findings revealed that the coach, Jenny, was exposed from a young age to collaborative environments. Social interactions with others such as mentors, colleagues, and athletes made major contributions to her coaching knowledge. As Jenny was exposed to a mixture of challenges and learning situations, she advanced from recreational para-swimming instructor to developmental adaptive sailing coach. The conclusions inform future research in disability sport coaching, coach education, and applied sport psychology. PMID:25210408

  18. Particle Swarm Social Model for Group Social Learning in Adaptive Environment

    SciTech Connect

    Cui, Xiaohui; Potok, Thomas E; Treadwell, Jim N; Patton, Robert M; Pullum, Laura L

    2008-01-01

    This report presents a study of integrating particle swarm algorithm, social knowledge adaptation and multi-agent approaches for modeling the social learning of self-organized groups and their collective searching behavior in an adaptive environment. The objective of this research is to apply the particle swarm metaphor as a model of social learning for a dynamic environment. The research provides a platform for understanding and insights into knowledge discovery and strategic search in human self-organized social groups, such as insurgents or online communities.

  19. Modeling the behavioral substrates of associate learning and memory - Adaptive neural models

    NASA Technical Reports Server (NTRS)

    Lee, Chuen-Chien

    1991-01-01

    Three adaptive single-neuron models based on neural analogies of behavior modification episodes are proposed, which attempt to bridge the gap between psychology and neurophysiology. The proposed models capture the predictive nature of Pavlovian conditioning, which is essential to the theory of adaptive/learning systems. The models learn to anticipate the occurrence of a conditioned response before the presence of a reinforcing stimulus when training is complete. Furthermore, each model can find the most nonredundant and earliest predictor of reinforcement. The behavior of the models accounts for several aspects of basic animal learning phenomena in Pavlovian conditioning beyond previous related models. Computer simulations show how well the models fit empirical data from various animal learning paradigms.

  20. Surprise and opportunity for learning in Grand Canyon: the Glen Canyon Dam Adaptive Management Program

    USGS Publications Warehouse

    Melis, Theodore S.; Walters, Carl; Korman, Josh

    2015-01-01

    With a focus on resources of the Colorado River ecosystem below Glen Canyon Dam, the Glen Canyon Dam Adaptive Management Program has included a variety of experimental policy tests, ranging from manipulation of water releases from the dam to removal of non-native fish within Grand Canyon National Park. None of these field-scale experiments has yet produced unambiguous results in terms of management prescriptions. But there has been adaptive learning, mostly from unanticipated or surprising resource responses relative to predictions from ecosystem modeling. Surprise learning opportunities may often be viewed with dismay by some stakeholders who might not be clear about the purpose of science and modeling in adaptive management. However, the experimental results from the Glen Canyon Dam program actually represent scientific successes in terms of revealing new opportunities for developing better river management policies. A new long-term experimental management planning process for Glen Canyon Dam operations, started in 2011 by the U.S. Department of the Interior, provides an opportunity to refocus management objectives, identify and evaluate key uncertainties about the influence of dam releases, and refine monitoring for learning over the next several decades. Adaptive learning since 1995 is critical input to this long-term planning effort. Embracing uncertainty and surprise outcomes revealed by monitoring and ecosystem modeling will likely continue the advancement of resource objectives below the dam, and may also promote efficient learning in other complex programs.

  1. Daytime sleep has no effect on the time course of motor sequence and visuomotor adaptation learning.

    PubMed

    Backhaus, Winifried; Braaß, Hanna; Renné, Thomas; Krüger, Christian; Gerloff, Christian; Hummel, Friedhelm C

    2016-05-01

    Sleep has previously been claimed to be essential for the continued learning processes of declarative information as well as procedural learning. This study was conducted to examine the importance of sleep, especially the effects of midday naps, on motor sequence and visuomotor adaptation learning. Thirty-five (27 females) healthy, young adults aged between 18 and 30years of age participated in the current study. Addressing potential differences in explicit sequence and motor adaptation learning participants were asked to learn both, a nine-element explicit sequence and a motor adaptation task, in a crossover fashion on two consecutive days. Both tasks were performed with their non-dominant left hand. Prior to learning, each participant was randomized to one of three interventions; (1) power nap: 10-20min sleep, (2) long nap: 50-80min sleep or (3) a 45-min wake-condition. Performance of the motor learning task took place prior to and after a midday rest period, as well as after a night of sleep. Both sleep conditions were dominated by Stage N2 sleep with embedded sleep spindles, which have been described to be associated with enhancement of motor performance. Significant performance changes were observed in both tasks across all interventions (sleep and wake) confirming that learning took place. In the present setup, the magnitude of motor learning was not sleep-dependent in young adults - no differences between the intervention groups (short nap, long nap, no nap) could be found. The effect of the following night of sleep was not influenced by the previous midday rest or sleep period. This finding may be related to the selectiveness of the human brain enhancing especially memory being thought of as important in the future. Previous findings on motor learning enhancing effects of sleep, especially of daytime sleep, are challenged.

  2. Adaptively smoothed background seismicity rates in the Intermountain West, United States

    NASA Astrophysics Data System (ADS)

    Moschetti, M. P.

    2013-05-01

    Spatially smoothed seismicity rates are an important seismic source for seismic hazard calculations across much of the Intermountain West (IMW). The U.S. national seismic hazard maps have historically used smoothed seismicity rate models generated with fixed-bandwidth smoothing methods (Frankel, 1996; Petersen et al., 2008); however, recent tests using the California earthquake catalog indicate that adapting the smoothing bandwidth to the local seismicity density (e.g., Helmstetter et al., 2007; Werner et al., 2011) produces improved seismic source models relative to models with fixed smoothing bandwidths (Schorlemmer et al., 2010). To test the ability of adaptively smoothed seismicity models to match epicenter locations from later parts of the IMW earthquake catalog, I generate time-independent maps of smoothed seismicity rates by spatially smoothing the seismicity rates of M4+ earthquake epicenters using fixed-radius and adaptive smoothing methods. I evaluate the 'forecast' smoothed seismicity models generated from the early part of the earthquake catalog by comparing the locations of earthquakes that occur in the later times of the catalog with the forecast seismicity rates. Forecasts are generated from a de-clustered catalog (Gardner and Knopoff, 1974) with completeness levels ranging from M4-6. The forecasts assume that the Gutenberg-Richter relation describes the magnitude-frequency distribution and that the locations of smaller earthquakes (M4+) can identify the locations of future large, and damaging, earthquakes. Spatially smoothed seismicity rate models are generated with isotropic Gaussian and power-law smoothing kernels using fixed and adaptive bandwidths; the adaptive smoothing bandwidths are calculated with the method of Helmstetter et al. (2007). To identify optimal smoothing methods for long-term earthquake rates, I calculate likelihood values for all smoothed seismicity models by using a Poisson distribution for earthquake occurrence and select the

  3. High speed and adaptable error correction for megabit/s rate quantum key distribution

    PubMed Central

    Dixon, A. R.; Sato, H.

    2014-01-01

    Quantum Key Distribution is moving from its theoretical foundation of unconditional security to rapidly approaching real world installations. A significant part of this move is the orders of magnitude increases in the rate at which secure key bits are distributed. However, these advances have mostly been confined to the physical hardware stage of QKD, with software post-processing often being unable to support the high raw bit rates. In a complete implementation this leads to a bottleneck limiting the final secure key rate of the system unnecessarily. Here we report details of equally high rate error correction which is further adaptable to maximise the secure key rate under a range of different operating conditions. The error correction is implemented both in CPU and GPU using a bi-directional LDPC approach and can provide 90–94% of the ideal secure key rate over all fibre distances from 0–80 km. PMID:25450416

  4. ASLForm: an adaptive self learning medical form generating system.

    PubMed

    Zheng, Shuai; Wang, Fusheng; Lu, James J

    2013-01-01

    To facilitate the process of extracting information from narrative medical reports and transforming extracted data into standardized structured forms, we present an interactive, incrementally learning based information extraction system - ASLForm. ASLForm provides users a convenient interface that can be used as a simple data extraction and data entry system. It is unique, however, in its ability to transparently analyze and quickly learn, from users' interactions with a small number of reports, the desired values for the data fields. Additional user feedback (through acceptance decision or edits on the generated values) can incrementally refine the decision model in real-time, which further reduces users' interaction effort thereafter. The system eventually achieves high accuracy on data extraction with minimal effort from users. ASLForm requires no special configuration or training sets, and is not constrained to specific domains, thus it is easy to use and highly portable. Our experiments demonstrate the effectiveness of the system.

  5. An adaptive online learning approach for Support Vector Regression: Online-SVR-FID

    NASA Astrophysics Data System (ADS)

    Liu, Jie; Zio, Enrico

    2016-08-01

    Support Vector Regression (SVR) is a popular supervised data-driven approach for building empirical models from available data. Like all data-driven methods, under non-stationary environmental and operational conditions it needs to be provided with adaptive learning capabilities, which might become computationally burdensome with large datasets cumulating dynamically. In this paper, a cost-efficient online adaptive learning approach is proposed for SVR by combining Feature Vector Selection (FVS) and Incremental and Decremental Learning. The proposed approach adaptively modifies the model only when different pattern drifts are detected according to proposed criteria. Two tolerance parameters are introduced in the approach to control the computational complexity, reduce the influence of the intrinsic noise in the data and avoid the overfitting problem of SVR. Comparisons of the prediction results is made with other online learning approaches e.g. NORMA, SOGA, KRLS, Incremental Learning, on several artificial datasets and a real case study concerning time series prediction based on data recorded on a component of a nuclear power generation system. The performance indicators MSE and MARE computed on the test dataset demonstrate the efficiency of the proposed online learning method.

  6. Reinforcement learning to adaptive control of nonlinear systems.

    PubMed

    Hwang, Kao-Shing; Tan, S W; Tsai, Min-Cheng

    2003-01-01

    Based on the feedback linearization theory, this paper presents how a reinforcement learning scheme that is adopted to construct artificial neural networks (ANNs) can linearize a nonlinear system effectively. The proposed reinforcement linearization learning system (RLLS) consists of two sub-systems: The evaluation predictor (EP) is a long-term policy selector, and the other is a short-term action selector composed of linearizing control (LC) and reinforce predictor (RP) elements. In addition, a reference model plays the role of the environment, which provides the reinforcement signal to the linearizing process. The RLLS thus receives reinforcement signals to accomplish the linearizing behavior to control a nonlinear system such that it can behave similarly to the reference model. Eventually, the RLLS performs identification and linearization concurrently. Simulation results demonstrate that the proposed learning scheme, which is applied to linearizing a pendulum system, provides better control reliability and robustness than conventional ANN schemes. Furthermore, a PI controller is used to control the linearized plant where the affine system behaves like a linear system.

  7. Reorganization of Finger Coordination Patterns During Adaptation to Rotation and Scaling of a Newly Learned Sensorimotor Transformation

    PubMed Central

    Liu, Xiaolin; Mosier, Kristine M.; Mussa-Ivaldi, Ferdinando A.; Casadio, Maura

    2011-01-01

    We examined how people organize redundant kinematic control variables (finger joint configurations) while learning to make goal-directed movements of a virtual object (a cursor) within a low-dimensional task space (a computer screen). Subjects participated in three experiments performed on separate days. Learning progressed rapidly on day 1, resulting in reduced target capture error and increased cursor trajectory linearity. On days 2 and 3, one group of subjects adapted to a rotation of the nominal map, imposed either stepwise or randomly over trials. Another group experienced a scaling distortion. We report two findings. First, adaptation rates and memory-dependent motor command updating depended on distortion type. Stepwise application and removal of the rotation induced a marked increase in finger motion variability but scaling did not, suggesting that the rotation initiated a more exhaustive search through the space of viable finger motions to resolve the target capture task than did scaling. Indeed, subjects formed new coordination patterns in compensating the rotation but relied on patterns established during baseline practice to compensate the scaling. These findings support the idea that the brain compensates direction and extent errors separately and in computationally distinct ways, but are inconsistent with the idea that once a task is learned, command updating is limited to those degrees of freedom contributing to performance (thereby minimizing energetic or similar costs of control). Second, we report that subjects who learned a scaling while moving to just one target generalized more narrowly across directions than those who learned a rotation. This contrasts with results from whole-arm reaching studies, where a learned scaling generalizes more broadly across direction than rotation. Based on inverse- and forward-dynamics analyses of reaching with the arm, we propose the difference in results derives from extensive exposure in reaching with familiar

  8. Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation.

    PubMed

    Xiao, Lei; Zhang, Mingsha; Xing, Dajun; Liang, Pei-Ji; Wu, Si

    2013-10-01

    Neuronal responses to prolonged stimulation attenuate over time. Here, we ask a fundamental question: is adaptation a simple process for the neural system during which sustained input is ignored, or is it actually part of a strategy for the neural system to adjust its encoding properties dynamically? After simultaneously recording the activities of a group of bullfrog's retinal ganglion cells (dimming detectors) in response to sustained dimming stimulation, we applied a combination of information analysis approaches to explore the time-dependent nature of information encoding during the adaptation. We found that at the early stage of the adaptation, the stimulus information was mainly encoded in firing rates, whereas at the late stage of the adaptation, it was more encoded in neural correlations. Such a transition in encoding properties is not a simple consequence of the attenuation of neuronal firing rates, but rather involves an active change in the neural correlation strengths, suggesting that it is a strategy adopted by the neural system for functional purposes. Our results reveal that in encoding a prolonged stimulation, the neural system may utilize concerted, but less active, firings of neurons to encode information.

  9. Proximate causes of adaptive growth rates: growth efficiency variation among latitudinal populations of Rana temporaria.

    PubMed

    Lindgren, B; Laurila, A

    2005-07-01

    In ectothermic organisms, declining season length and lower temperature towards higher latitudes often select for latitudinal variation in growth and development. However, the energetic mechanisms underlying this adaptive variation are largely unknown. We investigated growth, food intake and growth efficiency of Rana temporaria tadpoles from eight populations along a 1500 km latitudinal gradient across Sweden. To gain an insight into the mechanisms of adaptation at organ level, we also examined variation in tadpole gut length. The tadpoles were raised at two temperatures (16 and 20 degrees C) in a laboratory common garden experiment. We found increased growth rate towards higher latitudes, regardless of temperature treatment. This increase in growth was not because of a higher food intake rate, but populations from higher latitudes had higher growth efficiency, i.e. they were more efficient at converting ingested food into body mass. Low temperature reduced growth efficiency most strongly in southern populations. Relative gut length increased with latitude, and tadpoles at low temperature tended to have longer guts. However, variation in gut length was not the sole adaptive explanation for increased growth efficiency as latitude and body length still explained significant amounts of variation in growth efficiency. Hence, additional energetic adaptations are probably involved in growth efficiency variation along the latitudinal gradient.

  10. An SRWNN-based approach on developing a self-learning and self-evolving adaptive control system for motion platforms

    NASA Astrophysics Data System (ADS)

    Onur Ari, Evrim; Kocaoglan, Erol

    2016-02-01

    In this paper, a self-recurrent wavelet neural network (SRWNN)-based indirect adaptive control architecture is modified for performing speed control of a motion platform. The transient behaviour of the original learning algorithm has been improved by modifying the learning rate updates. The contribution of the proposed modification has been verified via both simulations and experiments. Moreover, the performance of the proposed architecture is compared with robust RST designs performed on a similar benchmark system, to show that via adaptive nonlinear control, it is possible to obtain a fast step response without degrading the robustness of a multi-body mechanical system. Finally, the architecture is further improved so as to possess structural learning for populating the SRWNNs automatically, rather than employing static network structures, and simulation results are provided to show the performance of the proposed structural learning algorithm.

  11. Use of Adapted Bicycles on the Learning of Conventional Cycling by Children with Mental Retardation

    ERIC Educational Resources Information Center

    Burt, Tammy L.; Porretta, David L.; Klein, Richard E.

    2007-01-01

    This study investigated the use of adapted bicycles on the acquisition, maintenance, and generalization of conventional cycling by seven children with mild mental retardation. Feedback was used in addition to the adapted bicycles and consisted of pedal rate, head position, and steering participation. A multiple probe design was used. Participants…

  12. Auto-adaptive robot-aided therapy using machine learning techniques.

    PubMed

    Badesa, Francisco J; Morales, Ricardo; Garcia-Aracil, Nicolas; Sabater, J M; Casals, Alicia; Zollo, Loredana

    2014-09-01

    This paper presents an application of a classification method to adaptively and dynamically modify the therapy and real-time displays of a virtual reality system in accordance with the specific state of each patient using his/her physiological reactions. First, a theoretical background about several machine learning techniques for classification is presented. Then, nine machine learning techniques are compared in order to select the best candidate in terms of accuracy. Finally, first experimental results are presented to show that the therapy can be modulated in function of the patient state using machine learning classification techniques.

  13. Autonomous beating rate adaptation in human stem cell-derived cardiomyocytes.

    PubMed

    Eng, George; Lee, Benjamin W; Protas, Lev; Gagliardi, Mark; Brown, Kristy; Kass, Robert S; Keller, Gordon; Robinson, Richard B; Vunjak-Novakovic, Gordana

    2016-01-01

    The therapeutic success of human stem cell-derived cardiomyocytes critically depends on their ability to respond to and integrate with the surrounding electromechanical environment. Currently, the immaturity of human cardiomyocytes derived from stem cells limits their utility for regenerative medicine and biological research. We hypothesize that biomimetic electrical signals regulate the intrinsic beating properties of cardiomyocytes. Here we show that electrical conditioning of human stem cell-derived cardiomyocytes in three-dimensional culture promotes cardiomyocyte maturation, alters their automaticity and enhances connexin expression. Cardiomyocytes adapt their autonomous beating rate to the frequency at which they were stimulated, an effect mediated by the emergence of a rapidly depolarizing cell population, and the expression of hERG. This rate-adaptive behaviour is long lasting and transferable to the surrounding cardiomyocytes. Thus, electrical conditioning may be used to promote cardiomyocyte maturation and establish their automaticity, with implications for cell-based reduction of arrhythmia during heart regeneration. PMID:26785135

  14. Autonomous beating rate adaptation in human stem cell-derived cardiomyocytes

    PubMed Central

    Eng, George; Lee, Benjamin W.; Protas, Lev; Gagliardi, Mark; Brown, Kristy; Kass, Robert S.; Keller, Gordon; Robinson, Richard B.; Vunjak-Novakovic, Gordana

    2016-01-01

    The therapeutic success of human stem cell-derived cardiomyocytes critically depends on their ability to respond to and integrate with the surrounding electromechanical environment. Currently, the immaturity of human cardiomyocytes derived from stem cells limits their utility for regenerative medicine and biological research. We hypothesize that biomimetic electrical signals regulate the intrinsic beating properties of cardiomyocytes. Here we show that electrical conditioning of human stem cell-derived cardiomyocytes in three-dimensional culture promotes cardiomyocyte maturation, alters their automaticity and enhances connexin expression. Cardiomyocytes adapt their autonomous beating rate to the frequency at which they were stimulated, an effect mediated by the emergence of a rapidly depolarizing cell population, and the expression of hERG. This rate-adaptive behaviour is long lasting and transferable to the surrounding cardiomyocytes. Thus, electrical conditioning may be used to promote cardiomyocyte maturation and establish their automaticity, with implications for cell-based reduction of arrhythmia during heart regeneration. PMID:26785135

  15. Group-Work in the Design of Complex Adaptive Learning Strategies

    ERIC Educational Resources Information Center

    Mavroudi, Anna; Hadzilacos, Thanasis

    2013-01-01

    This paper presents a case study where twelve graduate students undertook the demanding role of the adaptive e-course developer and worked collaboratively on an authentic and complex design task in the context of open and distance tertiary education. The students had to work in groups in order to conceptualise and design a learning scenario for…

  16. Using Weblog in Learning English and Encouraging Adaptation among International Students in Perlis

    ERIC Educational Resources Information Center

    Suryani, Ina; Hizwari, Shafiq; Islam, Md. Aminul; Desa, Hazry

    2012-01-01

    This study looks at the correlation of the English learning which is by using weblog and the adaptation for international students at Universiti Malaysia Perlis. The study was conducted on the first batch of International students. There were 37 students from three countries with the majority from China followed by Indonesia and Sudan. The…

  17. Lessons Learned in Designing and Implementing a Computer-Adaptive Test for English

    ERIC Educational Resources Information Center

    Burston, Jack; Neophytou, Maro

    2014-01-01

    This paper describes the lessons learned in designing and implementing a computer-adaptive test (CAT) for English. The early identification of students with weak L2 English proficiency is of critical importance in university settings that have compulsory English language course graduation requirements. The most efficient means of diagnosing the L2…

  18. Use of Adaptive Study Material in Education in E-Learning Environment

    ERIC Educational Resources Information Center

    Kostolányová, Katerina; Šarmanová, Jana

    2014-01-01

    Personalised education is a topical matter today and the impact of ICT on education has been covered extensively. The adaptation of education to various types of student is an issue of a vast number of papers presented at diverse conferences. The topic incorporates the fields of information technologies and eLearning, but in no small part also the…

  19. Adaptation for a Changing Environment: Developing Learning and Teaching with Information and Communication Technologies

    ERIC Educational Resources Information Center

    Kirkwood, Adrian; Price, Linda

    2006-01-01

    This article examines the relationship between the use of information and communication technologies (ICT) and learning and teaching, particularly in distance education contexts. We argue that environmental changes (societal, educational, and technological) make it necessary to adapt systems and practices that are no longer appropriate. The need…

  20. Bridging Scientific Reasoning and Conceptual Change through Adaptive Web-Based Learning

    ERIC Educational Resources Information Center

    She, Hsiao-Ching; Liao, Ya-Wen

    2010-01-01

    This study reports an adaptive digital learning project, Scientific Concept Construction and Reconstruction (SCCR), and examines its effects on 108 8th grade students' scientific reasoning and conceptual change through mixed methods. A one-group pre-, post-, and retention quasi-experimental design was used in the study. All students received tests…

  1. The Adaptation of Chinese International Students to Online Flexible Learning: Two Case Studies

    ERIC Educational Resources Information Center

    Chen, Rainbow Tsai-Hung; Bennett, Sue; Maton, Karl

    2008-01-01

    The cross-cultural experiences of Chinese international students in Western countries have been subject to intensive research, but only a very small number of studies have considered how these students adapt to learning in an online flexible delivery environment. Guided by Berry's acculturation framework (1980, 2005), the investigation discussed…

  2. Adaptation to Altitude as a Vehicle for Experiential Learning of Physiology by University Undergraduates

    ERIC Educational Resources Information Center

    Weigle, David S.; Buben, Amelia; Burke, Caitlin C.; Carroll, Nels D.; Cook, Brett M.; Davis, Benjamin S.; Dubowitz, Gerald; Fisher, Rian E.; Freeman, Timothy C.; Gibbons, Stephen M.; Hansen, Hale A.; Heys, Kimberly A.; Hopkins, Brittany; Jordan, Brittany L.; McElwain, Katherine L.; Powell, Frank L.; Reinhart, Katherine E.; Robbins, Charles D.; Summers, Cameron C.; Walker, Jennifer D.; Weber, Steven S.; Weinheimer, Caroline J.

    2007-01-01

    In this article, an experiential learning activity is described in which 19 university undergraduates made experimental observations on each other to explore physiological adaptations to high altitude. Following 2 wk of didactic sessions and baseline data collection at sea level, the group ascended to a research station at 12,500-ft elevation.…

  3. Psychological and Pedagogical Support for Students' Adaptation to Learning Activity in High Science School

    ERIC Educational Resources Information Center

    Zeleeva, Vera P.; Bykova, Svetlana S.; Varbanova, Silvia

    2016-01-01

    The relevance of the study is due to the importance of psychological and pedagogical support for students in university that would prevent difficulties in learning activities and increase adaptive capacity through the development of relevant personal traits. Therefore, this article is aimed at solving the problem of arranging psychological and…

  4. Adaptation and Analysis of Motivated Strategies for Learning Questionnaire in the Chinese Setting

    ERIC Educational Resources Information Center

    Lee, John Chi-kin; Yin, Hongbiao; Zhang, Zhonghua

    2010-01-01

    This article reports the adaptation and analysis of Pintrich's Motivated Strategies for Learning Questionnaire (MSLQ) in Hong Kong. First, this study examined the psychometric qualities of the existing Chinese version of MSLQ (MSLQ-CV). Based on this examination, this study developed a revised Chinese version of MSLQ (MSLQ-RCV) for junior…

  5. Adaptations for Culturally and Linguistically Diverse Families of English Language Learning Students with Autisim Spectrum Disorders

    ERIC Educational Resources Information Center

    Mitchell, Deborah J.

    2012-01-01

    The purpose of this qualitative, grounded theory study was to describe adaptations for culturally and linguistically diverse families of English language learning students with autism spectrum disorders. Each family's parent was interviewed three separate times to gather information to understand the needs and experiences regarding their…

  6. Methods of Adapting Digital Content for the Learning Process via Mobile Devices

    ERIC Educational Resources Information Center

    Lopez, J. L. Gimenez; Royo, T. Magal; Laborda, Jesus Garcia; Calvo, F. Garde

    2009-01-01

    This article analyses different methods of adapting digital content for its delivery via mobile devices taking into account two aspects which are a fundamental part of the learning process; on the one hand, functionality of the contents, and on the other, the actual controlled navigation requirements that the learner needs in order to acquire high…

  7. Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field

    ERIC Educational Resources Information Center

    Magnisalis, I.; Demetriadis, S.; Karakostas, A.

    2011-01-01

    This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence…

  8. Japanese English Education and Learning: A History of Adapting Foreign Cultures

    ERIC Educational Resources Information Center

    Shimizu, Minoru

    2010-01-01

    This essay is a history that relates the Japanese tradition of accepting and adapting aspects of foreign culture, especially as it applies to the learning of foreign languages. In particular, the essay describes the history of English education in Japan by investigating its developments after the Meiji era. The author addresses the issues from the…

  9. Strategies for Adapting WebQuests for Students with Learning Disabilities

    ERIC Educational Resources Information Center

    Skylar, Ashley A.; Higgins, Kyle; Boone, Randall

    2007-01-01

    WebQuests are gaining popularity as teachers explore using the Internet for guided learning activities. A WebQuest involves students working on a task that is broken down into clearly defined steps. Students often work in groups to actively conduct the research. This article suggests a variety of methods for adapting WebQuests for students with…

  10. Promoting Contextual Vocabulary Learning through an Adaptive Computer-Assisted EFL Reading System

    ERIC Educational Resources Information Center

    Wang, Y.-H.

    2016-01-01

    The study developed an adaptive computer-assisted reading system and investigated its effect on promoting English as a foreign language learner-readers' contextual vocabulary learning performance. Seventy Taiwanese college students were assigned to two reading groups. Participants in the customised reading group read online English texts, each of…

  11. Does Adaptive Scaffolding Facilitate Students' Ability to Regulate their Learning with Hypermedia?

    ERIC Educational Resources Information Center

    Azevedo, Roger; Cromley, Jennifer G.; Seibert, Diane

    2004-01-01

    Is adaptive scaffolding effective in facilitating students' ability to regulate their learning of complex science topics with hypermedia? We examined the role of different scaffolding instructional interventions in facilitating students' shift to more sophisticated mental models as indicated by both performance and process data. Undergraduate…

  12. Tracking Control of Hysteretic Piezoelectric Actuator using Adaptive Rate-Dependent Controller.

    PubMed

    Tan, U-Xuan; Latt, Win Tun; Widjaja, Ferdinan; Shee, Cheng Yap; Riviere, Cameron N; Ang, Wei Tech

    2009-03-16

    With the increasing popularity of actuators involving smart materials like piezoelectric, control of such materials becomes important. The existence of the inherent hysteretic behavior hinders the tracking accuracy of the actuators. To make matters worse, the hysteretic behavior changes with rate. One of the suggested ways is to have a feedforward controller to linearize the relationship between the input and output. Thus, the hysteretic behavior of the actuator must first be modeled by sensing the relationship between the input voltage and output displacement. Unfortunately, the hysteretic behavior is dependent on individual actuator and also environmental conditions like temperature. It is troublesome and costly to model the hysteresis regularly. In addition, the hysteretic behavior of the actuators also changes with age. Most literature model the actuator using a cascade of rate-independent hysteresis operators and a dynamical system. However, the inertial dynamics of the structure is not the only contributing factor. A complete model will be complex. Thus, based on the studies done on the phenomenological hysteretic behavior with rate, this paper proposes an adaptive rate-dependent feedforward controller with Prandtl-Ishlinskii (PI) hysteresis operators for piezoelectric actuators. This adaptive controller is achieved by adapting the coefficients to manipulate the weights of the play operators. Actual experiments are conducted to demonstrate the effectiveness of the adaptive controller. The main contribution of this paper is its ability to perform tracking control of non-periodic motion and is illustrated with the tracking control ability of a couple of different non-periodic waveforms which were created by passing random numbers through a low pass filter with a cutoff frequency of 20Hz.

  13. Efficient QoS provisioning for adaptive multimedia in mobile communication networks by reinforcement learning

    NASA Astrophysics Data System (ADS)

    Yu, Fei; Wong, Vincent W. S.; Leung, Victor C.

    2003-12-01

    The scarcity and large fluctuations of link bandwidth in wireless networks have motivated the development of adaptive multimedia services in mobile communication networks, where it is possible to increase or decrease the bandwidth of individual ongoing flows. This paper studies the issues of quality of service (QoS) provisioning in such systems. In particular, call admission control and bandwidth adaptation are formulated as a constrained Markov decision problem. The rapid growth in the number of states and the difficulty in estimating state transition probabilities in practical systems make it very difficult to employ classical methods to find the optimal policy. We present a novel approach that uses a form of reinforcement learning known as Q-learning to solve QoS provisioning for wireless adaptive multimedia. Q-learning does not require the explicit state transition model to solve the Markov decision problem; therefore more general and realistic assumptions can be applied to the underlying system model for this approach than in previous schemes. Moreover, the proposed scheme can efficiently handle the large state space and action set of the wireless adaptive multimedia QoS provisioning problem. Handoff dropping probability and average allocated bandwidth are considered as QoS constraints in our model and can be guaranteed simultaneously. Simulation results demonstrate the effectiveness of the proposed scheme in adaptive multimedia mobile communication networks.

  14. Taking Aim at the Cognitive Side of Learning in Sensorimotor Adaptation Tasks.

    PubMed

    McDougle, Samuel D; Ivry, Richard B; Taylor, Jordan A

    2016-07-01

    Sensorimotor adaptation tasks have been used to characterize processes responsible for calibrating the mapping between desired outcomes and motor commands. Research has focused on how this form of error-based learning takes place in an implicit and automatic manner. However, recent work has revealed the operation of multiple learning processes, even in this simple form of learning. This review focuses on the contribution of cognitive strategies and heuristics to sensorimotor learning, and how these processes enable humans to rapidly explore and evaluate novel solutions to enable flexible, goal-oriented behavior. This new work points to limitations in current computational models, and how these must be updated to describe the conjoint impact of multiple processes in sensorimotor learning. PMID:27261056

  15. Adaptation to climate change: changes in farmland use and stocking rate in the U.S.

    USGS Publications Warehouse

    Mu, Jianhong E.; McCarl, Bruce A.; Wein, Anne M.

    2013-01-01

    This paper examines possible adaptations to climate change in terms of pasture and crop land use and stocking rate in the United States (U.S.). Using Agricultural Census and climate data in a statistical model, we find that as temperature and precipitation increases agricultural commodity producers respond by reducing crop land and increasing pasture land. In addition, cattle stocking rate decreases as the summer Temperature-humidity Index (THI) increases and summer precipitation decreases. Using the statistical model with climate data from four General Circulation Models (GCMs), we project that land use shifts from cropping to grazing and the stocking rate declines, and these adaptations are more pronounced in the central and the southeast regions of the U.S. Controlling for other farm production variables, crop land decreases by 6 % and pasture land increases by 33 % from the baseline. Correspondingly, the associated economic impact due to adaptation is around -14 and 29 million dollars to crop producers and pasture producers by the end of this century, respectively. The national and regional results have implications for farm programs and subsidy policies.

  16. Neuromorphic adaptive plastic scalable electronics: analog learning systems.

    PubMed

    Srinivasa, Narayan; Cruz-Albrecht, Jose

    2012-01-01

    Decades of research to build programmable intelligent machines have demonstrated limited utility in complex, real-world environments. Comparing their performance with biological systems, these machines are less efficient by a factor of 1 million1 billion in complex, real-world environments. The Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program is a multifaceted Defense Advanced Research Projects Agency (DARPA) project that seeks to break the programmable machine paradigm and define a new path for creating useful, intelligent machines. Since real-world systems exhibit infinite combinatorial complexity, electronic neuromorphic machine technology would be preferable in a host of applications, but useful and practical implementations still do not exist. HRL Laboratories LLC has embarked on addressing these challenges, and, in this article, we provide an overview of our project and progress made thus far.

  17. E-Learning Library with Local Indexing and Adaptive Navigation Support for Web-Based Learning

    ERIC Educational Resources Information Center

    Hasegawa, Shinobu; Kashihara, Akihiro; Toyoda, Jun'ichi

    2003-01-01

    Learning with existing web-based resources has become popular and important, for example, in cases where there are diverse learning resources dealing with the same learning topic. However, many resources do not have a clear description of their characteristics, which makes it difficult for learners to select appropriate resources. This article…

  18. Ontology-Based Adaptive Dynamic e-Learning Map Planning Method for Conceptual Knowledge Learning

    ERIC Educational Resources Information Center

    Chen, Tsung-Yi; Chu, Hui-Chuan; Chen, Yuh-Min; Su, Kuan-Chun

    2016-01-01

    E-learning improves the shareability and reusability of knowledge, and surpasses the constraints of time and space to achieve remote asynchronous learning. Since the depth of learning content often varies, it is thus often difficult to adjust materials based on the individual levels of learners. Therefore, this study develops an ontology-based…

  19. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    PubMed

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs. PMID:26830003

  20. Learning from adaptive neural network output feedback control of a unicycle-type mobile robot.

    PubMed

    Zeng, Wei; Wang, Qinghui; Liu, Fenglin; Wang, Ying

    2016-03-01

    This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.

  1. Design and optimisation of a (FA)Q-learning-based HTTP adaptive streaming client

    NASA Astrophysics Data System (ADS)

    Claeys, Maxim; Latré, Steven; Famaey, Jeroen; Wu, Tingyao; Van Leekwijck, Werner; De Turck, Filip

    2014-01-01

    In recent years, HTTP (Hypertext Transfer Protocol) adaptive streaming (HAS) has become the de facto standard for adaptive video streaming services. A HAS video consists of multiple segments, encoded at multiple quality levels. State-of-the-art HAS clients employ deterministic heuristics to dynamically adapt the requested quality level based on the perceived network conditions. Current HAS client heuristics are, however, hardwired to fit specific network configurations, making them less flexible to fit a vast range of settings. In this article, a (frequency adjusted) Q-learning HAS client is proposed. In contrast to existing heuristics, the proposed HAS client dynamically learns the optimal behaviour corresponding to the current network environment in order to optimise the quality of experience. Furthermore, the client has been optimised both in terms of global performance and convergence speed. Thorough evaluations show that the proposed client can outperform deterministic algorithms by 11-18% in terms of mean opinion score in a wide range of network configurations.

  2. Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

    NASA Technical Reports Server (NTRS)

    Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

    1988-01-01

    Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

  3. Flexible explicit but rigid implicit learning in a visuomotor adaptation task

    PubMed Central

    Bond, Krista M.

    2015-01-01

    There is mounting evidence for the idea that performance in a visuomotor rotation task can be supported by both implicit and explicit forms of learning. The implicit component of learning has been well characterized in previous experiments and is thought to arise from the adaptation of an internal model driven by sensorimotor prediction errors. However, the role of explicit learning is less clear, and previous investigations aimed at characterizing the explicit component have relied on indirect measures such as dual-task manipulations, posttests, and descriptive computational models. To address this problem, we developed a new method for directly assaying explicit learning by having participants verbally report their intended aiming direction on each trial. While our previous research employing this method has demonstrated the possibility of measuring explicit learning over the course of training, it was only tested over a limited scope of manipulations common to visuomotor rotation tasks. In the present study, we sought to better characterize explicit and implicit learning over a wider range of task conditions. We tested how explicit and implicit learning change as a function of the specific visual landmarks used to probe explicit learning, the number of training targets, and the size of the rotation. We found that explicit learning was remarkably flexible, responding appropriately to task demands. In contrast, implicit learning was strikingly rigid, with each task condition producing a similar degree of implicit learning. These results suggest that explicit learning is a fundamental component of motor learning and has been overlooked or conflated in previous visuomotor tasks. PMID:25855690

  4. Obstacle avoidance during human walking: learning rate and cross-modal transfer.

    PubMed

    Erni, T; Dietz, V

    2001-07-01

    1. The aim of this study was to investigate the significance of specific afferent information during motor learning. Blindfolded subjects stepped over an obstacle on a treadmill while different stimuli (acoustic (ACU), somatosensory (SOM) and light flash (LED)) signalled the approaching obstacle. The effect of the above stimuli was then evaluated and compared to full vision (VIS) locomotion. In the non-visual conditions feedback information about the performance was provided by an acoustic signal. 2. Using each of the different stimuli for information the level of subject performance was assessed by noting foot clearance and analysing both leg muscle electromyographic activity and movement trajectories during three successive runs. Each of these runs consisted of 100 steps over the obstacle. 3. The best performance at the onset of the first run was achieved during the VIS condition. When the VIS condition (run 1 + 2) was followed by ACU or SOM information or when the ACU condition (run 1 + 2) was followed by LED, little cross-modal transfer (CMT) occurred, i.e. adaptation in run 3 started again at a low level of performance. In contrast, if adaptation started with ACU stimuli followed by SOM stimuli, almost full CMT occurred. The absolute level of performance achieved after the second or third runs was similar in the VIS and non-VIS conditions. 4. In conclusion, the course of motor learning depends on specific afferent information, and feedforward control has a special influence on the performance only at the onset of the experiment but not on the rate of learning. The fact that little CMT occurs from visual to non-visual stimuli and from ACU to LED suggests that visual afferent input is processed in a different way to non-visual stimuli.

  5. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

    PubMed

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting. PMID:25729347

  6. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

    PubMed

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

  7. Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

    PubMed Central

    Bauer, Robert; Gharabaghi, Alireza

    2015-01-01

    Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting. PMID:25729347

  8. Predicting demographically sustainable rates of adaptation: can great tit breeding time keep pace with climate change?

    PubMed

    Gienapp, Phillip; Lof, Marjolein; Reed, Thomas E; McNamara, John; Verhulst, Simon; Visser, Marcel E

    2013-01-19

    Populations need to adapt to sustained climate change, which requires micro-evolutionary change in the long term. A key question is how the rate of this micro-evolutionary change compares with the rate of environmental change, given that theoretically there is a 'critical rate of environmental change' beyond which increased maladaptation leads to population extinction. Here, we parametrize two closely related models to predict this critical rate using data from a long-term study of great tits (Parus major). We used stochastic dynamic programming to predict changes in optimal breeding time under three different climate scenarios. Using these results we parametrized two theoretical models to predict critical rates. Results from both models agreed qualitatively in that even 'mild' rates of climate change would be close to these critical rates with respect to great tit breeding time, while for scenarios close to the upper limit of IPCC climate projections the calculated critical rates would be clearly exceeded with possible consequences for population persistence. We therefore tentatively conclude that micro-evolution, together with plasticity, would rescue only the population from mild rates of climate change, although the models make many simplifying assumptions that remain to be tested.

  9. Characterizing the Influence of Effective Population Size on the Rate of Adaptation: Gillespie’s Darwin Domain

    PubMed Central

    Jensen, Jeffrey D.; Bachtrog, Doris

    2011-01-01

    Characterizing the role of effective population size in dictating the rate of adaptive evolution remains a major challenge in evolutionary biology. Depending on the underlying distribution of fitness effects of new mutations, populations of different sizes may differ vastly in their rate of adaptation. Here, we collect polymorphism data at over 100 loci for two closely related Drosophila species with different current effective population sizes (Ne), Drosophila miranda and D. pseudoobscura, to evaluate the prevalence of adaptive evolution versus genetic drift in molecular evolution. Utilizing these large and consistently sampled data sets, we obtain greatly improved estimates of the demographic histories of both species. Specifically, although current Ne differs between these species, their ancestral sizes were much more similar. We find that statistical approaches capturing recent adaptive evolution (using patterns of polymorphisms) detect higher rates of adaptive evolution in the larger D. pseudoobscura population. In contrast, methods aimed at detecting selection over longer time periods (i.e., those relying on divergence data) estimate more similar rates of adaptation between the two species. Thus, our results suggest an important role of effective population size in dictating rates of adaptation and highlight how complicated population histories—as is probably the case for most species—can effect rates of adaptation. Additionally, we also show how different methodologies to detect positive selection can reveal information about different timescales of adaptive evolution. PMID:21705473

  10. A Case-Study for Life-Long Learning and Adaptation in Cooperative Robot Teams

    SciTech Connect

    Parker, L.E.

    1999-09-19

    While considerable progress has been made in recent years toward the development of multi-robot teams, much work remains to be done before these teams are used widely in real-world applications. Two particular needs toward this end are the development of mechanisms that enable robot teams to generate cooperative behaviors on their own, and the development of techniques that allow these teams to autonomously adapt their behavior over time as the environment or the robot team changes. This paper proposes the use of the Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) application as a rich domain for studying the issues of multi-robot learning and adaptation. After discussing the need for learning and adaptation in multi-robot teams, this paper describes the CMOMMT application and its relevance to multi-robot learning. We discuss the results of the previously- developed, hand-generated algorithm for CMOMMT and the potential for learning that was discovered from the hand-generated approach. We then describe the early work that has been done (by us and others) to generate multi- robot learning techniques for the CMOMMT application, as well as our ongoing research to develop approaches that give performance as good, or better, than the hand-generated approach. The ultimate goal of this research is to develop techniques for multi-robot learning and adaptation in the CMOMMT application domain that will generalize to cooperative robot applications in other domains, thus making the practical use of multi-robot teams in a wide variety of real-world applications much closer to reality.

  11. Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

    PubMed

    Jordan, Rebecca; Gray, Steven; Sorensen, Amanda; Newman, Greg; Mellor, David; Newman, Greg; Hmelo-Silver, Cindy; LaDeau, Shannon; Biehler, Dawn; Crall, Alycia

    2016-06-01

    Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning.

  12. Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

    PubMed

    Jordan, Rebecca; Gray, Steven; Sorensen, Amanda; Newman, Greg; Mellor, David; Newman, Greg; Hmelo-Silver, Cindy; LaDeau, Shannon; Biehler, Dawn; Crall, Alycia

    2016-06-01

    Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning. PMID:26585836

  13. Does Visuomotor Adaptation Proceed in Stages? An Examination of the Learning Model by Chein and Schneider (2012).

    PubMed

    Simon, Anja; Bock, Otmar

    2015-01-01

    A new 3-stage model based on neuroimaging evidence is proposed by Chein and Schneider (2012). Each stage is associated with different brain regions, and draws on cognitive abilities: the first stage on creativity, the second on selective attention, and the third on automatic processing. The purpose of the present study was to scrutinize the validity of this model for 1 popular learning paradigm, visuomotor adaptation. Participants completed tests for creativity, selective attention and automated processing before attending in a pointing task with adaptation to a 60° rotation of visual feedback. To examine the relationship between cognitive abilities and motor learning at different times of practice, associations between cognitive and adaptation scores were calculated repeatedly throughout adaptation. The authors found no benefit of high creativity for adaptive performance. High levels of selective attention were positively associated with early adaptation, but hardly with late adaptation and de-adaptation. High levels of automated execution were beneficial for late adaptation, but hardly for early and de-adaptation. From this we conclude that Chein and Schneider's first learning stage is difficult to confirm by research on visuomotor adaptation, and that the other 2 learning stages rather relate to workaround strategies than to actual adaptive recalibration. PMID:25826096

  14. Anatomy of Student Models in Adaptive Learning Systems: A Systematic Literature Review of Individual Differences from 2001 to 2013

    ERIC Educational Resources Information Center

    Nakic, Jelena; Granic, Andrina; Glavinic, Vlado

    2015-01-01

    This study brings an evidence-based review of user individual characteristics employed as sources of adaptation in recent adaptive learning systems. Twenty-two user individual characteristics were explored in a systematically designed search procedure, while 17 of them were identified as sources of adaptation in final selection. The content…

  15. Adaptive Grid Based Localized Learning for Multidimensional Data

    ERIC Educational Resources Information Center

    Saini, Sheetal

    2012-01-01

    Rapid advances in data-rich domains of science, technology, and business has amplified the computational challenges of "Big Data" synthesis necessary to slow the widening gap between the rate at which the data is being collected and analyzed for knowledge. This has led to the renewed need for efficient and accurate algorithms, framework,…

  16. Time Course of Reach Adaptation and Proprioceptive Recalibration during Visuomotor Learning

    PubMed Central

    Ruttle, Jennifer E.; Cressman, Erin K.; ’t Hart, Bernard Marius; Henriques, Denise Y. P.

    2016-01-01

    Training to reach with rotated visual feedback results in adaptation of hand movements, which persist when the perturbation is removed (reach aftereffects). Training also leads to changes in felt hand position, which we refer to as proprioceptive recalibration. The rate at which motor and proprioceptive changes develop throughout training is unknown. Here, we aim to determine the timescale of these changes in order to gain insight into the processes that may be involved in motor learning. Following six rotated reach training trials (30° rotation), at three radially located targets, we measured reach aftereffects and perceived hand position (proprioceptive guided reaches). Participants trained with opposing rotations one week apart to determine if the original training led to any retention or interference. Results suggest that both motor and proprioceptive recalibration occurred in as few as six rotated-cursor training trials (7.57° & 3.88° respectively), with no retention or interference present one week after training. Despite the rapid speed of both motor and sensory changes, these shifts do not saturate to the same degree. Thus, different processes may drive these changes and they may not constitute a single implicit process. PMID:27732595

  17. Cold climate specialization: adaptive covariation between metabolic rate and thermoregulation in pregnant vipers.

    PubMed

    Lourdais, Olivier; Guillon, Michaël; Denardo, Dale; Blouin-Demers, Gabriel

    2013-07-01

    We compared thermoregulatory strategies during pregnancy in two congeneric viperid snakes (Vipera berus and Vipera aspis) with parapatric geographic ranges. V. berus is a boreal specialist with the largest known distribution among terrestrial snakes while V. aspis is a south-European species. Despite contrasted climatic affinities, the two species displayed identical thermal preferences (Tset) in a laboratory thermal gradient. Under identical natural conditions, however, V. berus was capable of maintaining Tset for longer periods, especially when the weather was constraining. Consistent with the metabolic cold adaptation hypothesis, V. berus displayed higher standard metabolic rate at all temperatures considered. We used the thermal dependence of metabolic rate to calculate daily metabolic profiles from body temperature under natural conditions. The boreal specialist experienced higher daily metabolic rate and minimized gestation duration chiefly because of differences in the metabolic reaction norms, but also superior thermoregulatory efficiency. Under cold climates, thermal constraints should make precise thermoregulation costly. However, a shift in the metabolic reaction norm may compensate for thermal constraints and modify the cost-benefit balance of thermoregulation. Covariation between metabolic rate and thermoregulation efficiency is likely an important adaptation to cold climates.

  18. Predicting coral bleaching hotspots: the role of regional variability in thermal stress and potential adaptation rates

    NASA Astrophysics Data System (ADS)

    Teneva, Lida; Karnauskas, Mandy; Logan, Cheryl A.; Bianucci, Laura; Currie, Jock C.; Kleypas, Joan A.

    2012-03-01

    Sea surface temperature fields (1870-2100) forced by CO2-induced climate change under the IPCC SRES A1B CO2 scenario, from three World Climate Research Programme Coupled Model Intercomparison Project Phase 3 (WCRP CMIP3) models (CCSM3, CSIRO MK 3.5, and GFDL CM 2.1), were used to examine how coral sensitivity to thermal stress and rates of adaption affect global projections of coral-reef bleaching. The focus of this study was two-fold, to: (1) assess how the impact of Degree-Heating-Month (DHM) thermal stress threshold choice affects potential bleaching predictions and (2) examine the effect of hypothetical adaptation rates of corals to rising temperature. DHM values were estimated using a conventional threshold of 1°C and a variability-based threshold of 2σ above the climatological maximum Coral adaptation rates were simulated as a function of historical 100-year exposure to maximum annual SSTs with a dynamic rather than static climatological maximum based on the previous 100 years, for a given reef cell. Within CCSM3 simulations, the 1°C threshold predicted later onset of mild bleaching every 5 years for the fraction of reef grid cells where 1°C > 2σ of the climatology time series of annual SST maxima (1961-1990). Alternatively, DHM values using both thresholds, with CSIRO MK 3.5 and GFDL CM 2.1 SSTs, did not produce drastically different onset timing for bleaching every 5 years. Across models, DHMs based on 1°C thermal stress threshold show the most threatened reefs by 2100 could be in the Central and Western Equatorial Pacific, whereas use of the variability-based threshold for DHMs yields the Coral Triangle and parts of Micronesia and Melanesia as bleaching hotspots. Simulations that allow corals to adapt to increases in maximum SST drastically reduce the rates of bleaching. These findings highlight the importance of considering the thermal stress threshold in DHM estimates as well as potential adaptation models in future coral bleaching projections.

  19. Spike timing precision changes with spike rate adaptation in the owl's auditory space map

    PubMed Central

    Takahashi, Terry T.

    2015-01-01

    Spike rate adaptation (SRA) is a continuing change of responsiveness to ongoing stimuli, which is ubiquitous across species and levels of sensory systems. Under SRA, auditory responses to constant stimuli change over time, relaxing toward a long-term rate often over multiple timescales. With more variable stimuli, SRA causes the dependence of spike rate on sound pressure level to shift toward the mean level of recent stimulus history. A model based on subtractive adaptation (Benda J, Hennig RM. J Comput Neurosci 24: 113–136, 2008) shows that changes in spike rate and level dependence are mechanistically linked. Space-specific neurons in the barn owl's midbrain, when recorded under ketamine-diazepam anesthesia, showed these classical characteristics of SRA, while at the same time exhibiting changes in spike timing precision. Abrupt level increases of sinusoidally amplitude-modulated (SAM) noise initially led to spiking at higher rates with lower temporal precision. Spike rate and precision relaxed toward their long-term values with a time course similar to SRA, results that were also replicated by the subtractive model. Stimuli whose amplitude modulations (AMs) were not synchronous across carrier frequency evoked spikes in response to stimulus envelopes of a particular shape, characterized by the spectrotemporal receptive field (STRF). Again, abrupt stimulus level changes initially disrupted the temporal precision of spiking, which then relaxed along with SRA. We suggest that shifts in latency associated with stimulus level changes may differ between carrier frequency bands and underlie decreased spike precision. Thus SRA is manifest not simply as a change in spike rate but also as a change in the temporal precision of spiking. PMID:26269555

  20. Investigating the Impact of Formal Reflective Activities on Skill Adaptation in a Work-Related Instrumental Learning Setting

    ERIC Educational Resources Information Center

    Roessger, Kevin M.

    2013-01-01

    In work-related, instrumental learning contexts the role of reflective activities is unclear. Kolb's (1985) experiential learning theory and Mezirow's transformative learning theory (2000) predict skill-adaptation as a possible outcome. This prediction was experimentally explored by manipulating reflective activities and assessing participants'…

  1. Introducing Adaptivity Features to a Regular Learning Management System to Support Creation of Advanced eLessons

    ERIC Educational Resources Information Center

    Komlenov, Zivana; Budimac, Zoran; Ivanovic, Mirjana

    2010-01-01

    In order to improve the learning process for students with different pre-knowledge, personal characteristics and preferred learning styles, a certain degree of adaptability must be introduced to online courses. In learning environments that support such kind of functionalities students can explicitly choose different paths through course contents…

  2. Energy Detection Based Estimation of Channel Occupancy Rate with Adaptive Noise Estimation

    NASA Astrophysics Data System (ADS)

    Lehtomäki, Janne J.; Vuohtoniemi, Risto; Umebayashi, Kenta; Mäkelä, Juha-Pekka

    Recently, there has been growing interest in opportunistically utilizing the 2.4GHz ISM-band. Numerous spectrum occupancy measurements covering the ISM-band have been performed to analyze the spectrum usage. However, in these campaigns the verification of the correctness of the obtained occupancy values for the highly dynamic ISM-band has not been presented. In this paper, we propose and verify channel occupancy rate (COR) estimation utilizing energy detection mechanism with a novel adaptive energy detection threshold setting method. The results are compared with the true reference COR values. Several different types of verification measurements showed that our setup can estimate the COR values of 802.11 traffic well, with negligible overestimation. The results from real-time real-life measurements also confirm that the proposed adaptive threshold setting method enables accurate thresholds even in the situations where multiple interferers are present in the received signal.

  3. Climate and Adaptive Management: What Are We Learning While We're Doing?

    NASA Astrophysics Data System (ADS)

    Pulwarty, R.; Melis, T.; Shurts, J.; Jain, S.

    2005-12-01

    Learning is of strategic importance in the decades-long process of adapting to climatic change and variability and in accumulating lessons from past and current practices. Even when physical effects can be established with fair confidence there usually exist large uncertainties about biological and ecological effects and even greater uncertainties with respect to social consequences. Much work and experience has shown that long-term environmental problems can seldom be dealt with by single discrete actions or policies but respond only to continuing, sustained efforts at learning, supported by steady public attention and visibility. In many cases, the complications of recorded changes in the spatial and temporal distribution of rainfall, temperature soil moisture, runoff, frequency and magnitudes of droughts and floods have not been explicitly included in response planning. The idea of "adaptive management" has been widely advocated as a bridge between science and policy with a specific focus on ecosystems. We discuss this idea in the context of climatic and other uncertainties but ground the discussion in the implementation of actual adaptive management programs. Adaptive management has three key tenets (1) Policies are experiments that should be designed to produce usable lessons; (2) It should operate on scales compatible with natural processes, recognizing social and economic viability within functioning ecosystems; and: (3) Is realized through effective partnerships among private, local, state, tribal and federal interests. In a watershed setting this can mean balancing hydropower production, habitat management, conservation, endangered species recovery, and cultural resources in order to experiment, learn, incorporate learning, and adapt. Each of these carries its sources of uncertainty. The primary focus is on the experience of the Columbia and Colorado River Basins, the longest running explicit efforts at adaptive management. Experience will also be drawn

  4. Generic Service Integration in Adaptive Learning Experiences Using IMS Learning Design

    ERIC Educational Resources Information Center

    de-la-Fuente-Valentin, Luis; Pardo, Abelardo; Kloos, Carlos Delgado

    2011-01-01

    IMS Learning Design is a specification to capture the orchestration taking place in a learning scenario. This paper presents an extension called Generic Service Integration. This paradigm allows a bidirectional communication between the course engine in charge of the orchestration and conventional Web 2.0 tools. This communication allows the…

  5. A Fuzzy Logic-Based Personalized Learning System for Supporting Adaptive English Learning

    ERIC Educational Resources Information Center

    Hsieh, Tung-Cheng; Wang, Tzone-I; Su, Chien-Yuan; Lee, Ming-Che

    2012-01-01

    As a nearly global language, English as a Foreign Language (EFL) programs are essential for people wishing to learn English. Researchers have noted that extensive reading is an effective way to improve a person's command of English. Choosing suitable articles in accordance with a learner's needs, interests and ability using an e-learning system…

  6. Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement

    PubMed Central

    Layher, Georg; Schrodt, Fabian; Butz, Martin V.; Neumann, Heiko

    2014-01-01

    The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations

  7. A fast and efficient adaptive threshold rate control scheme for remote sensing images.

    PubMed

    Chen, Xiao; Xu, Xiaoqing

    2012-01-01

    The JPEG2000 image compression standard is ideal for processing remote sensing images. However, its algorithm is complex and it requires large amounts of memory, making it difficult to adapt to the limited transmission and storage resources necessary for remote sensing images. In the present study, an improved rate control algorithm for remote sensing images is proposed. The required coded blocks are sorted downward according to their numbers of bit planes prior to entropy coding. An adaptive threshold computed from the combination of the minimum number of bit planes, along with the minimum rate-distortion slope and the compression ratio, is used to truncate passes of each code block during Tier-1 encoding. This routine avoids the encoding of all code passes and improves the coding efficiency. The simulation results show that the computational cost and working buffer memory size of the proposed algorithm reach only 18.13 and 7.81%, respectively, of the same parameters in the postcompression rate distortion algorithm, while the peak signal-to-noise ratio across the images remains almost the same. The proposed algorithm not only greatly reduces the code complexity and buffer requirements but also maintains the image quality.

  8. Identifying innovation in laboratory studies of cultural evolution: rates of retention and measures of adaptation.

    PubMed

    Caldwell, Christine A; Cornish, Hannah; Kandler, Anne

    2016-03-19

    In recent years, laboratory studies of cultural evolution have become increasingly prevalent as a means of identifying and understanding the effects of cultural transmission on the form and functionality of transmitted material. The datasets generated by these studies may provide insights into the conditions encouraging, or inhibiting, high rates of innovation, as well as the effect that this has on measures of adaptive cultural change. Here we review recent experimental studies of cultural evolution with a view to elucidating the role of innovation in generating observed trends. We first consider how tasks are presented to participants, and how the corresponding conceptualization of task success is likely to influence the degree of intent underlying any deviations from perfect reproduction. We then consider the measures of interest used by the researchers to track the changes that occur as a result of transmission, and how these are likely to be affected by differing rates of retention. We conclude that considering studies of cultural evolution from the perspective of innovation provides us with valuable insights that help to clarify important differences in research designs, which have implications for the likely effects of variation in retention rates on measures of cultural adaptation.

  9. Interactive effects of age and multi-gene profile on motor learning and sensorimotor adaptation.

    PubMed

    Noohi, Fatemeh; Boyden, Nate B; Kwak, Youngbin; Humfleet, Jennifer; Müller, Martijn L T M; Bohnen, Nicolaas I; Seidler, Rachael D

    2016-04-01

    The interactive association of age and dopaminergic polymorphisms on cognitive function has been studied extensively. However, there is limited research on whether age interacts with the association between genetic polymorphisms and motor learning. We examined a group of young and older adults' performance in three motor tasks: explicit sequence learning, visuomotor adaptation, and grooved pegboard. We assessed whether individuals' motor learning and performance were associated with their age and genotypes. We selected three genetic polymorphisms: Catechol-O-Methyl Transferase (COMT val158met) and Dopamine D2 Receptor (DRD2 G>T), which are involved with dopaminergic regulation, and Brain Derived Neurotrophic Factor (BDNF val66met) that modulates neuroplasticity and has been shown to interact with dopaminergic genes. Although the underlying mechanisms of the function of these three genotypes are different, the high performance alleles of each have been linked to better learning and performance. We created a composite polygene score based on the Number of High Performance Alleles (NHPA) that each individual carried. We found several associations between genetic profile, motor performance, and sensorimotor adaptation. More importantly, we found that this association varies with age, task type, and engagement of implicit versus explicit learning processes. PMID:26926580

  10. Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller

    NASA Astrophysics Data System (ADS)

    Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

    2014-06-01

    Objective. Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. Approach. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Main results. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance—competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. Significance. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

  11. Evolution of cooperation facilitated by reinforcement learning with adaptive aspiration levels.

    PubMed

    Tanabe, Shoma; Masuda, Naoki

    2012-01-21

    Repeated interaction between individuals is the main mechanism for maintaining cooperation in social dilemma situations. Variants of tit-for-tat (repeating the previous action of the opponent) and the win-stay lose-shift strategy are known as strong competitors in iterated social dilemma games. On the other hand, real repeated interaction generally allows plasticity (i.e., learning) of individuals based on the experience of the past. Although plasticity is relevant to various biological phenomena, its role in repeated social dilemma games is relatively unexplored. In particular, if experience-based learning plays a key role in promotion and maintenance of cooperation, learners should evolve in the contest with nonlearners under selection pressure. By modeling players using a simple reinforcement learning model, we numerically show that learning enables the evolution of cooperation. We also show that numerically estimated adaptive dynamics appositely predict the outcome of evolutionary simulations. The analysis of the adaptive dynamics enables us to capture the obtained results as an affirmative example of the Baldwin effect, where learning accelerates the evolution to optimality. PMID:22037063

  12. Evolution of cooperation facilitated by reinforcement learning with adaptive aspiration levels.

    PubMed

    Tanabe, Shoma; Masuda, Naoki

    2012-01-21

    Repeated interaction between individuals is the main mechanism for maintaining cooperation in social dilemma situations. Variants of tit-for-tat (repeating the previous action of the opponent) and the win-stay lose-shift strategy are known as strong competitors in iterated social dilemma games. On the other hand, real repeated interaction generally allows plasticity (i.e., learning) of individuals based on the experience of the past. Although plasticity is relevant to various biological phenomena, its role in repeated social dilemma games is relatively unexplored. In particular, if experience-based learning plays a key role in promotion and maintenance of cooperation, learners should evolve in the contest with nonlearners under selection pressure. By modeling players using a simple reinforcement learning model, we numerically show that learning enables the evolution of cooperation. We also show that numerically estimated adaptive dynamics appositely predict the outcome of evolutionary simulations. The analysis of the adaptive dynamics enables us to capture the obtained results as an affirmative example of the Baldwin effect, where learning accelerates the evolution to optimality.

  13. Perspectives of probabilistic inferences: Reinforcement learning and an adaptive network compared.

    PubMed

    Rieskamp, Jörg

    2006-11-01

    The assumption that people possess a strategy repertoire for inferences has been raised repeatedly. The strategy selection learning theory specifies how people select strategies from this repertoire. The theory assumes that individuals select strategies proportional to their subjective expectations of how well the strategies solve particular problems; such expectations are assumed to be updated by reinforcement learning. The theory is compared with an adaptive network model that assumes people make inferences by integrating information according to a connectionist network. The network's weights are modified by error correction learning. The theories were tested against each other in 2 experimental studies. Study 1 showed that people substantially improved their inferences through feedback, which was appropriately predicted by the strategy selection learning theory. Study 2 examined a dynamic environment in which the strategies' performances changed. In this situation a quick adaptation to the new situation was not observed; rather, individuals got stuck on the strategy they had successfully applied previously. This "inertia effect" was most strongly predicted by the strategy selection learning theory.

  14. Direct adaptive iterative learning control of nonlinear systems using an output-recurrent fuzzy neural network.

    PubMed

    Wang, Ying-Chung; Chien, Chiang-Ju; Teng, Ching-Cheng

    2004-06-01

    In this paper, a direct adaptive iterative learning control (DAILC) based on a new output-recurrent fuzzy neural network (ORFNN) is presented for a class of repeatable nonlinear systems with unknown nonlinearities and variable initial resetting errors. In order to overcome the design difficulty due to initial state errors at the beginning of each iteration, a concept of time-varying boundary layer is employed to construct an error equation. The learning controller is then designed by using the given ORFNN to approximate an optimal equivalent controller. Some auxiliary control components are applied to eliminate approximation error and ensure learning convergence. Since the optimal ORFNN parameters for a best approximation are generally unavailable, an adaptive algorithm with projection mechanism is derived to update all the consequent, premise, and recurrent parameters during iteration processes. Only one network is required to design the ORFNN-based DAILC and the plant nonlinearities, especially the nonlinear input gain, are allowed to be totally unknown. Based on a Lyapunov-like analysis, we show that all adjustable parameters and internal signals remain bounded for all iterations. Furthermore, the norm of state tracking error vector will asymptotically converge to a tunable residual set as iteration goes to infinity. Finally, iterative learning control of two nonlinear systems, inverted pendulum system and Chua's chaotic circuit, are performed to verify the tracking performance of the proposed learning scheme.

  15. Generalization patterns for reach adaptation and proprioceptive recalibration differ after visuomotor learning.

    PubMed

    Cressman, Erin K; Henriques, Denise Y P

    2015-07-01

    Visuomotor learning results in changes in both motor and sensory systems (Cressman EK, Henriques DY. J Neurophysiol 102: 3505-3518, 2009), such that reaches are adapted and sense of felt hand position recalibrated after reaching with altered visual feedback of the hand. Moreover, visuomotor learning has been shown to generalize such that reach adaptation achieved at a trained target location can influence reaches to novel target directions (Krakauer JW, Pine ZM, Ghilardi MF, Ghez C. J Neurosci 20: 8916-8924, 2000). We looked to determine whether proprioceptive recalibration also generalizes to novel locations. Moreover, we looked to establish the relationship between reach adaptation and changes in sense of felt hand position by determining whether proprioceptive recalibration generalizes to novel targets in a similar manner as reach adaptation. On training trials, subjects reached to a single target with aligned or misaligned cursor-hand feedback, in which the cursor was either rotated or scaled in extent relative to hand movement. After reach training, subjects reached to the training target and novel targets (including targets from a second start position) without visual feedback to assess generalization of reach adaptation. Subjects then performed a proprioceptive estimation task, in which they indicated the position of their hand relative to visual reference markers placed at similar locations as the trained and novel reach targets. Results indicated that shifts in hand position generalized across novel locations, independent of reach adaptation. Thus these distinct sensory and motor generalization patterns suggest that reach adaptation and proprioceptive recalibration arise from independent error signals and that changes in one system cannot guide adjustments in the other.

  16. Adaptive data rate SSMA system for personal and mobile satellite communications

    NASA Technical Reports Server (NTRS)

    Ikegami, Tetsushi; Takahashi, Takashi; Arakaki, Yoshiya; Wakana, Hiromitsu

    1995-01-01

    An adaptive data rate SSMA (spread spectrum multiple access) system is proposed for mobile and personal multimedia satellite communications without the aid of system control earth stations. This system has a constant occupied bandwidth and has variable data rates and processing gains to mitigate communication link impairments such as fading, rain attenuation and interference as well as to handle variable data rate on demand. Proof of concept hardware for 6MHz bandwidth transponder is developed, that uses offset-QPSK (quadrature phase shift keying) and MSK (minimum shift keying) for direct sequence spread spectrum modulation and handle data rates of 4k to 64kbps. The RS422 data interface, low rate voice and H.261 video codecs are installed. The receiver is designed with coherent matched filter technique to achieve fast code acquisition, AFC (automatic frequency control) and coherent detection with minimum hardware losses in a single matched filter circuit. This receiver structure facilitates variable data rate on demand during a call. This paper shows the outline of the proposed system and the performance of the prototype equipment.

  17. Automatic network-adaptive ultra-low-bit-rate video coding

    NASA Astrophysics Data System (ADS)

    Chien, Wei-Jung; Lam, Tuyet-Trang; Abousleman, Glen P.; Karam, Lina J.

    2006-05-01

    This paper presents a software-only, real-time video coder/decoder (codec) for use with low-bandwidth channels where the bandwidth is unknown or varies with time. The codec incorporates a modified JPEG2000 core and interframe predictive coding, and can operate with network bandwidths of less than 1 kbits/second. The encoder and decoder establish two virtual connections over a single IP-based communications link. The first connection is UDP/IP guaranteed throughput, which is used to transmit the compressed video stream in real time, while the second is TCP/IP guaranteed delivery, which is used for two-way control and compression parameter updating. The TCP/IP link serves as a virtual feedback channel and enables the decoder to instruct the encoder to throttle back the transmission bit rate in response to the measured packet loss ratio. It also enables either side to initiate on-the-fly parameter updates such as bit rate, frame rate, frame size, and correlation parameter, among others. The codec also incorporates frame-rate throttling whereby the number of frames decoded is adjusted based upon the available processing resources. Thus, the proposed codec is capable of automatically adjusting the transmission bit rate and decoding frame rate to adapt to any network scenario. Video coding results for a variety of network bandwidths and configurations are presented to illustrate the vast capabilities of the proposed video coding system.

  18. Management of an adaptable-bit-rate video service in a MAN environment

    NASA Astrophysics Data System (ADS)

    Marini, Michele; Albanese, Andres

    1991-02-01

    This paper describes an adaptable-bit-rate video service concept experiment and its management in an experimental prototype of a public metropolitan area network (MAN). In the experiment the " service providers" supply their customers with a set of service management primitives to implement customer-defined management applications and provide users with a high level of flexibility in the service definition. The paper describes the architecture for an experimental service management system that includes customer controlled features for dynamic bandwidth allocation group addressing and address screening. 1

  19. Error-Induced Learning as a Resource-Adaptive Process in Young and Elderly Individuals

    NASA Astrophysics Data System (ADS)

    Ferdinand, Nicola K.; Weiten, Anja; Mecklinger, Axel; Kray, Jutta

    Thorndike described in his law of effect [44] that actions followed by positive events are more likely to be repeated in the future, whereas actions that are followed by negative outcomes are less likely to be repeated. This implies that behavior is evaluated in the light of its potential consequences, and non-reward events (i.e., errors) must be detected for reinforcement learning to take place. In short, humans have to monitor their performance in order to detect and correct errors, and this allows them to successfully adapt their behavior to changing environmental demands and acquire new behavior, i.e., to learn.

  20. Adaptive learning via selectionism and Bayesianism, Part I: connection between the two.

    PubMed

    Zhang, Jun

    2009-04-01

    According to the selection-by-consequence characterization of operant learning, individual animals/species increase or decrease their future probability of action choices based on the consequence (i.e., reward or punishment) of the currently selected action (the so-called "Law of Effect"). Under Bayesianism, on the other hand, evidence is evaluated based on likelihood functions so that action probability is modified from a priori to a posteriori according to the Bayes formula. Viewed as hypothesis testing, a selectionist framework attributes evidence exclusively to the selected, focal hypothesis, whereas a Bayesian framework distributes across all hypotheses the support from a piece of evidence. Here, an intimate connection between the two theoretical frameworks is revealed. Specifically, it is proven that when individuals modify their action choices based on the selectionist's Law of Effect, the learning population, on the ensemble level, evolves according to a Bayesian-like dynamics. The learning equation of the linear operator model [Bush, R. R., & Mosteller, F. (1955). Stochastic models for learning, New York: John Wiley and Sons], under ensemble averaging, yields the class of predictive reinforcement learning models (e.g., [Busemeyer, J. R., & Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121, 177-194; Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16, 1936-1947]).

  1. Adaptive learning via selectionism and Bayesianism, Part II: the sequential case.

    PubMed

    Zhang, Jun

    2009-04-01

    Animals increase or decrease their future tendency of emitting an action based on whether performing such action has, in the past, resulted in positive or negative reinforcement. An analysis in the companion paper [Zhang, J. (2009). Adaptive learning via selectionism and Bayesianism. Part I: Connection between the two. Neural Networks, 22(3), 220-228] of such selectionist style of learning reveals a resemblance between its ensemble-level dynamics governing the change of action probability and Bayesian learning where evidence (in this case, reward) is distributively applied to all action alternatives. Here, this equivalence is further explored in solving the temporal credit-assignment problem during the learning of an action sequence ("operant chain"). Naturally emerging are the notion of secondary (conditioned) reinforcement predicting the average reward associated with a stimulus, and the notion of actor-critic architecture involving concurrent learning of both action probability and reward prediction. While both are consistent with solutions provided by contemporary reinforcement learning theory (Sutton & Barto, 1998) for optimizing sequential decision-making under stationary Markov environments, we investigate the effect of action learning on reward prediction when both are carried out concurrently in any on-line scheme.

  2. Adaptive learning via selectionism and Bayesianism, Part I: connection between the two.

    PubMed

    Zhang, Jun

    2009-04-01

    According to the selection-by-consequence characterization of operant learning, individual animals/species increase or decrease their future probability of action choices based on the consequence (i.e., reward or punishment) of the currently selected action (the so-called "Law of Effect"). Under Bayesianism, on the other hand, evidence is evaluated based on likelihood functions so that action probability is modified from a priori to a posteriori according to the Bayes formula. Viewed as hypothesis testing, a selectionist framework attributes evidence exclusively to the selected, focal hypothesis, whereas a Bayesian framework distributes across all hypotheses the support from a piece of evidence. Here, an intimate connection between the two theoretical frameworks is revealed. Specifically, it is proven that when individuals modify their action choices based on the selectionist's Law of Effect, the learning population, on the ensemble level, evolves according to a Bayesian-like dynamics. The learning equation of the linear operator model [Bush, R. R., & Mosteller, F. (1955). Stochastic models for learning, New York: John Wiley and Sons], under ensemble averaging, yields the class of predictive reinforcement learning models (e.g., [Busemeyer, J. R., & Myung, I. J. (1992). An adaptive approach to human decision making: Learning theory, decision theory, and human performance. Journal of Experimental Psychology: General, 121, 177-194; Montague, P. R., Dayan, P., & Sejnowski, T. J. (1996). A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of Neuroscience, 16, 1936-1947]). PMID:19386469

  3. Adaptive learning via selectionism and Bayesianism, Part II: the sequential case.

    PubMed

    Zhang, Jun

    2009-04-01

    Animals increase or decrease their future tendency of emitting an action based on whether performing such action has, in the past, resulted in positive or negative reinforcement. An analysis in the companion paper [Zhang, J. (2009). Adaptive learning via selectionism and Bayesianism. Part I: Connection between the two. Neural Networks, 22(3), 220-228] of such selectionist style of learning reveals a resemblance between its ensemble-level dynamics governing the change of action probability and Bayesian learning where evidence (in this case, reward) is distributively applied to all action alternatives. Here, this equivalence is further explored in solving the temporal credit-assignment problem during the learning of an action sequence ("operant chain"). Naturally emerging are the notion of secondary (conditioned) reinforcement predicting the average reward associated with a stimulus, and the notion of actor-critic architecture involving concurrent learning of both action probability and reward prediction. While both are consistent with solutions provided by contemporary reinforcement learning theory (Sutton & Barto, 1998) for optimizing sequential decision-making under stationary Markov environments, we investigate the effect of action learning on reward prediction when both are carried out concurrently in any on-line scheme. PMID:19395235

  4. Linguistic Adaptation of the Clinical Dementia Rating Scale for a Spanish-Speaking Population

    PubMed Central

    Oquendo-Jiménez, Ilia; Mena, Rafaela; Antoun, Mikhail D.; Wojna, Valerie

    2012-01-01

    Background Alzheimer's disease (AD) is the most common form of dementia worldwide. In Hispanic populations there are few validated tests for the accurate identification and diagnosis of AD. The Clinical Dementia Rating (CDR) scale is an internationally recognized questionnaire used to stage dementia. This study's objective was to develop a linguistic adaptation of the CDR for the Puerto Rican population. Methods The linguistic adaptation consisted of the evaluation of each CDR question (item) and the questionnaire's instructions, for similarities in meaning (semantic equivalence), relevance of content (content equivalence), and appropriateness of the questionnaire's format and measuring technique (technical equivalence). A focus group methodology was used to assess cultural relevance, clarity, and suitability of the measuring technique in the Argentinean version of the CDR for use in a Puerto Rican population. Results A total of 27 semantic equivalence changes were recommended in four categories: higher than 6th grade level of reading, meaning, common use, and word preference. Four content equivalence changes were identified, all focused on improving the applicability of the test questions to the general population's concept of street addresses and common dietary choices. There were no recommendations for changes in the assessment of technical equivalence. Conclusions We developed a linguistically adapted CDR instrument for the Puerto Rican population, preserving the semantic, content, and technical equivalences of the original version. Further studies are needed to validate the CDR instrument with the staging of Alzheimer's disease in the Puerto Rican population. PMID:20496524

  5. Paradoxical results of adaptive false discovery rate procedures in neuroimaging studies.

    PubMed

    Reiss, Philip T; Schwartzman, Armin; Lu, Feihan; Huang, Lei; Proal, Erika

    2012-12-01

    Adaptive false discovery rate (FDR) procedures, which offer greater power than the original FDR procedure of Benjamini and Hochberg, are often applied to statistical maps of the brain. When a large proportion of the null hypotheses are false, as in the case of widespread effects such as cortical thinning throughout much of the brain, adaptive FDR methods can surprisingly reject more null hypotheses than not accounting for multiple testing at all-i.e., using uncorrected p-values. A straightforward mathematical argument is presented to explain why this can occur with the q-value method of Storey and colleagues, and a simulation study shows that it can also occur, to a lesser extent, with a two-stage FDR procedure due to Benjamini and colleagues. We demonstrate the phenomenon with reference to a published data set documenting cortical thinning in attention deficit/hyperactivity disorder. The paper concludes with recommendations for how to proceed when adaptive FDR results of this kind are encountered in practice.

  6. Dynamical consequences of adaptation of the growth rates in a system of three competing populations

    NASA Astrophysics Data System (ADS)

    Dimitrova, Zlatinka I.; Vitanov, Nikolay K.

    2001-09-01

    We investigate the nonlinear dynamics of a system of populations competing for the same limited resource for the case where each of the populations adapts its growth rate to the total number of individuals in all populations. We consider regions of parameter space where chaotic motion of the Shilnikov kind exists and present results for two characteristic values of the growth ratio adaptation factor r*: r* = -0.15 and 5. Negative r* can lead to vanishing of regions of chaotic motion and to a stabilization of a fixed point of the studied model system of differential equations. Positive r* lead to changes of the shape of the bifurcation diagrams in comparison with the bifurcation diagrams for the case without adaptation. For the case r* = 5 we observe transition to chaos by period-doubling bifurcations, windows of periodic motion between the regions of chaotic motion and a region of transient chaos after the last window of periodic motion. The Lyapunov dimension for the chaotic attractors is close to two and the Lyapunov spectrum has a structure which allows a topological analysis of the attractors of the investigated system.

  7. Machine-Learning Based Co-adaptive Calibration: A Perspective to Fight BCI Illiteracy

    NASA Astrophysics Data System (ADS)

    Vidaurre, Carmen; Sannelli, Claudia; Müller, Klaus-Robert; Blankertz, Benjamin

    "BCI illiteracy" is one of the biggest problems and challenges in BCI research. It means that BCI control cannot be achieved by a non-negligible number of subjects (estimated 20% to 25%). There are two main causes for BCI illiteracy in BCI users: either no SMR idle rhythm is observed over motor areas, or this idle rhythm is not attenuated during motor imagery, resulting in a classification performance lower than 70% (criterion level) already for offline calibration data. In a previous work of the same authors, the concept of machine learning based co-adaptive calibration was introduced. This new type of calibration provided substantially improved performance for a variety of users. Here, we use a similar approach and investigate to what extent co-adapting learning enables substantial BCI control for completely novice users and those who suffered from BCI illiteracy before.

  8. Distributed adaptive fuzzy iterative learning control of coordination problems for higher order multi-agent systems

    NASA Astrophysics Data System (ADS)

    Li, Jinsha; Li, Junmin

    2016-07-01

    In this paper, the adaptive fuzzy iterative learning control scheme is proposed for coordination problems of Mth order (M ≥ 2) distributed multi-agent systems. Every follower agent has a higher order integrator with unknown nonlinear dynamics and input disturbance. The dynamics of the leader are a higher order nonlinear systems and only available to a portion of the follower agents. With distributed initial state learning, the unified distributed protocols combined time-domain and iteration-domain adaptive laws guarantee that the follower agents track the leader uniformly on [0, T]. Then, the proposed algorithm extends to achieve the formation control. A numerical example and a multiple robotic system are provided to demonstrate the performance of the proposed approach.

  9. Learning rate and temperament in a high predation risk environment

    USGS Publications Warehouse

    DePasquale, C.; Wagner, Tyler; Archard, G.A.; Ferguson, B.; Braithwaite, V.A.

    2014-01-01

    Living in challenging environments can influence the behavior of animals in a number of ways. For instance, populations of prey fish that experience frequent, nonlethal interactions with predators have a high proportion of individuals that express greater reaction to risk and increased activity and exploration—collectively known as temperament traits. Temperament traits are often correlated, such that individuals that are risk-prone also tend to be active and explore more. Spatial learning, which requires the integration of many sensory cues, has also been shown to vary in fish exposed to different levels of predation threat. Fish from areas of low predation risk learn to solve spatial tasks faster than fish from high predation areas. However, it is not yet known whether simpler forms of learning, such as learning associations between two events, are similarly influenced. Simple forms of associative learning are likely to be affected by temperament because a willingness to approach and explore novel situations could provide animals with a learning advantage. However, it is possible that routine-forming and inflexible traits associated with risk-prone and increased exploratory behavior may act in the opposite way and make risk-prone individuals poorer at learning associations. To investigate this, we measured temperament in Panamanian bishop fish (Brachyrhaphis episcopi) sampled from a site known to contain many predators. The B. episcopi were then tested with an associative learning task. Within this population, fish that explored more were faster at learning a cue that predicted access to food, indicating a link between temperament and basic learning abilities.

  10. Learning rate and temperament in a high predation risk environment.

    PubMed

    DePasquale, C; Wagner, T; Archard, G A; Ferguson, B; Braithwaite, V A

    2014-11-01

    Living in challenging environments can influence the behavior of animals in a number of ways. For instance, populations of prey fish that experience frequent, nonlethal interactions with predators have a high proportion of individuals that express greater reaction to risk and increased activity and exploration-collectively known as temperament traits. Temperament traits are often correlated, such that individuals that are risk-prone also tend to be active and explore more. Spatial learning, which requires the integration of many sensory cues, has also been shown to vary in fish exposed to different levels of predation threat. Fish from areas of low predation risk learn to solve spatial tasks faster than fish from high predation areas. However, it is not yet known whether simpler forms of learning, such as learning associations between two events, are similarly influenced. Simple forms of associative learning are likely to be affected by temperament because a willingness to approach and explore novel situations could provide animals with a learning advantage. However, it is possible that routine-forming and inflexible traits associated with risk-prone and increased exploratory behavior may act in the opposite way and make risk-prone individuals poorer at learning associations. To investigate this, we measured temperament in Panamanian bishop fish (Brachyrhaphis episcopi) sampled from a site known to contain many predators. The B. episcopi were then tested with an associative learning task. Within this population, fish that explored more were faster at learning a cue that predicted access to food, indicating a link between temperament and basic learning abilities.

  11. Performance of the JPEG Estimated Spectrum Adaptive Postfilter (JPEG-ESAP) for Low Bit Rates

    NASA Technical Reports Server (NTRS)

    Linares, Irving (Inventor)

    2016-01-01

    Frequency-based, pixel-adaptive filtering using the JPEG-ESAP algorithm for low bit rate JPEG formatted color images may allow for more compressed images while maintaining equivalent quality at a smaller file size or bitrate. For RGB, an image is decomposed into three color bands--red, green, and blue. The JPEG-ESAP algorithm is then applied to each band (e.g., once for red, once for green, and once for blue) and the output of each application of the algorithm is rebuilt as a single color image. The ESAP algorithm may be repeatedly applied to MPEG-2 video frames to reduce their bit rate by a factor of 2 or 3, while maintaining equivalent video quality, both perceptually, and objectively, as recorded in the computed PSNR values.

  12. Simulation of a QPSK high data rate receiver - Modeling of tracking loops and adaptive equalizer

    NASA Astrophysics Data System (ADS)

    Schiavoni, Maryanne T.; Ray, Robert T.

    The simulation effort supporting the design of the high-data-rate receiver for the second tracking and data relay satellite system ground terminal is detailed. The receiver accepts quadrature-phase-shift-keying (QPSK) signals at data rates from 6 to 300 Mb/s. The modeling of the Costas tracking loop that tracks the phase of the carrier signal and the clock recovery loop that aligns the data timing, both of which include hardlimiting functions that add a degree of nonlinearity to the designs, is discussed. The simulation of the equalizer, which applies a least-mean-squares adaptive algorithm to remove the intersymbol interference and crosstalk from the channel in order to improve the end-to-end link performance, is also addressed. Simulation results are provided.

  13. Optimal joint power-rate adaptation for error resilient video coding

    NASA Astrophysics Data System (ADS)

    Lin, Yuan; Gürses, Eren; Kim, Anna N.; Perkis, Andrew

    2008-01-01

    In recent years digital imaging devices become an integral part of our daily lives due to the advancements in imaging, storage and wireless communication technologies. Power-Rate-Distortion efficiency is the key factor common to all resource constrained portable devices. In addition, especially in real-time wireless multimedia applications, channel adaptive and error resilient source coding techniques should be considered in conjunction with the P-R-D efficiency, since most of the time Automatic Repeat-reQuest (ARQ) and Forward Error Correction (FEC) are either not feasible or costly in terms of bandwidth efficiency delay. In this work, we focus on the scenarios of real-time video communication for resource constrained devices over bandwidth limited and lossy channels, and propose an analytic Power-channel Error-Rate-Distortion (P-E-R-D) model. In particular, probabilities of macroblocks coding modes are intelligently controlled through an optimization process according to their distinct rate-distortion-complexity performance for a given channel error rate. The framework provides theoretical guidelines for the joint analysis of error resilient source coding and resource allocation. Experimental results show that our optimal framework provides consistent rate-distortion performance gain under different power constraints.

  14. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning

    SciTech Connect

    Zhu Xiaofeng; Ge Yaorong; Li Taoran; Thongphiew, Danthai; Yin Fangfang; Wu, Q Jackie

    2011-02-15

    Purpose: To ensure plan quality for adaptive IMRT of the prostate, we developed a quantitative evaluation tool using a machine learning approach. This tool generates dose volume histograms (DVHs) of organs-at-risk (OARs) based on prior plans as a reference, to be compared with the adaptive plan derived from fluence map deformation. Methods: Under the same configuration using seven-field 15 MV photon beams, DVHs of OARs (bladder and rectum) were estimated based on anatomical information of the patient and a model learned from a database of high quality prior plans. In this study, the anatomical information was characterized by the organ volumes and distance-to-target histogram (DTH). The database consists of 198 high quality prostate plans and was validated with 14 cases outside the training pool. Principal component analysis (PCA) was applied to DVHs and DTHs to quantify their salient features. Then, support vector regression (SVR) was implemented to establish the correlation between the features of the DVH and the anatomical information. Results: DVH/DTH curves could be characterized sufficiently just using only two or three truncated principal components, thus, patient anatomical information was quantified with reduced numbers of variables. The evaluation of the model using the test data set demonstrated its accuracy {approx}80% in prediction and effectiveness in improving ART planning quality. Conclusions: An adaptive IMRT plan quality evaluation tool based on machine learning has been developed, which estimates OAR sparing and provides reference in evaluating ART.

  15. Enhancing learning, innovation, adaptation, and sustainability in health care organizations: the ELIAS performance management framework.

    PubMed

    Persaud, D David

    2014-01-01

    The development of sustainable health care organizations that provide high-quality accessible care is a topic of intense interest. This article provides a practical performance management framework that can be utilized to develop sustainable health care organizations. It is a cyclical 5-step process that is premised on accountability, performance management, and learning practices that are the foundation for a continuous process of measurement, disconfirmation, contextualization, implementation, and routinization This results in the enhancement of learning, innovation, adaptation, and sustainability (ELIAS). Important considerations such as recognizing that health care organizations are complex adaptive systems and the presence of a dynamic learning culture are necessary contextual factors that maximize the effectiveness of the proposed framework. Importantly, the ELIAS framework utilizes data that are already being collected by health care organizations for accountability, improvement, evaluation, and strategic purposes. Therefore, the benefit of the framework, when used as outlined, would be to enhance the chances of health care organizations achieving the goals of ongoing adaptation and sustainability, by design, rather than by chance.

  16. L1-norm locally linear representation regularization multi-source adaptation learning.

    PubMed

    Tao, Jianwen; Wen, Shiting; Hu, Wenjun

    2015-09-01

    In most supervised domain adaptation learning (DAL) tasks, one has access only to a small number of labeled examples from target domain. Therefore the success of supervised DAL in this "small sample" regime needs the effective utilization of the large amounts of unlabeled data to extract information that is useful for generalization. Toward this end, we here use the geometric intuition of manifold assumption to extend the established frameworks in existing model-based DAL methods for function learning by incorporating additional information about the target geometric structure of the marginal distribution. We would like to ensure that the solution is smooth with respect to both the ambient space and the target marginal distribution. In doing this, we propose a novel L1-norm locally linear representation regularization multi-source adaptation learning framework which exploits the geometry of the probability distribution, which has two techniques. Firstly, an L1-norm locally linear representation method is presented for robust graph construction by replacing the L2-norm reconstruction measure in LLE with L1-norm one, which is termed as L1-LLR for short. Secondly, considering the robust graph regularization, we replace traditional graph Laplacian regularization with our new L1-LLR graph Laplacian regularization and therefore construct new graph-based semi-supervised learning framework with multi-source adaptation constraint, which is coined as L1-MSAL method. Moreover, to deal with the nonlinear learning problem, we also generalize the L1-MSAL method by mapping the input data points from the input space to a high-dimensional reproducing kernel Hilbert space (RKHS) via a nonlinear mapping. Promising experimental results have been obtained on several real-world datasets such as face, visual video and object.

  17. Conditions and limitations on learning in the adaptive management of mallard harvests

    USGS Publications Warehouse

    Johnson, F.A.; Kendall, W.L.; Dubovsky, J.A.

    2002-01-01

    In 1995, the United States Fish and Wildlife Service adopted a protocol for the adaptive management of waterfowl hunting regulations (AHM) to help reduce uncertainty about the magnitude of sustainable harvests. To date, the AHM process has focused principally on the midcontinent population of mallards (Anas platyrhynchos), whose dynamics are described by 4 alternative models. Collectively, these models express uncertainty (or disagreement) about whether harvest is an additive or a compensatory form of mortality and whether the reproductive process is weakly or strongly density-dependent. Each model is associated with a probability or 'weight,' which describes its relative ability to predict changes in population size. These Bayesian probabilities are updated annually using a comparison of population size predicted under each model with that observed by a monitoring program. The current AHM process is passively adaptive, in the sense that there is no a priori consideration of how harvest decisions might affect discrimination among models. We contrast this approach with an actively adaptive approach, in which harvest decisions are used in part to produce the learning needed to increase long-term management performance. Our investigation suggests that the passive approach is expected to perform nearly as well as an optimal actively adaptive approach, particularly considering the nature of the model set, management objectives and constraints, and current regulatory alternatives. We offer some comments about the nature of the biological hypotheses being tested and describe some of the inherent limitations on learning in the AHM process.

  18. Saccade Adaptation as a Model of Flexible and General Motor Learning

    PubMed Central

    Herman, James P.; Blangero, Annabelle; Madelain, Laurent; Khan, Afsheen; Harwood, Mark R.

    2013-01-01

    The rapid point-to-point movements of the eyes called saccades are the most commonly made movement by humans, yet differ from nearly every other type of motor output in that they are completed too quickly to be adjusted during their execution by visual feedback. Saccadic accuracy remains quite high over a lifetime despite inevitable changes to the physical structures controlling the eyes, indicating that the oculomotor system actively monitors and adjusts motor commands to achieve consistent behavioural production. Indeed, it seems that beyond the ability to compensate for slow, age-related bodily changes, saccades can be modified following traumatic injury or pathology that affects their production, or in response to more short-term systematic alterations to post-saccadic visual feedback in a laboratory setting. These forms of plasticity rely on the visual detection of accuracy errors by a unified set of mechanisms that support the process known as saccade adaptation. Saccade adaptation has been mostly studied as a phenomenon in its own right, outside of motor learning in general. Here, we highlight the commonalities between eye and arm movement adaptation by reviewing the literature across these fields wherever there are compelling overlapping theories or data. Recent exciting findings are challenging previous interpretations of the underlying mechanism of saccade adaptation with the incorporation of concepts including prediction, reinforcement and contextual learning. We review the emerging ideas and evidence with particular emphasis on the important contributions made by Josh Wallman in this sphere over the past 15 years. PMID:23597598

  19. Adaptive iterative learning control for a class of non-linearly parameterised systems with input saturations

    NASA Astrophysics Data System (ADS)

    Zhang, Ruikun; Hou, Zhongsheng; Ji, Honghai; Yin, Chenkun

    2016-04-01

    In this paper, an adaptive iterative learning control scheme is proposed for a class of non-linearly parameterised systems with unknown time-varying parameters and input saturations. By incorporating a saturation function, a new iterative learning control mechanism is presented which includes a feedback term and a parameter updating term. Through the use of parameter separation technique, the non-linear parameters are separated from the non-linear function and then a saturated difference updating law is designed in iteration domain by combining the unknown parametric term of the local Lipschitz continuous function and the unknown time-varying gain into an unknown time-varying function. The analysis of convergence is based on a time-weighted Lyapunov-Krasovskii-like composite energy function which consists of time-weighted input, state and parameter estimation information. The proposed learning control mechanism warrants a L2[0, T] convergence of the tracking error sequence along the iteration axis. Simulation results are provided to illustrate the effectiveness of the adaptive iterative learning control scheme.

  20. EEG-Based Emotion Recognition Using Deep Learning Network with Principal Component Based Covariate Shift Adaptation

    PubMed Central

    Jirayucharoensak, Suwicha; Pan-Ngum, Setha; Israsena, Pasin

    2014-01-01

    Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemented with a stacked autoencoder (SAE) using hierarchical feature learning approach. Input features of the network are power spectral densities of 32-channel EEG signals from 32 subjects. To alleviate overfitting problem, principal component analysis (PCA) is applied to extract the most important components of initial input features. Furthermore, covariate shift adaptation of the principal components is implemented to minimize the nonstationary effect of EEG signals. Experimental results show that the DLN is capable of classifying three different levels of valence and arousal with accuracy of 49.52% and 46.03%, respectively. Principal component based covariate shift adaptation enhances the respective classification accuracy by 5.55% and 6.53%. Moreover, DLN provides better performance compared to SVM and naive Bayes classifiers. PMID:25258728

  1. Learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems.

    PubMed

    Wang, Cong; Wang, Min; Liu, Tengfei; Hill, David J

    2012-10-01

    This paper studies learning from adaptive neural control (ANC) for a class of nonlinear strict-feedback systems with unknown affine terms. To achieve the purpose of learning, a simple input-to-state stability (ISS) modular ANC method is first presented to ensure the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in finite time. Subsequently, it is proven that learning with the proposed stable ISS-modular ANC can be achieved. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using existing methods. To overcome these difficulties, the stable closed-loop system in the control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation condition for the radial basis function neural network (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop system dynamics is achieved in a local region along recurrent orbits of closed-loop signals, and learning is implemented during a closed-loop feedback control process. The learned knowledge is reused to achieve stability and an improved performance, thereby avoiding the tremendous repeated training process of NNs. Simulation studies are given to demonstrate the effectiveness of the proposed method.

  2. Adoption, adaptation, and abandonment: Appropriation of science education professional development learning

    NASA Astrophysics Data System (ADS)

    Longhurst, Max L.

    Understanding factors that impact teacher utilization of learning from professional development is critical in order maximize the educational and financial investment in teacher professional learning. This study used a multicase mixed quantitative and qualitative methodology to investigate the factors that influence teacher adoption, adaption, or abandonment of learning from science teacher professional development. The theoretical framework of activity theory was identified as a useful way to investigate the phenomenon of teacher appropriation of pedagogical practices from professional development. This framework has the capacity to account for a multitude of elements in the context of a learning experience. In this study educational appropriation is understood through a continuum of how an educator acquires and implements both practical and conceptual aspects of learning from professional development within localized context. The variability associated with instructional changes made from professional development drives this inquiry to search for better understandings of the appropriation of pedagogical practices. Purposeful sampling was used to identify two participants from a group of eighth-grade science teachers engaged in professional development designed to investigate how cyber-enabled technologies might enhance instruction and learning in integrated science classrooms. The data from this investigation add to the literature of appropriation of instructional practices by connecting eight factors that influence conceptual and practical tools with the development of ownership of pedagogical practices in the appropriation hierarchy. Recommendations are shared with professional development developers, providers, and participants in anticipation that future science teaching experiences might be informed by findings from this study.

  3. Fast adaptive OFDM-PON over single fiber loopback transmission using dynamic rate adaptation-based algorithm for channel performance improvement

    NASA Astrophysics Data System (ADS)

    Kartiwa, Iwa; Jung, Sang-Min; Hong, Moon-Ki; Han, Sang-Kook

    2014-03-01

    In this paper, we propose a novel fast adaptive approach that was applied to an OFDM-PON 20-km single fiber loopback transmission system to improve channel performance in term of stabilized BER below 2 × 10-3 and higher throughput beyond 10 Gb/s. The upstream transmission is performed through light source-seeded modulation using 1-GHz RSOA at the ONU. Experimental results indicated that the dynamic rate adaptation algorithm based on greedy Levin-Campello could be an effective solution to mitigate channel instability and data rate degradation caused by the Rayleigh back scattering effect and inefficient resource subcarrier allocation.

  4. In Vivo Human Left-to-Right Ventricular Differences in Rate Adaptation Transiently Increase Pro-Arrhythmic Risk following Rate Acceleration

    PubMed Central

    Bueno-Orovio, Alfonso; Hanson, Ben M.; Gill, Jaswinder S.; Taggart, Peter; Rodriguez, Blanca

    2012-01-01

    Left-to-right ventricular (LV/RV) differences in repolarization have been implicated in lethal arrhythmias in animal models. Our goal is to quantify LV/RV differences in action potential duration (APD) and APD rate adaptation and their contribution to arrhythmogenic substrates in the in vivo human heart using combined in vivo and in silico studies. Electrograms were acquired from 10 LV and 10 RV endocardial sites in 15 patients with normal ventricles. APD and APD adaptation were measured during an increase in heart rate. Analysis of in vivo electrograms revealed longer APD in LV than RV (207.8±21.5 vs 196.7±20.1 ms; P<0.05), and slower APD adaptation in LV than RV (time constant τs = 47.0±14.3 vs 35.6±6.5 s; P<0.05). Following rate acceleration, LV/RV APD dispersion experienced an increase of up to 91% in 12 patients, showing a strong correlation (r2 = 0.90) with both initial dispersion and LV/RV difference in slow adaptation. Pro-arrhythmic implications of measured LV/RV functional differences were studied using in silico simulations. Results show that LV/RV APD and APD adaptation heterogeneities promote unidirectional block following rate acceleration, albeit being insufficient for establishment of reentry in normal hearts. However, in the presence of an ischemic region at the LV/RV junction, LV/RV heterogeneity in APD and APD rate adaptation promotes reentrant activity and its degeneration into fibrillatory activity. Our results suggest that LV/RV heterogeneities in APD adaptation cause a transient increase in APD dispersion in the human ventricles following rate acceleration, which promotes unidirectional block and wave-break at the LV/RV junction, and may potentiate the arrhythmogenic substrate, particularly in patients with ischemic heart disease. PMID:23284948

  5. Adapt

    NASA Astrophysics Data System (ADS)

    Bargatze, L. F.

    2015-12-01

    Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted

  6. An adaptive weighted ensemble procedure for efficient computation of free energies and first passage rates.

    PubMed

    Bhatt, Divesh; Bahar, Ivet

    2012-09-14

    We introduce an adaptive weighted-ensemble procedure (aWEP) for efficient and accurate evaluation of first-passage rates between states for two-state systems. The basic idea that distinguishes aWEP from conventional weighted-ensemble (WE) methodology is the division of the configuration space into smaller regions and equilibration of the trajectories within each region upon adaptive partitioning of the regions themselves into small grids. The equilibrated conditional∕transition probabilities between each pair of regions lead to the determination of populations of the regions and the first-passage times between regions, which in turn are combined to evaluate the first passage times for the forward and backward transitions between the two states. The application of the procedure to a non-trivial coarse-grained model of a 70-residue calcium binding domain of calmodulin is shown to efficiently yield information on the equilibrium probabilities of the two states as well as their first passage times. Notably, the new procedure is significantly more efficient than the canonical implementation of the WE procedure, and this improvement becomes even more significant at low temperatures.

  7. An adaptation of the Interpersonal Problem Areas Rating Scale: pilot and interrater agreement study

    PubMed Central

    de Andrade, Ana Claudia Fontes; Frank, Ellen; Neto, Francisco Lotufo; Houck, Patricia R

    2012-01-01

    Objective This article describes the adaptation of a rating scale of interpersonal psychotherapy problem areas to include a fifth problem area appropriate to bipolar disorder and an interrater agreement study in identifying interpersonal problem areas and selecting a primary treatment focus if patients were to engage in treatment. Method Five research interpersonal psychotherapists assessed nine audiotapes of a single interview with five bipolar and four unipolar patients in which the interpersonal inventory and identification of problem areas were undertaken. Results Raters agreed on presence and absence of problem areas in seven tapes. Kappas for identification of problem areas were 1.00 (grief), 0.77 (role dispute), 0.61 (role transition), 0.57 (interpersonal deficits) and 1.00 (loss of healthy self). Kappa for agreement on a primary clinical focus if patients were to engage in interpersonal psychotherapy treatment was 0.64. Conclusions The adaptation of the original scale to include an area pertinent to bipolar disorder proved to be applicable and relevant for use with this population. The results show substantial interrater agreement in identifying problem areas and potential treatment focus. PMID:19142412

  8. Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms.

    PubMed

    Paalasmaa, Joonas; Toivonen, Hannu; Partinen, Markku

    2015-11-01

    We present a method for measuring beat-to-beat heart rate from ballistocardiograms acquired with force sensors. First, a model for the heartbeat shape is adaptively inferred from the signal using hierarchical clustering. Then, beat-to-beat intervals are detected by finding positions where the heartbeat shape best fits the signal. The method was validated with overnight recordings from 46 subjects in varying setups (sleep clinic, home, single bed, double bed, two sensor types). The mean beat-to-beat interval error was 13 ms and on an average 54% of the beat-to-beat intervals were detected. The method is part of a home-use e-health system for an unobtrusive sleep measurement.

  9. Context-Adaptive Arithmetic Coding Scheme for Lossless Bit Rate Reduction of MPEG Surround in USAC

    NASA Astrophysics Data System (ADS)

    Yoon, Sungyong; Pang, Hee-Suk; Sung, Koeng-Mo

    We propose a new coding scheme for lossless bit rate reduction of the MPEG Surround module in unified speech and audio coding (USAC). The proposed scheme is based on context-adaptive arithmetic coding for efficient bit stream composition of spatial parameters. Experiments show that it achieves the significant lossless bit reduction of 9.93% to 12.14% for spatial parameters and 8.64% to 8.96% for the overall MPEG Surround bit streams compared to the original scheme. The proposed scheme, which is not currently included in USAC, can be used for the improved coding efficiency of MPEG Surround in USAC, where the saved bits can be utilized by the other modules in USAC.

  10. Adaptive optics for high data rate satellite to ground laser link

    NASA Astrophysics Data System (ADS)

    Védrenne, N.; Conan, J.-M.; Petit, C.; Michau, V.

    2016-03-01

    To match the increasing need for high data rate between high altitude platforms and ground free space optics links are investigated. Part of the growing interest is motivated by the possibility to reap the benefits of the technological maturity of the fibered components. This requires the injection of the received wave into a single mode fiber. To reduce injection losses on the ground terminal the use of adaptive optics (AO) is investigated. The AO system must work for a wide variety of turbulence conditions: by daytime and nighttime, at potentially very low elevations for LEO satellites, with localizations of optical ground stations that could be unfavorable regarding atmospheric turbulence. Contrary to astronomy where the quantity optimized is the average Strehl ratio, for free space communications statistical and temporal characteristics of the injection losses must be taken into account. The consequences of a partial correction are investigated here by numerical simulation for both GEO and LEO to ground links.

  11. Adaptive Changes in Basal Metabolic Rate in Humans in Different Eco-Geographical Areas.

    PubMed

    Maximov, Arkady L; Belkin, Victor Sh; Kalichman, Leonid; Kobyliansky, Eugene D

    2015-12-01

    Our aim was to establish whether the human basal metabolic rate (BMR) shifts towards the reduction of vital functions as an adaptation response to extreme environmental conditions. Data was collected in arid and Extreme North zones. The arid zone samples included Bedouins living in the Sinai Peninsula in Egypt, Turkmen students, the Pedagogical University of Chardzhou, Turkmenistan born Russians and Russian soldiers. Soldiers were divided into 3 groups according to the length of their tour of duty in the area: 1st group: up to six months, 2nd group: up to 2 years and the 3rd group: 3-5 years. The Extreme North samples comprised Chukchi natives, 1st generation Russian immigrants born in the area and 3 groups of soldiers comparable to the soldiers from Turkmenistan. BMR values of the new recruits had the highest values of total and relative BMR (1769 ± 16 and 28.3 ± 0.6, correspondingly). The total and relative BMR tended to decrease within a longer adaptation period. The BMR values of officers who served >3 years in Turkmenistan were very similar to the Turkmenistan born Russians (1730 ± 14 vs. 1726 ± 18 and 26.5 ± 0.6 vs. 27.3 ± 0.7, correspondingly). Similarly, in Chukotka, the highest relative BMR was found in the new recruits, serving up to 6 months (28.1 ± 0.7) and was significantly (p < 0.05) lower in the Russians serving in Chukotka over 1.5 years (27.1 ± 0.3). The BMR was virtually similar in Russian officers serving > 3 years, compared to the middle-aged Chukchi or Chukotka-born Russians (25.8 ± 0.5 vs. 25.6 ± 0.5 and 25.5 ± 0.6, correspondingly). The BMR parameters demonstrated a stronger association with body weight than with age. In extreme environmental conditions, migrant populations showed a decrease in BMR, thus reducing its vital functions. The BMR reduction effect with the adequate adaptive transformation is likely to be the key strategy for developing programs to facilitate human and animal adaptation to extreme factors. This process is

  12. Self-adaptive trust based ABR protocol for MANETs using Q-learning.

    PubMed

    Kumar, Anitha Vijaya; Jeyapal, Akilandeswari

    2014-01-01

    Mobile ad hoc networks (MANETs) are a collection of mobile nodes with a dynamic topology. MANETs work under scalable conditions for many applications and pose different security challenges. Due to the nomadic nature of nodes, detecting misbehaviour is a complex problem. Nodes also share routing information among the neighbours in order to find the route to the destination. This requires nodes to trust each other. Thus we can state that trust is a key concept in secure routing mechanisms. A number of cryptographic protection techniques based on trust have been proposed. Q-learning is a recently used technique, to achieve adaptive trust in MANETs. In comparison to other machine learning computational intelligence techniques, Q-learning achieves optimal results. Our work focuses on computing a score using Q-learning to weigh the trust of a particular node over associativity based routing (ABR) protocol. Thus secure and stable route is calculated as a weighted average of the trust value of the nodes in the route and associativity ticks ensure the stability of the route. Simulation results show that Q-learning based trust ABR protocol improves packet delivery ratio by 27% and reduces the route selection time by 40% over ABR protocol without trust calculation. PMID:25254243

  13. Self-Adaptive Trust Based ABR Protocol for MANETs Using Q-Learning

    PubMed Central

    Jeyapal, Akilandeswari

    2014-01-01

    Mobile ad hoc networks (MANETs) are a collection of mobile nodes with a dynamic topology. MANETs work under scalable conditions for many applications and pose different security challenges. Due to the nomadic nature of nodes, detecting misbehaviour is a complex problem. Nodes also share routing information among the neighbours in order to find the route to the destination. This requires nodes to trust each other. Thus we can state that trust is a key concept in secure routing mechanisms. A number of cryptographic protection techniques based on trust have been proposed. Q-learning is a recently used technique, to achieve adaptive trust in MANETs. In comparison to other machine learning computational intelligence techniques, Q-learning achieves optimal results. Our work focuses on computing a score using Q-learning to weigh the trust of a particular node over associativity based routing (ABR) protocol. Thus secure and stable route is calculated as a weighted average of the trust value of the nodes in the route and associativity ticks ensure the stability of the route. Simulation results show that Q-learning based trust ABR protocol improves packet delivery ratio by 27% and reduces the route selection time by 40% over ABR protocol without trust calculation. PMID:25254243

  14. Self-adaptive trust based ABR protocol for MANETs using Q-learning.

    PubMed

    Kumar, Anitha Vijaya; Jeyapal, Akilandeswari

    2014-01-01

    Mobile ad hoc networks (MANETs) are a collection of mobile nodes with a dynamic topology. MANETs work under scalable conditions for many applications and pose different security challenges. Due to the nomadic nature of nodes, detecting misbehaviour is a complex problem. Nodes also share routing information among the neighbours in order to find the route to the destination. This requires nodes to trust each other. Thus we can state that trust is a key concept in secure routing mechanisms. A number of cryptographic protection techniques based on trust have been proposed. Q-learning is a recently used technique, to achieve adaptive trust in MANETs. In comparison to other machine learning computational intelligence techniques, Q-learning achieves optimal results. Our work focuses on computing a score using Q-learning to weigh the trust of a particular node over associativity based routing (ABR) protocol. Thus secure and stable route is calculated as a weighted average of the trust value of the nodes in the route and associativity ticks ensure the stability of the route. Simulation results show that Q-learning based trust ABR protocol improves packet delivery ratio by 27% and reduces the route selection time by 40% over ABR protocol without trust calculation.

  15. Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials.

    PubMed

    Qiu, Wei; Chang, Chunqi; Liu, Wenqing; Poon, Paul W F; Hu, Yong; Lam, F K; Hamernik, Roger P; Wei, Gang; Chan, Francis H Y

    2006-02-01

    Tracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing non-linear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of non-linear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the improved performance of our new algorithm. PMID:16485751

  16. SSD-Optimized Workload Placement with Adaptive Learning and Classification in HPC Environments

    SciTech Connect

    Wan, Lipeng; Lu, Zheng; Cao, Qing; Wang, Feiyi; Oral, H Sarp; Settlemyer, Bradley W

    2014-01-01

    In recent years, non-volatile memory devices such as SSD drives have emerged as a viable storage solution due to their increasing capacity and decreasing cost. Due to the unique capability and capacity requirements in large scale HPC (High Performance Computing) storage environment, a hybrid config- uration (SSD and HDD) may represent one of the most available and balanced solutions considering the cost and performance. Under this setting, effective data placement as well as movement with controlled overhead become a pressing challenge. In this paper, we propose an integrated object placement and movement framework and adaptive learning algorithms to address these issues. Specifically, we present a method that shuffle data objects across storage tiers to optimize the data access performance. The method also integrates an adaptive learning algorithm where real- time classification is employed to predict the popularity of data object accesses, so that they can be placed on, or migrate between SSD or HDD drives in the most efficient manner. We discuss preliminary results based on this approach using a simulator we developed to show that the proposed methods can dynamically adapt storage placements and access pattern as workloads evolve to achieve the best system level performance such as throughput.

  17. Adaptive evolution by recombination is not associated with increased mutation rates in Maize streak virus

    PubMed Central

    2012-01-01

    Background Single-stranded (ss) DNA viruses in the family Geminiviridae are proving to be very useful in real-time evolution studies. The high mutation rate of geminiviruses and other ssDNA viruses is somewhat mysterious in that their DNA genomes are replicated in host nuclei by high fidelity host polymerases. Although strand specific mutation biases observed in virus species from the geminivirus genus Mastrevirus indicate that the high mutation rates in viruses in this genus may be due to mutational processes that operate specifically on ssDNA, it is currently unknown whether viruses from other genera display similar strand specific mutation biases. Also, geminivirus genomes frequently recombine with one another and an alternative cause of their high mutation rates could be that the recombination process is either directly mutagenic or produces a selective environment in which the survival of mutants is favoured. To investigate whether there is an association between recombination and increased basal mutation rates or increased degrees of selection favoring the survival of mutations, we compared the mutation dynamics of the MSV-MatA and MSV-VW field isolates of Maize streak virus (MSV; Mastrevirus), with both a laboratory constructed MSV recombinant, and MSV recombinants closely resembling MSV-MatA. To determine whether strand specific mutation biases are a general characteristic of geminivirus evolution we compared mutation spectra arising during these MSV experiments with those arising during similar experiments involving the geminivirus Tomato yellow leaf curl virus (Begomovirus genus). Results Although both the genomic distribution of mutations and the occurrence of various convergent mutations at specific genomic sites indicated that either mutation hotspots or selection for adaptive mutations might elevate observed mutation rates in MSV, we found no association between recombination and mutation rates. Importantly, when comparing the mutation spectra of MSV

  18. Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning.

    PubMed

    Viejo, Guillaume; Khamassi, Mehdi; Brovelli, Andrea; Girard, Benoît

    2015-01-01

    Current learning theory provides a comprehensive description of how humans and other animals learn, and places behavioral flexibility and automaticity at heart of adaptive behaviors. However, the computations supporting the interactions between goal-directed and habitual decision-making systems are still poorly understood. Previous functional magnetic resonance imaging (fMRI) results suggest that the brain hosts complementary computations that may differentially support goal-directed and habitual processes in the form of a dynamical interplay rather than a serial recruitment of strategies. To better elucidate the computations underlying flexible behavior, we develop a dual-system computational model that can predict both performance (i.e., participants' choices) and modulations in reaction times during learning of a stimulus-response association task. The habitual system is modeled with a simple Q-Learning algorithm (QL). For the goal-directed system, we propose a new Bayesian Working Memory (BWM) model that searches for information in the history of previous trials in order to minimize Shannon entropy. We propose a model for QL and BWM coordination such that the expensive memory manipulation is under control of, among others, the level of convergence of the habitual learning. We test the ability of QL or BWM alone to explain human behavior, and compare them with the performance of model combinations, to highlight the need for such combinations to explain behavior. Two of the tested combination models are derived from the literature, and the latter being our new proposal. In conclusion, all subjects were better explained by model combinations, and the majority of them are explained by our new coordination proposal. PMID:26379518

  19. Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning.

    PubMed

    Viejo, Guillaume; Khamassi, Mehdi; Brovelli, Andrea; Girard, Benoît

    2015-01-01

    Current learning theory provides a comprehensive description of how humans and other animals learn, and places behavioral flexibility and automaticity at heart of adaptive behaviors. However, the computations supporting the interactions between goal-directed and habitual decision-making systems are still poorly understood. Previous functional magnetic resonance imaging (fMRI) results suggest that the brain hosts complementary computations that may differentially support goal-directed and habitual processes in the form of a dynamical interplay rather than a serial recruitment of strategies. To better elucidate the computations underlying flexible behavior, we develop a dual-system computational model that can predict both performance (i.e., participants' choices) and modulations in reaction times during learning of a stimulus-response association task. The habitual system is modeled with a simple Q-Learning algorithm (QL). For the goal-directed system, we propose a new Bayesian Working Memory (BWM) model that searches for information in the history of previous trials in order to minimize Shannon entropy. We propose a model for QL and BWM coordination such that the expensive memory manipulation is under control of, among others, the level of convergence of the habitual learning. We test the ability of QL or BWM alone to explain human behavior, and compare them with the performance of model combinations, to highlight the need for such combinations to explain behavior. Two of the tested combination models are derived from the literature, and the latter being our new proposal. In conclusion, all subjects were better explained by model combinations, and the majority of them are explained by our new coordination proposal.

  20. Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning

    PubMed Central

    Viejo, Guillaume; Khamassi, Mehdi; Brovelli, Andrea; Girard, Benoît

    2015-01-01

    Current learning theory provides a comprehensive description of how humans and other animals learn, and places behavioral flexibility and automaticity at heart of adaptive behaviors. However, the computations supporting the interactions between goal-directed and habitual decision-making systems are still poorly understood. Previous functional magnetic resonance imaging (fMRI) results suggest that the brain hosts complementary computations that may differentially support goal-directed and habitual processes in the form of a dynamical interplay rather than a serial recruitment of strategies. To better elucidate the computations underlying flexible behavior, we develop a dual-system computational model that can predict both performance (i.e., participants' choices) and modulations in reaction times during learning of a stimulus–response association task. The habitual system is modeled with a simple Q-Learning algorithm (QL). For the goal-directed system, we propose a new Bayesian Working Memory (BWM) model that searches for information in the history of previous trials in order to minimize Shannon entropy. We propose a model for QL and BWM coordination such that the expensive memory manipulation is under control of, among others, the level of convergence of the habitual learning. We test the ability of QL or BWM alone to explain human behavior, and compare them with the performance of model combinations, to highlight the need for such combinations to explain behavior. Two of the tested combination models are derived from the literature, and the latter being our new proposal. In conclusion, all subjects were better explained by model combinations, and the majority of them are explained by our new coordination proposal. PMID:26379518

  1. Inquiry in Action for Leadership in Turbulent Times: Exploring the Connections Between Transformative Learning and Adaptive Leadership

    ERIC Educational Resources Information Center

    Nicolaides, Aliki; McCallum, David C.

    2013-01-01

    This article discusses the theory and practices associated with a methodology for leadership capacity development that utilizes Collaborative Developmental Action Inquiry to support adults in understanding the connections between transformative learning and adaptive leadership. Discussion is focused on transformative learning, ways of knowing, or…

  2. Developing the Learning Physical Science Curriculum: Adapting a Small Enrollment, Laboratory and Discussion Based Physical Science Course for Large Enrollments

    ERIC Educational Resources Information Center

    Goldberg, Fred; Price, Edward; Robinson, Stephen; Boyd-Harlow, Danielle; McKean, Michael

    2012-01-01

    We report on the adaptation of the small enrollment, lab and discussion based physical science course, "Physical Science and Everyday Thinking" (PSET), for a large-enrollment, lecture-style setting. Like PSET, the new "Learning Physical Science" (LEPS) curriculum was designed around specific principles based on research on learning to meet the…

  3. The Link between Age, Career Goals, and Adaptive Development for Work-Related Learning among Local Government Employees

    ERIC Educational Resources Information Center

    Tones, Megan; Pillay, Hitendra; Kelly, Kathy

    2011-01-01

    More recently, lifespan development psychology models of adaptive development have been applied to the workforce to investigate ageing worker and lifespan issues. The current study uses the Learning and Development Survey (LDS) to investigate employee selection and engagement of learning and development goals and opportunities and constraints for…

  4. Adaptability and Replicability of Web-Facilitated, Hybrid, and Online Learning in an Undergraduate Exercise Psychology Course

    ERIC Educational Resources Information Center

    Xin, Huaibo; Kempland, Monica; Blankson, Faustina H.

    2015-01-01

    The study aims to examine the effectiveness of web-facilitated, hybrid, and online learning modalities among undergraduate students in a public institution so as to determine the adaptability and replicability of these three learning modalities. This is a quasi-experimental study. A total of 103 undergraduate exercise science majors participated…

  5. Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time

    ERIC Educational Resources Information Center

    Matthews, Kevin; Janicki, Thomas; He, Ling; Patterson, Laurie

    2012-01-01

    This research focuses on the development and implementation of an adaptive learning and grading system with a goal to increase the effectiveness and quality of feedback to students. By utilizing various concepts from established learning theories, the goal of this research is to improve the quantity, quality, and speed of feedback as it pertains…

  6. Adaptive decision systems with extended learning for deployment in partially exposed environments

    NASA Astrophysics Data System (ADS)

    Dasarathy, Belur V.

    1995-05-01

    The design and development of decision systems capable of adaptively learning in the operational environment is presented. Innovative adaptive learning concepts and methodologies are offered that are designed for enhancing the performance of decision systems, such as automatic target recognition systems, wherein robustness of performance is a significant issue. The fundamental concept underlying this design is that of learning in partially exposed environments, wherein, at the start, the system is not necessarily aware of all the pattern classes that may be encountered in the future phase of operations. The decision system is based on a variant to the widely popular nearest-neighbor concept. Several stages of sophistication of the system design are presented. The potential problem of increase in computational loads is addressed in detail by exploring the benefits of employing the recently proposed concept of minimal consistent set. The effectiveness of the system design is experimentally illustrated using two data sets, the now classical IRIS data and some real-world TV image data.

  7. Adaptive critic learning techniques for engine torque and air-fuel ratio control.

    PubMed

    Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

    2008-08-01

    A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

  8. Assessing Faculty Bias in Rating Embedded Assurance of Learning Assignments

    ERIC Educational Resources Information Center

    Kim, Dong-gook; Helms, Marilyn M.

    2016-01-01

    Assurance of learning (AoL) processes for continuous improvement and accreditation require business schools to assess program goals. Findings from the process can lead to changes in course design or curriculum. Often AoL assignments are embedded into existing courses and assessed at regular intervals. Faculty members may evaluate an assignment in…

  9. Social--Population Growth Rate Learning Module. Development Education Program.

    ERIC Educational Resources Information Center

    World Bank, Washington, DC.

    This learning module has two main goals: (1) to increase students' knowledge and understanding of the often complex relationship between sustainable development and the social, economic, and environmental conditions in a country; and (2) to strengthen students' abilities to perform statistical calculations, make and interpret maps, charts, and…

  10. A neural observer with time-varying learning rate: analysis and applications.

    PubMed

    Gurubel, K J; Alanis, A Y; Sanchez, E N; Carlos-Hernandez, S

    2014-02-01

    In this paper, a reduced order neural observer (RONO) with a time-varying learning rate is proposed. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. A time-varying learning rate is designed in order to improve the learning of the neuronal network in presence of disturbances and parameter variations. This work includes the stability proof of the time-varying learning. The applicability of the developed observer is illustrated via simulations for a nonlinear anaerobic digestion process.

  11. Increased episomal replication accounts for the high rate of adaptive mutation in recD mutants of Escherichia coli.

    PubMed Central

    Foster, P L; Rosche, W A

    1999-01-01

    Adaptive mutation has been studied extensively in FC40, a strain of Escherichia coli that cannot metabolize lactose (Lac-) because of a frameshift mutation affecting the lacZ gene on its episome. recD mutants of FC40, in which the exonuclease activity of RecBCD (ExoV) is abolished but its helicase activity is retained, have an increased rate of adaptive mutation. The results presented here show that, in several respects, adaptive mutation to Lac+ involves different mechanisms in recD mutant cells than in wild-type cells. About half of the apparent increase in the adaptive mutation rate of recD mutant cells is due to a RecA-dependent increase in episomal copy number and to growth of the Lac- cells on the lactose plates. The remaining increase appears to be due to continued replication of the episome, with the extra copies being degraded or passed to recD+ recipients. In addition, the increase in adaptive mutation rate in recD mutant cells is (i) dependent on activities of the single-stranded exonucleases, RecJ and ExoI, which are not required for (in fact, slightly inhibit) adaptive mutation in wild-type cells, and (ii) enhanced by RecG, which opposes adaptive mutation in wild-type cells. PMID:10224241

  12. Efficient Authoring of SCORM Courseware Adapted to User Learning Style: The Case of ProPer SAT

    NASA Astrophysics Data System (ADS)

    Kazanidis, Ioannis; Satratzemi, Maya

    Online courses are the most popular way to deliver knowledge for distance learning. New researches attempt to personalize the educational process with the use of the Adaptive Educational Hypermedia Systems. Moreover, due to the significant amount of time, money and effort devoted to creating online courses, developers strive to incorporate standards, such as SCORM, for the reusability, interoperability and durability of the educational content. However, it is a difficult task for teachers without programming knowledge to design and author adaptive courses. This work presents ProPer SAT, an authoring tool implemented for quick and easy SCORM courseware construction which can also be adapted to specific user learning styles.

  13. Interest Level in 2-Year-Olds with Autism Spectrum Disorder Predicts Rate of Verbal, Nonverbal, and Adaptive Skill Acquisition

    ERIC Educational Resources Information Center

    Klintwall, Lars; Macari, Suzanne; Eikeseth, Svein; Chawarska, Katarzyna

    2015-01-01

    Recent studies have suggested that skill acquisition rates for children with autism spectrum disorders receiving early interventions can be predicted by child motivation. We examined whether level of interest during an Autism Diagnostic Observation Schedule assessment at 2?years predicts subsequent rates of verbal, nonverbal, and adaptive skill…

  14. Adapting CEF-Descriptors for Rating Purposes: Validation by a Combined Rater Training and Scale Revision Approach

    ERIC Educational Resources Information Center

    Harsch, Claudia; Martin, Guido

    2012-01-01

    We explore how a local rating scale can be based on the Common European Framework CEF-proficiency scales. As part of the scale validation (Alderson, 1991; Lumley, 2002), we examine which adaptations are needed to turn CEF-proficiency descriptors into a rating scale for a local context, and to establish a practicable method to revise the initial…

  15. Lessons learned from studying the functional impact of adaptive seating interventions for children with cerebral palsy.

    PubMed

    Ryan, Stephen E

    2016-03-01

    Little empirical evidence exists about the effectiveness of assistive technology interventions for children with cerebral palsy (CP) to inform clinical practice. This article reviews what we know about the functional impact of adaptive seating interventions - a common assistive technology type recommended for children with CP. A contemporary assistive technology outcomes framework is considered as a way to model the temporality and measure the effects of seating interventions and moderating cofactors. Three research studies are profiled to illustrate different research methods, measurement approaches, and follow-up periods to learn about adaptive seating outcomes. Recommendations for future research include the adoption of common measurement indicators, consideration of quality assessment criteria, and the use of varied methodologies to generate new knowledge about functional outcomes. It is suggested that the proposed strategies will lead to new understandings, clinical applications, and ultimately improvements in the everyday lives of children with CP and their families. PMID:27027612

  16. Caudate nucleus reactivity predicts perceptual learning rate for visual feature conjunctions.

    PubMed

    Reavis, Eric A; Frank, Sebastian M; Tse, Peter U

    2015-04-15

    Useful information in the visual environment is often contained in specific conjunctions of visual features (e.g., color and shape). The ability to quickly and accurately process such conjunctions can be learned. However, the neural mechanisms responsible for such learning remain largely unknown. It has been suggested that some forms of visual learning might involve the dopaminergic neuromodulatory system (Roelfsema et al., 2010; Seitz and Watanabe, 2005), but this hypothesis has not yet been directly tested. Here we test the hypothesis that learning visual feature conjunctions involves the dopaminergic system, using functional neuroimaging, genetic assays, and behavioral testing techniques. We use a correlative approach to evaluate potential associations between individual differences in visual feature conjunction learning rate and individual differences in dopaminergic function as indexed by neuroimaging and genetic markers. We find a significant correlation between activity in the caudate nucleus (a component of the dopaminergic system connected to visual areas of the brain) and visual feature conjunction learning rate. Specifically, individuals who showed a larger difference in activity between positive and negative feedback on an unrelated cognitive task, indicative of a more reactive dopaminergic system, learned visual feature conjunctions more quickly than those who showed a smaller activity difference. This finding supports the hypothesis that the dopaminergic system is involved in visual learning, and suggests that visual feature conjunction learning could be closely related to associative learning. However, no significant, reliable correlations were found between feature conjunction learning and genotype or dopaminergic activity in any other regions of interest.

  17. Adaptation of Lactococcus lactis to high growth temperature leads to a dramatic increase in acidification rate.

    PubMed

    Chen, Jun; Shen, Jing; Ingvar Hellgren, Lars; Ruhdal Jensen, Peter; Solem, Christian

    2015-09-21

    Lactococcus lactis is essential for most cheese making, and this mesophilic bacterium has its growth optimum around 30 °C. We have, through adaptive evolution, isolated a mutant TM29 that grows well up to 39 °C, and continuous growth at 40 °C is possible if pre-incubated at a slightly lower temperature. At the maximal permissive temperature for the wild-type, 38 °C, TM29 grows 33% faster and has a 12% higher specific lactate production rate than its parent MG1363, which results in fast lactate accumulation. Genome sequencing was used to reveal the mutations accumulated, most of which were shown to affect thermal tolerance. Of the mutations with more pronounced effects, two affected expression of single proteins (chaperone; riboflavin transporter), two had pleiotropic effects (RNA polymerase) which changed the gene expression profile, and one resulted in a change in the coding sequence of CDP-diglyceride synthase. A large deletion containing 10 genes was also found to affect thermal tolerance significantly. With this study we demonstrate a simple approach to obtain non-GMO derivatives of the important L. lactis that possess properties desirable by the industry, e.g. thermal robustness and increased rate of acidification. The mutations we have identified provide a genetic basis for further investigation of thermal tolerance.

  18. Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems

    PubMed Central

    Yin, Zhendong; Zhuang, Shufeng; Wu, Zhilu; Ma, Bo

    2015-01-01

    Orthogonal frequency division multiple access (OFDMA), which is widely used in the wireless sensor networks, allows different users to obtain different subcarriers according to their subchannel gains. Therefore, how to assign subcarriers and power to different users to achieve a high system sum rate is an important research area in OFDMA systems. In this paper, the focus of study is on the rate adaptive (RA) based resource allocation with proportional fairness constraints. Since the resource allocation is a NP-hard and non-convex optimization problem, a new efficient resource allocation algorithm ACO-SPA is proposed, which combines ant colony optimization (ACO) and suboptimal power allocation (SPA). To reduce the computational complexity, the optimization problem of resource allocation in OFDMA systems is separated into two steps. For the first one, the ant colony optimization algorithm is performed to solve the subcarrier allocation. Then, the suboptimal power allocation algorithm is developed with strict proportional fairness, and the algorithm is based on the principle that the sums of power and the reciprocal of channel-to-noise ratio for each user in different subchannels are equal. To support it, plenty of simulation results are presented. In contrast with root-finding and linear methods, the proposed method provides better performance in solving the proportional resource allocation problem in OFDMA systems. PMID:26426016

  19. Gait in ducks (Anas platyrhynchos) and chickens (Gallus gallus) - similarities in adaptation to high growth rate.

    PubMed

    Duggan, B M; Hocking, P M; Clements, D N

    2016-01-01

    Genetic selection for increased growth rate and muscle mass in broiler chickens has been accompanied by mobility issues and poor gait. There are concerns that the Pekin duck, which is on a similar selection trajectory (for production traits) to the broiler chicken, may encounter gait problems in the future. In order to understand how gait has been altered by selection, the walking ability of divergent lines of high- and low-growth chickens and ducks was objectively measured using a pressure platform, which recorded various components of their gait. In both species, lines which had been selected for large breast muscle mass moved at a slower velocity and with a greater step width than their lighter conspecifics. These high-growth lines also spent more time supported by two feet in order to improve balance when compared with their lighter, low-growth conspecifics. We demonstrate that chicken and duck lines which have been subjected to intense selection for high growth rates and meat yields have adapted their gait in similar ways. A greater understanding of which components of gait have been altered in selected lines with impaired walking ability may lead to more effective breeding strategies to improve gait in poultry. PMID:27387535

  20. Adaptation of Lactococcus lactis to high growth temperature leads to a dramatic increase in acidification rate.

    PubMed

    Chen, Jun; Shen, Jing; Ingvar Hellgren, Lars; Ruhdal Jensen, Peter; Solem, Christian

    2015-01-01

    Lactococcus lactis is essential for most cheese making, and this mesophilic bacterium has its growth optimum around 30 °C. We have, through adaptive evolution, isolated a mutant TM29 that grows well up to 39 °C, and continuous growth at 40 °C is possible if pre-incubated at a slightly lower temperature. At the maximal permissive temperature for the wild-type, 38 °C, TM29 grows 33% faster and has a 12% higher specific lactate production rate than its parent MG1363, which results in fast lactate accumulation. Genome sequencing was used to reveal the mutations accumulated, most of which were shown to affect thermal tolerance. Of the mutations with more pronounced effects, two affected expression of single proteins (chaperone; riboflavin transporter), two had pleiotropic effects (RNA polymerase) which changed the gene expression profile, and one resulted in a change in the coding sequence of CDP-diglyceride synthase. A large deletion containing 10 genes was also found to affect thermal tolerance significantly. With this study we demonstrate a simple approach to obtain non-GMO derivatives of the important L. lactis that possess properties desirable by the industry, e.g. thermal robustness and increased rate of acidification. The mutations we have identified provide a genetic basis for further investigation of thermal tolerance. PMID:26388459

  1. Adaptation of Lactococcus lactis to high growth temperature leads to a dramatic increase in acidification rate

    PubMed Central

    Chen, Jun; Shen, Jing; Ingvar Hellgren, Lars; Ruhdal Jensen, Peter; Solem, Christian

    2015-01-01

    Lactococcus lactis is essential for most cheese making, and this mesophilic bacterium has its growth optimum around 30 °C. We have, through adaptive evolution, isolated a mutant TM29 that grows well up to 39 °C, and continuous growth at 40 °C is possible if pre-incubated at a slightly lower temperature. At the maximal permissive temperature for the wild-type, 38 °C, TM29 grows 33% faster and has a 12% higher specific lactate production rate than its parent MG1363, which results in fast lactate accumulation. Genome sequencing was used to reveal the mutations accumulated, most of which were shown to affect thermal tolerance. Of the mutations with more pronounced effects, two affected expression of single proteins (chaperone; riboflavin transporter), two had pleiotropic effects (RNA polymerase) which changed the gene expression profile, and one resulted in a change in the coding sequence of CDP-diglyceride synthase. A large deletion containing 10 genes was also found to affect thermal tolerance significantly. With this study we demonstrate a simple approach to obtain non-GMO derivatives of the important L. lactis that possess properties desirable by the industry, e.g. thermal robustness and increased rate of acidification. The mutations we have identified provide a genetic basis for further investigation of thermal tolerance. PMID:26388459

  2. Estimating oxygen consumption from heart rate using adaptive neuro-fuzzy inference system and analytical approaches.

    PubMed

    Kolus, Ahmet; Dubé, Philippe-Antoine; Imbeau, Daniel; Labib, Richard; Dubeau, Denise

    2014-11-01

    In new approaches based on adaptive neuro-fuzzy systems (ANFIS) and analytical method, heart rate (HR) measurements were used to estimate oxygen consumption (VO2). Thirty-five participants performed Meyer and Flenghi's step-test (eight of which performed regeneration release work), during which heart rate and oxygen consumption were measured. Two individualized models and a General ANFIS model that does not require individual calibration were developed. Results indicated the superior precision achieved with individualized ANFIS modelling (RMSE = 1.0 and 2.8 ml/kg min in laboratory and field, respectively). The analytical model outperformed the traditional linear calibration and Flex-HR methods with field data. The General ANFIS model's estimates of VO2 were not significantly different from actual field VO2 measurements (RMSE = 3.5 ml/kg min). With its ease of use and low implementation cost, the General ANFIS model shows potential to replace any of the traditional individualized methods for VO2 estimation from HR data collected in the field. PMID:24793823

  3. Collaborative Education in Climate Change Sciences and Adaptation through Interactive Learning

    NASA Astrophysics Data System (ADS)

    Ozbay, G.; Sriharan, S.; Fan, C.

    2014-12-01

    As a result of several funded climate change education grants, collaboration between VSU, DSU, and MSU, was established to provide the innovative and cohesive education and research opportunities to underrepresented groups in the climate related sciences. Prior to offering climate change and adaptation related topics to the students, faculty members of the three collaborating institutions participated at a number of faculty training and preparation workshops for teaching climate change sciences (i.e. AMS Diversity Project Workshop, NCAR Faculty-Student Team on Climate Change, NASA-NICE Program). In order to enhance the teaching and student learning on various issues in the Environmental Sciences Programs, Climatology, Climate Change Sciences and Adaptation or related courses were developed at Delaware State University and its partner institutions (Virginia State University and Morgan State University). These courses were prepared to deliver information on physical basis for the earth's climate system and current climate change instruction modules by AMS and historic climate information (NOAA Climate Services, U.S. and World Weather Data, NCAR and NASA Climate Models). By using Global Seminar as a Model, faculty members worked in teams to engage students in videoconferencing on climate change through Contemporary Global Studies and climate courses including Climate Change and Adaptation Science, Sustainable Agriculture, Introduction to Environmental Sciences, Climatology, and Ecology and Adaptation courses. All climate change courses have extensive hands-on practices and research integrated into the student learning experiences. Some of these students have presented their classroom projects during Earth Day, Student Climate Change Symposium, Undergraduate Summer Symposium, and other national conferences.

  4. Adaptive Neuron Model: An architecture for the rapid learning of nonlinear topological transformations

    NASA Technical Reports Server (NTRS)

    Tawel, Raoul (Inventor)

    1994-01-01

    A method for the rapid learning of nonlinear mappings and topological transformations using a dynamically reconfigurable artificial neural network is presented. This fully-recurrent Adaptive Neuron Model (ANM) network was applied to the highly degenerate inverse kinematics problem in robotics, and its performance evaluation is bench-marked. Once trained, the resulting neuromorphic architecture was implemented in custom analog neural network hardware and the parameters capturing the functional transformation downloaded onto the system. This neuroprocessor, capable of 10(exp 9) ops/sec, was interfaced directly to a three degree of freedom Heathkit robotic manipulator. Calculation of the hardware feed-forward pass for this mapping was benchmarked at approximately 10 microsec.

  5. Adaptive Performance Seeking Control Using Fuzzy Model Reference Learning Control and Positive Gradient Control

    NASA Technical Reports Server (NTRS)

    Kopasakis, George

    1997-01-01

    Performance Seeking Control attempts to find the operating condition that will generate optimal performance and control the plant at that operating condition. In this paper a nonlinear multivariable Adaptive Performance Seeking Control (APSC) methodology will be developed and it will be demonstrated on a nonlinear system. The APSC is comprised of the Positive Gradient Control (PGC) and the Fuzzy Model Reference Learning Control (FMRLC). The PGC computes the positive gradients of the desired performance function with respect to the control inputs in order to drive the plant set points to the operating point that will produce optimal performance. The PGC approach will be derived in this paper. The feedback control of the plant is performed by the FMRLC. For the FMRLC, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for the effective tuning of the FMRLC controller.

  6. Dual-dictionary learning based MR image reconstruction with self-adaptive dictionaries.

    PubMed

    Jiansen Li; Ying Song; Jun Zhao

    2015-01-01

    Dual-dictionary learning method utilizes two dictionaries at two different resolution levels, a high resolution dictionary trained with full-data training set, and a low resolution dictionary co-trained with corresponding undersampled dataset. This method effectively incorporates a priori knowledge of typical structures, specific features and local details, leading to its success in magnetic resonance (MR) image reconstruction from highly undersampled k-space data. In this paper, we improve this dual-dictionary learning method by using self-adaptive dictionaries. The two level dictionaries are updated correspondingly in the inner iteration after updating the reconstruction result to maintain their matching accuracy. Experimental results show that the proposed method can improve the reconstruction quality efficiently and enhance the robustness significantly.

  7. Adaptive, Fast Walking in a Biped Robot under Neuronal Control and Learning

    PubMed Central

    Kulvicius, Tomas; Porr, Bernd; Wörgötter, Florentin

    2007-01-01

    Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (>3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks. PMID:17630828

  8. WCTC 5-Year Graduation Rates. Retention for Learning Presentation.

    ERIC Educational Resources Information Center

    Brenner, Viktor; Sanford, Doug

    This study addresses Waukesha County Technical College's (WCTC) 5-year retention and graduate rates. Some of the key findings of the report are as follow: (1) overall, 35.4% of cohort students graduated; (2) a 4-year combined graduation rate for ethnic minorities was 26%, well below the overall average; (3) the percent of cohort that graduated…

  9. Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

    PubMed

    Peng, Yong; Lu, Bao-Liang; Wang, Suhang

    2015-05-01

    Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches. PMID:25634552

  10. LABRADOR: a learning autonomous behavior-based robot for adaptive detection and object retrieval

    NASA Astrophysics Data System (ADS)

    Yamauchi, Brian; Moseley, Mark; Brookshire, Jonathan

    2013-01-01

    As part of the TARDEC-funded CANINE (Cooperative Autonomous Navigation in a Networked Environment) Program, iRobot developed LABRADOR (Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval). LABRADOR was based on the rugged, man-portable, iRobot PackBot unmanned ground vehicle (UGV) equipped with an explosives ordnance disposal (EOD) manipulator arm and a custom gripper. For LABRADOR, we developed a vision-based object learning and recognition system that combined a TLD (track-learn-detect) filter based on object shape features with a color-histogram-based object detector. Our vision system was able to learn in real-time to recognize objects presented to the robot. We also implemented a waypoint navigation system based on fused GPS, IMU (inertial measurement unit), and odometry data. We used this navigation capability to implement autonomous behaviors capable of searching a specified area using a variety of robust coverage strategies - including outward spiral, random bounce, random waypoint, and perimeter following behaviors. While the full system was not integrated in time to compete in the CANINE competition event, we developed useful perception, navigation, and behavior capabilities that may be applied to future autonomous robot systems.

  11. Development and Evaluation of an E-Learning Course for Deaf and Hard of Hearing Based on the Advanced Adapted Pedagogical Index Method

    ERIC Educational Resources Information Center

    Debevc, Matjaž; Stjepanovic, Zoran; Holzinger, Andreas

    2014-01-01

    Web-based and adapted e-learning materials provide alternative methods of learning to those used in a traditional classroom. Within the study described in this article, deaf and hard of hearing people used an adaptive e-learning environment to improve their computer literacy. This environment included streaming video with sign language interpreter…

  12. Reliability of the Motor Learning Strategy Rating Instrument for Children and Youth with Acquired Brain Injury

    ERIC Educational Resources Information Center

    Kamath, Trishna; Pfeifer, Megan; Banerjee-Guenette, Priyanka; Hunter, Theresa; Ito, Julia; Salbach, Nancy M.; Wright, Virginia; Levac, Danielle

    2012-01-01

    Purpose: To evaluate reliability and feasibility of the Motor Learning Strategy Rating Instrument (MLSRI) in children with acquired brain injury (ABI). The MLSRI quantifies the extent to which motor learning strategies (MLS) are used within physiotherapy (PT) interventions. Methods: PT sessions conducted by ABI team physiotherapists with a…

  13. Characteristics and Activities of Teachers on Distance Learning Programs That Affect Their Ratings

    ERIC Educational Resources Information Center

    Stanišic Stojic, Svetlana M.; Dobrijevic, Gordana; Stanišic, Nemanja; Stanic, Nenad

    2014-01-01

    This paper presents an analysis of teachers' ratings on distance learning undergraduate study programs: 7,156 students enrolled in traditional and 528 students enrolled in distance learning studies took part in the evaluation questionnaire, assessing 71 teachers. The data were collected from the Moodle platform and from the Singidunum…

  14. Maternal Ratings of Temperament and Operant Learning in Two- to Three-Month-Old Infants.

    ERIC Educational Resources Information Center

    Dunst, Carl J.; Lingerfelt, Barbara

    1985-01-01

    Relationship between maternal ratings of temperament and operant learning was examined in 18 2- to 3-month-old infants. Subjects participated in a conjugate reinforcement experiment; mothers of subjects completed the Carey and McDevitt Revised Infant Temperament Questionnaire 2 to 3 days before the learning study. Two temperament dimensions,…

  15. Effects of Graduate Teaching Assistant Attire on Student Learning, Misbehaviors, and Ratings of Instruction.

    ERIC Educational Resources Information Center

    Roach, K. David

    1997-01-01

    Finds significant relationships between levels of teaching assistant dress and student cognitive learning, student affective learning, and ratings of instruction. Finds significant negative relationship between casual instructor attire and student likelihood of misbehavior, with misbehaviors less likely for teaching assistants with high…

  16. Effects of Learning Experience on Forgetting Rates of Item and Associative Memories

    ERIC Educational Resources Information Center

    Yang, Jiongjiong; Zhan, Lexia; Wang, Yingying; Du, Xiaoya; Zhou, Wenxi; Ning, Xueling; Sun, Qing; Moscovitch, Morris

    2016-01-01

    Are associative memories forgotten more quickly than item memories, and does the level of original learning differentially influence forgetting rates? In this study, we addressed these questions by having participants learn single words and word pairs once (Experiment 1), three times (Experiment 2), and six times (Experiment 3) in a massed…

  17. The Factor Structure and Sources of Variation Underlying the Social Learning Environment Rating Scales: Monograph 1.

    ERIC Educational Resources Information Center

    Warshow, Joyce P.; Bepko, Raymond A.

    Seventeen intermediate level classes for the educable mentally retarded were involved in an investigation of the factor structure of the Social Learning Environment Rating Scale (SLERS), an instrument designed to quantify teacher-student behavior based on the Social Learning Curriculum (SLC). The 17 classes were observed implementing six…

  18. New Treatments for Autism: Effects of a Gluten-Free Diet on Rate of Learning.

    ERIC Educational Resources Information Center

    Grace, Jennifer B.; Velez, Denise M.; Chambliss, Catherine

    This study assessed the effects of a gluten-free diet over one year on learning patterns in three autistic children (ages 5 to 8) participating in an applied behavioral analysis program. Rates of learning for five behavioral targets 3 months, 6 months, 9 months, and 12 months after the start of the diet were compared using a within-subjects…

  19. Spontaneous eye blink rate predicts learning from negative, but not positive, outcomes.

    PubMed

    Slagter, Heleen A; Georgopoulou, Katerina; Frank, Michael J

    2015-05-01

    A large body of research shows that striatal dopamine critically affects the extent to which we learn from the positive and negative outcomes of our decisions. In this study, we examined the relationship between reinforcement learning and spontaneous eye blink rate (sEBR), a cheap, non-invasive, and easy to obtain marker of striatal dopaminergic activity. Based on previous findings from pharmacological and patient studies, our main prediction was that in healthy individuals, low blink rates (and concomitant lower striatal dopamine levels) would be associated with better learning from negative choices, while high blink rates (and concomitant higher striatal dopamine levels) would be associated with learning from positive choices. Behavioral analyses showed that in healthy individuals, lower blink rates were indeed associated with greater learning from negative outcomes, indicating that lower dopamine levels per se may enhance avoidance learning. Yet, higher EBR was not associated with better learning from positive outcomes. These observations support the notion that sEBR reflects tonic dopamine levels, and suggest that sEBR may specifically relate to dopamine D2 receptor function, given the importance of the dopaminergic D2 pathway in avoidance learning. More generally, these findings highlight the usefulness of sEBR as a non-invasive and cheap method for assessing the relationship between striatal dopaminergic function and behavior.

  20. The role of motor learning in spatial adaptation near a tool.

    PubMed

    Brown, Liana E; Doole, Robert; Malfait, Nicole

    2011-01-01

    Some visual-tactile (bimodal) cells have visual receptive fields (vRFs) that overlap and extend moderately beyond the skin of the hand. Neurophysiological evidence suggests, however, that a vRF will grow to encompass a hand-held tool following active tool use but not after passive holding. Why does active tool use, and not passive holding, lead to spatial adaptation near a tool? We asked whether spatial adaptation could be the result of motor or visual experience with the tool, and we distinguished between these alternatives by isolating motor from visual experience with the tool. Participants learned to use a novel, weighted tool. The active training group received both motor and visual experience with the tool, the passive training group received visual experience with the tool, but no motor experience, and finally, a no-training control group received neither visual nor motor experience using the tool. After training, we used a cueing paradigm to measure how quickly participants detected targets, varying whether the tool was placed near or far from the target display. Only the active training group detected targets more quickly when the tool was placed near, rather than far, from the target display. This effect of tool location was not present for either the passive-training or control groups. These results suggest that motor learning influences how visual space around the tool is represented. PMID:22174944

  1. Dolphin genome provides evidence for adaptive evolution of nervous system genes and a molecular rate slowdown

    PubMed Central

    McGowen, Michael R.; Grossman, Lawrence I.; Wildman, Derek E.

    2012-01-01

    Cetaceans (dolphins and whales) have undergone a radical transformation from the original mammalian bodyplan. In addition, some cetaceans have evolved large brains and complex cognitive capacities. We compared approximately 10 000 protein-coding genes culled from the bottlenose dolphin genome with nine other genomes to reveal molecular correlates of the remarkable phenotypic features of these aquatic mammals. Evolutionary analyses demonstrated that the overall synonymous substitution rate in dolphins has slowed compared with other studied mammals, and is within the range of primates and elephants. We also discovered 228 genes potentially under positive selection (dN/dS > 1) in the dolphin lineage. Twenty-seven of these genes are associated with the nervous system, including those related to human intellectual disabilities, synaptic plasticity and sleep. In addition, genes expressed in the mitochondrion have a significantly higher mean dN/dS ratio in the dolphin lineage than others examined, indicating evolution in energy metabolism. We encountered selection in other genes potentially related to cetacean adaptations such as glucose and lipid metabolism, dermal and lung development, and the cardiovascular system. This study underlines the parallel molecular trajectory of cetaceans with other mammalian groups possessing large brains. PMID:22740643

  2. Foundations for learning and adaptation in a multi-degree-of-freedom unmanned ground vehicle

    NASA Astrophysics Data System (ADS)

    Blackburn, Michael R.; Bailey, Richard

    2004-04-01

    The real-time coordination and control of a many motion degrees of freedom (dof) unmanned ground vehicle under dynamic conditions in a complex environment is nearly impossible for a human operator to accomplish. Needed are adaptive on-board mechanisms to quickly complete sensor-effector loops to maintain balance and leverage. This paper contains a description of our approach to the control problem for a small unmanned ground vehicle with six dof in the three spatial dimensions. Vehicle control is based upon seven fixed action patterns that exercise all of the motion dof of which the vehicle is capable, and five basic reactive behaviors that protect the vehicle during operation. The reactive behaviors demonstrate short-term adaptations. The learning processes for long-term adaptations of the vehicle control functions that we are implementing are composed of classical and operant conditionings of novel responses to information available from distance sensors (vision and audition) built upon the pre-defined fixed action patterns. The fixed action patterns are in turn modulated by the pre-defined low-level reactive behaviors that, as unconditioned responses, continuously serve to maintain the viability of the robot during the activations of the fixed action patterns, and of the higher-order (conditioned) behaviors. The sensors of the internal environment that govern the low-level reactive behaviors also serve as the criteria for operant conditioning, and satisfy the requirement for basic behavioral motivation.

  3. The cultural niche: Why social learning is essential for human adaptation

    PubMed Central

    Boyd, Robert; Richerson, Peter J.; Henrich, Joseph

    2011-01-01

    In the last 60,000 y humans have expanded across the globe and now occupy a wider range than any other terrestrial species. Our ability to successfully adapt to such a diverse range of habitats is often explained in terms of our cognitive ability. Humans have relatively bigger brains and more computing power than other animals, and this allows us to figure out how to live in a wide range of environments. Here we argue that humans may be smarter than other creatures, but none of us is nearly smart enough to acquire all of the information necessary to survive in any single habitat. In even the simplest foraging societies, people depend on a vast array of tools, detailed bodies of local knowledge, and complex social arrangements and often do not understand why these tools, beliefs, and behaviors are adaptive. We owe our success to our uniquely developed ability to learn from others. This capacity enables humans to gradually accumulate information across generations and develop well-adapted tools, beliefs, and practices that are too complex for any single individual to invent during their lifetime. PMID:21690340

  4. Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

    NASA Technical Reports Server (NTRS)

    Troudet, Terry; Merrill, Walter C.

    1990-01-01

    The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

  5. Effects of learning experience on forgetting rates of item and associative memories.

    PubMed

    Yang, Jiongjiong; Zhan, Lexia; Wang, Yingying; Du, Xiaoya; Zhou, Wenxi; Ning, Xueling; Sun, Qing; Moscovitch, Morris

    2016-07-01

    Are associative memories forgotten more quickly than item memories, and does the level of original learning differentially influence forgetting rates? In this study, we addressed these questions by having participants learn single words and word pairs once (Experiment 1), three times (Experiment 2), and six times (Experiment 3) in a massed learning (ML) or a distributed learning (DL) mode. Then they were tested for item and associative recognition separately after four retention intervals: 10 min, 1 d, 1 wk, and 1 mo. The contribution of recollection and familiarity processes were assessed by participants' remember/know judgments. The results showed that for both item and associative memories, across different degrees of learning, recollection decreased significantly and was the main source of forgetting over time, whereas familiarity remained relatively stable over time. Learning multiple times led to slower forgetting at shorter intervals, depending on recollection and familiarity processes. Compared with massed learning, distributed learning (six times) especially benefited associative memory by increasing recollection, leading to slower forgetting at longer intervals. This study highlighted the importance of process contribution and learning experiences in modulating the forgetting rates of item and associative memories. We interpret these results within the framework of a dual factor representational model of forgetting (as noted in a previous study) in which recollection is more prone to decay over time than familiarity.

  6. Multilevel learning in the adaptive management of waterfowl harvests: 20 years and counting

    USGS Publications Warehouse

    Johnson, Fred A.; Boomer, G. Scott; Williams, Byron K.; Nichols, James D.; Case, David J.

    2015-01-01

    In 1995, the U.S. Fish and Wildlife Service implemented an adaptive harvest management program (AHM) for the sport harvest of midcontinent mallards (Anas platyrhynchos). The program has been successful in reducing long-standing contentiousness in the regulatory process, while integrating science and policy in a coherent, rigorous, and transparent fashion. After 20 years, much has been learned about the relationship among waterfowl populations, their environment, and hunting regulations, with each increment of learning contributing to better management decisions. At the same time, however, much has been changing in the social, institutional, and environmental arenas that provide context for the AHM process. Declines in hunter numbers, competition from more pressing conservation issues, and global-change processes are increasingly challenging waterfowl managers to faithfully reflect the needs and desires of stakeholders, to account for an increasing number of institutional constraints, and to (probabilistically) predict the consequences of regulatory policy in a changing environment. We review the lessons learned from the AHM process so far, and describe emerging challenges and ways in which they may be addressed. We conclude that the practice of AHM has greatly increased an awareness of the roles of social values, trade-offs, and attitudes toward risk in regulatory decision-making. Nevertheless, going forward the waterfowl management community will need to focus not only on the relationships among habitat, harvest, and waterfowl populations, but on the ways in which society values waterfowl and how those values can change over time. 

  7. Base-Rate Neglect as a Function of Base Rates in Probabilistic Contingency Learning

    ERIC Educational Resources Information Center

    Kutzner, Florian; Freytag, Peter; Vogel, Tobias; Fiedler, Klaus

    2008-01-01

    When humans predict criterion events based on probabilistic predictors, they often lend excessive weight to the predictor and insufficient weight to the base rate of the criterion event. In an operant analysis, using a matching-to-sample paradigm, Goodie and Fantino (1996) showed that humans exhibit base-rate neglect when predictors are associated…

  8. Adaptation and Validation of the Inventory of Learning Styles for Quality Assurance in a Hong Kong Post-Secondary Education Context

    ERIC Educational Resources Information Center

    Law, Dennis C. S.; Meyer, Jan H. F.

    2010-01-01

    A Chinese translation of the Inventory of Learning Styles (ILS), a quantitative instrument employed mainly in Western higher education contexts for collecting students' feedback on their learning patterns (in the form of students' processing strategies, regulation strategies, learning orientations and conceptions of learning), was adapted and…

  9. Adaptation and Evaluation of Online Self-learning Modules to Teach Critical Appraisal and Evidence-Based Practice in Nursing: An International Collaboration.

    PubMed

    Gagnon, Johanne; Gagnon, Marie-Pierre; Buteau, Rose-Anne; Azizah, Ginette Mbourou; Jetté, Sylvie; Lampron, Amélie; Simonyan, David; Asua, José; Reviriego, Eva

    2015-07-01

    Healthcare professionals need to update their knowledge and acquire skills to continually inform their practice based on scientific evidence. This study was designed to evaluate online self-learning modules on critical appraisal skills to promote the use of research in clinical practice among nurses from Quebec (Canada) and the Basque Country (Spain). The teaching material was developed in Quebec and adapted to the Basque Country as part of an international collaboration project. A prospective pre-post study was conducted with 36 nurses from Quebec and 47 from the Basque Country. Assessment comprised the administration of questionnaires before and after the course in order to explore the main intervention outcomes: knowledge acquisition and self-learning readiness. Satisfaction was also measured at the end of the course. Two of the three research hypotheses were confirmed: (1) participants significantly improved their overall knowledge score after the educational intervention; and (2) they were, in general, satisfied with the course, giving it a rating of seven out of 10. Participants also reported a greater readiness for self-directed learning after the course, but this result was not significant in Quebec. The study provides unique knowledge on the cultural adaptation of online self-learning modules for teaching nurses about critical appraisal skills and evidence-based practice.

  10. An Investigation into the Factors Influencing Extreme-Response Style: Improving Meaning of Translated and Culturally Adapted Rating Scales

    ERIC Educational Resources Information Center

    Arce-Ferrer, Alvaro J.

    2006-01-01

    Translation and cultural adaptation of rating scales are two critical components in testing culturally and/or linguistically heterogeneous populations. Despite the proper use of these scales, challenges typically arise from respondents' language, culture, ratiocination, and characteristics of measurement processes. This study investigated factors…

  11. The Parent Version of the Preschool Social Skills Rating System: Psychometric Analysis and Adaptation with a German Preschool Sample

    ERIC Educational Resources Information Center

    Hess, Markus; Scheithauer, Herbert; Kleiber, Dieter; Wille, Nora; Erhart, Michael; Ravens-Sieberer, Ulrike

    2014-01-01

    The Social Skills Rating System (SSRS) developed by Gresham and Elliott (1990) is a multirater, norm-referenced instrument measuring social skills and adaptive behavior in preschool children. The aims of the present study were (a) to test the factorial structure of the Parent Form of the SSRS for the first time with a German preschool sample (391…

  12. Interest level in 2-year-olds with autism spectrum disorder predicts rate of verbal, nonverbal, and adaptive skill acquisition

    PubMed Central

    Klintwall, Lars; Macari, Suzanne; Eikeseth, Svein; Chawarska, Katarzyna

    2016-01-01

    Recent studies have suggested that skill acquisition rates for children with autism spectrum disorders receiving early interventions can be predicted by child motivation. We examined whether level of interest during an Autism Diagnostic Observation Schedule assessment at 2 years predicts subsequent rates of verbal, nonverbal, and adaptive skill acquisition to the age of 3 years. A total of 70 toddlers with autism spectrum disorder, mean age of 21.9 months, were scored using Interest Level Scoring for Autism, quantifying toddlers’ interest in toys, social routines, and activities that could serve as reinforcers in an intervention. Adaptive level and mental age were measured concurrently (Time 1) and again after a mean of 16.3 months of treatment (Time 2). Interest Level Scoring for Autism score, Autism Diagnostic Observation Schedule score, adaptive age equivalent, verbal and nonverbal mental age, and intensity of intervention were entered into regression models to predict rates of skill acquisition. Interest level at Time 1 predicted subsequent acquisition rate of adaptive skills (R2 = 0.36) and verbal mental age (R2 = 0.30), above and beyond the effects of Time 1 verbal and nonverbal mental ages and Autism Diagnostic Observation Schedule scores. Interest level at Time 1 also contributed (R2 = 0.30), with treatment intensity, to variance in development of nonverbal mental age. PMID:25398893

  13. The Adaptation of the Mathematics Anxiety Rating Scale-Elementary Form into Turkish, Language Validity, and Preliminary Psychometric Investigation

    ERIC Educational Resources Information Center

    Baloglu, Mustafa; Balgalmis, Esra

    2010-01-01

    The purpose of the present study was to adapt the Mathematics Anxiety Rating Scale- Elementary Form (MARS-E, Suinn, 1988) into Turkish by first doing the translation of its items and then the preliminary psychometric investigation of the Turkish form. The study included four different samples: 30 bilingual language experts, 50 Turkish language…

  14. Adaptive iterative learning control for nonlinearly parameterised systems with unknown time-varying delays and input saturations

    NASA Astrophysics Data System (ADS)

    Zhang, Ruikun; Hou, Zhongsheng; Chi, Ronghu; Ji, Honghai

    2015-06-01

    In this work, an adaptive iterative learning control (AILC) scheme is proposed to address a class of nonlinearly parameterised systems with both unknown time-varying delays and input saturations. By incorporating a saturation function, a novel iterative learning control mechanism is constructed with a feedback term in the time domain and a fully saturated adaptive learning term in the iteration domain, which is used to estimate the unknown time-varying system uncertainty. A new time-weighted Lyapunov-Krasovskii-like composite energy function (LKL-CEF) is designed for the convergence analysis where time-weighted inputs, states and estimates of system uncertainty are all considered. Despite the existence of time-varying parametric uncertainties, time-varying delays, input saturations and local Lipschitz nonlinearities, the learning convergence is guaranteed with rigorous mathematical analysis. Simulation results verify the correctness and effectiveness of the proposed method further.

  15. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    NASA Astrophysics Data System (ADS)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  16. MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

    SciTech Connect

    Valiollahzadeh, S; Clark, J; Mawlawi, O

    2014-06-15

    Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care.

  17. Inferring learning rules from distributions of firing rates in cortical neurons.

    PubMed

    Lim, Sukbin; McKee, Jillian L; Woloszyn, Luke; Amit, Yali; Freedman, David J; Sheinberg, David L; Brunel, Nicolas

    2015-12-01

    Information about external stimuli is thought to be stored in cortical circuits through experience-dependent modifications of synaptic connectivity. These modifications of network connectivity should lead to changes in neuronal activity as a particular stimulus is repeatedly encountered. Here we ask what plasticity rules are consistent with the differences in the statistics of the visual response to novel and familiar stimuli in inferior temporal cortex, an area underlying visual object recognition. We introduce a method that allows one to infer the dependence of the presumptive learning rule on postsynaptic firing rate, and we show that the inferred learning rule exhibits depression for low postsynaptic rates and potentiation for high rates. The threshold separating depression from potentiation is strongly correlated with both mean and s.d. of the firing rate distribution. Finally, we show that network models implementing a rule extracted from data show stable learning dynamics and lead to sparser representations of stimuli. PMID:26523643

  18. Smart microscope: an adaptive optics learning system for aberration correction in multiphoton confocal microscopy.

    PubMed

    Albert, O; Sherman, L; Mourou, G; Norris, T B; Vdovin, G

    2000-01-01

    Off-axis aberrations in a beam-scanning multiphoton confocal microscope are corrected with a deformable mirror. The optimal mirror shape for each pixel is determined by a genetic learning algorithm, in which the second-harmonic or two-photon fluorescence signal from a reference sample is maximized. The speed of the convergence is improved by use of a Zernike polynomial basis for the deformable mirror shape. This adaptive optical correction scheme is implemented in an all-reflective system by use of extremely short (10-fs) optical pulses, and it is shown that the scanning area of an f:1 off-axis parabola can be increased by nine times with this technique. PMID:18059779

  19. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    NASA Astrophysics Data System (ADS)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  20. Negotiated meanings of disability simulations in an adapted physical activity course: learning from student reflections.

    PubMed

    Leo, Jennifer; Goodwin, Donna

    2014-04-01

    Disability simulations have been used as a pedagogical tool to simulate the functional and cultural experiences of disability. Despite their widespread application, disagreement about their ethical use, value, and efficacy persists. The purpose of this study was to understand how postsecondary kinesiology students experienced participation in disability simulations. An interpretative phenomenological approach guided the study's collection of journal entries and clarifying one-on-one interviews with four female undergraduate students enrolled in a required adapted physical activity course. The data were analyzed thematically and interpreted using the conceptual framework of situated learning. Three themes transpired: unnerving visibility, negotiating environments differently, and tomorrow I'll be fine. The students described emotional responses to the use of wheelchairs as disability artifacts, developed awareness of environmental barriers to culturally and socially normative activities, and moderated their discomfort with the knowledge they could end the simulation at any time.