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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. Regularized background adaptation: a novel learning rate control scheme for gaussian mixture modeling.

    PubMed

    Lin, Horng-Horng; Chuang, Jen-Hui; Liu, Tyng-Luh

    2011-03-01

    To model a scene for background subtraction, Gaussian mixture modeling (GMM) is a popular choice for its capability of adaptation to background variations. However, GMM often suffers from a tradeoff between robustness to background changes and sensitivity to foreground abnormalities and is inefficient in managing the tradeoff for various surveillance scenarios. By reviewing the formulations of GMM, we identify that such a tradeoff can be easily controlled by adaptive adjustments of the GMM's learning rates for image pixels at different locations and of distinct properties. A new rate control scheme based on high-level feedback is then developed to provide better regularization of background adaptation for GMM and to help resolving the tradeoff. Additionally, to handle lighting variations that change too fast to be caught by GMM, a heuristic rooting in frame difference is proposed to assist the proposed rate control scheme for reducing false foreground alarms. Experiments show the proposed learning rate control scheme, together with the heuristic for adaptation of over-quick lighting change, gives better performance than conventional GMM approaches.

  4. 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

  5. Reduction in learning rates associated with anterograde interference results from interactions between different timescales in motor adaptation.

    PubMed

    Sing, Gary C; Smith, Maurice A

    2010-08-19

    Prior experiences can influence future actions. These experiences can not only drive adaptive changes in motor output, but they can also modulate the rate at which these adaptive changes occur. Here we studied anterograde interference in motor adaptation--the ability of a previously learned motor task (Task A) to reduce the rate of subsequently learning a different (and usually opposite) motor task (Task B). We examined the formation of the motor system's capacity for anterograde interference in the adaptive control of human reaching-arm movements by determining the amount of interference after varying durations of exposure to Task A (13, 41, 112, 230, and 369 trials). We found that the amount of anterograde interference observed in the learning of Task B increased with the duration of Task A. However, this increase did not continue indefinitely; instead, the interference reached asymptote after 15-40 trials of Task A. Interestingly, we found that a recently proposed multi-rate model of motor adaptation, composed of two distinct but interacting adaptive processes, predicts several key features of the interference patterns we observed. Specifically, this computational model (without any free parameters) predicts the initial growth and leveling off of anterograde interference that we describe, as well as the asymptotic amount of interference that we observe experimentally (R(2) = 0.91). Understanding the mechanisms underlying anterograde interference in motor adaptation may enable the development of improved training and rehabilitation paradigms that mitigate unwanted interference.

  6. Adaptive manifold learning.

    PubMed

    Zhang, Zhenyue; Wang, Jing; Zha, Hongyuan

    2012-02-01

    Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization. In this paper, we develop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the local neighborhood sizes when imposing a connectivity structure on the given set of high-dimensional data points and 2) the adaptive bias reduction in the local low-dimensional embedding by accounting for the variations in the curvature of the manifold as well as its interplay with the sampling density of the data set. We demonstrate the effectiveness of our methods for improving the performance of manifold learning algorithms using both synthetic and real-world data sets.

  7. 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…

  8. 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

  9. 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…

  10. 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…

  11. Savings in locomotor adaptation explained by changes in learning parameters following initial adaptation.

    PubMed

    Mawase, Firas; Shmuelof, Lior; Bar-Haim, Simona; Karniel, Amir

    2014-04-01

    Faster relearning of an external perturbation, savings, offers a behavioral linkage between motor learning and memory. To explain savings effects in reaching adaptation experiments, recent models suggested the existence of multiple learning components, each shows different learning and forgetting properties that may change following initial learning. Nevertheless, the existence of these components in rhythmic movements with other effectors, such as during locomotor adaptation, has not yet been studied. Here, we study savings in locomotor adaptation in two experiments; in the first, subjects adapted to speed perturbations during walking on a split-belt treadmill, briefly adapted to a counter-perturbation and then readapted. In a second experiment, subjects readapted after a prolonged period of washout of initial adaptation. In both experiments we find clear evidence for increased learning rates (savings) during readaptation. We show that the basic error-based multiple timescales linear state space model is not sufficient to explain savings during locomotor adaptation. Instead, we show that locomotor adaptation leads to changes in learning parameters, so that learning rates are faster during readaptation. Interestingly, we find an intersubject correlation between the slow learning component in initial adaptation and the fast learning component in the readaptation phase, suggesting an underlying mechanism for savings. Together, these findings suggest that savings in locomotion and in reaching may share common computational and neuronal mechanisms; both are driven by the slow learning component and are likely to depend on cortical plasticity.

  12. 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.…

  13. Learning improves growth rate in grasshoppers.

    PubMed

    Dukas, R; Bernays, E A

    2000-03-14

    To quantify the adaptive significance of insect learning, we documented the behavior and growth rate of grasshoppers (Schistocerca americana) in an environment containing two artificial food types, one providing a balanced diet of protein and carbohydrate, which maximizes growth, and the other being carbohydrate-deficient, which is unsuitable for growth. Grasshoppers in the Learning treatment experienced a predictable environment, where the spatial location, taste, and color of each food source remained constant throughout the experiment. In contrast, grasshoppers of the Random treatment developed in a temporally varying environment, where the spatial location, taste, and color of the balanced and deficient food types randomly alternated twice each day. Our results show that the grasshoppers that could employ associative learning for diet choice experienced higher growth rates than individuals of the Random treatment, demonstrating the adaptive significance of learning in a small short-lived insect.

  14. 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…

  15. 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…

  16. Variation in Recombination Rate: Adaptive or Not?

    PubMed

    Ritz, Kathryn R; Noor, Mohamed A F; Singh, Nadia D

    2017-03-27

    Rates of meiotic recombination are widely variable both within and among species. However, the functional significance of this variation remains largely unknown. Is the observed within-species variation in recombination rate adaptive? Recent work has revealed new insight into the scale and scope of population-level variation in recombination rate. These data indicate that the magnitude of within-population variation in recombination is similar among taxa. The apparent similarity of the variance in recombination rate among individuals between distantly related species suggests that the relative costs and benefits of recombination that establish the upper and lower bounds may be similar across species. Here we review the current data on intraspecific variation in recombination rate and discuss the molecular and evolutionary costs and benefits of recombination frequency. We place this variation in the context of adaptation and highlight the need for more empirical studies focused on the adaptive value of variation in recombination rate.

  17. 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.

  18. 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…

  19. Adaptive and accelerated tracking-learning-detection

    NASA Astrophysics Data System (ADS)

    Guo, Pengyu; Li, Xin; Ding, Shaowen; Tian, Zunhua; Zhang, Xiaohu

    2013-08-01

    An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object motion to lessen the detector's searching range and the fixed number of positive and negative samples that ensures a constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in order to obtain a better effect, some TLD's details are redesigned, which uses a weight including both normalized correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.

  20. Teacher Adaptation to Personalized Learning Spaces

    ERIC Educational Resources Information Center

    Deed, Craig; Lesko, Thomas M.; Lovejoy, Valerie

    2014-01-01

    Personalized learning spaces are emerging in schools as a critical reaction to "industrial-era" school models. As the form and function of schools and pedagogy change, this places pressure on teachers to adapt their conventional practice. This paper addresses the question of how teachers can adapt their classroom practice to create…

  1. A model for culturally adapting a learning system.

    PubMed

    Del Rosario, M L

    1975-12-01

    The Cross-Cultural Adaption Model (XCAM) is designed to help identify cultural values contained in the text, narration, or visual components of a learning instrument and enables the adapter to evaluate his adapted model so that he can modify or revise it, and allows him to assess the modified version by actually measuring the amount of cultural conflict still present in it. Such a model would permit world-wide adaption of learning materials in population regulation. A random sample of the target group is selected. The adapter develops a measurin g instrument, the cross-cultural adaption scale (XCA), a number of statements about the cultural affinity of the object evaluated. The pretest portion of the sample tests the clarity and understandability of the rating scale to be used for evaluating the instructional materials; the pilot group analyzes the original version of the instructional mater ials, determines the criteria for change, and analyzes the adapted version in terms of these criteria; the control group is administered the original version of the learning materials; and the experimental group is administered the adapted version. Finally, the responses obtained from the XRA rating scale and discussions of both the experimental and control groups are studied and group differences are ev aluated according to cultural conflicts met with each version. With this data, the preferred combination of elements is constructed.

  2. 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…

  3. 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…

  4. 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

  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.

  6. ALISA: adaptive learning image and signal analysis

    NASA Astrophysics Data System (ADS)

    Bock, Peter

    1999-01-01

    ALISA (Adaptive Learning Image and Signal Analysis) is an adaptive statistical learning engine that may be used to detect and classify the surfaces and boundaries of objects in images. The engine has been designed, implemented, and tested at both the George Washington University and the Research Institute for Applied Knowledge Processing in Ulm, Germany over the last nine years with major funding from Robert Bosch GmbH and Lockheed-Martin Corporation. The design of ALISA was inspired by the multi-path cortical- column architecture and adaptive functions of the mammalian visual cortex.

  7. Simultaneous sensorimotor adaptation and sequence learning.

    PubMed

    Overduin, Simon A; Richardson, Andrew G; Bizzi, Emilio; Press, Daniel Z

    2008-01-01

    Sensorimotor adaptation and sequence learning have often been treated as distinct forms of motor learning. But frequently the motor system must acquire both types of experience simultaneously. Here, we investigated the interaction of these two forms of motor learning by having subjects adapt to predictable forces imposed by a robotic manipulandum while simultaneously reaching to an implicit sequence of targets. We show that adaptation to novel dynamics and learning of a sequence of movements can occur simultaneously and without significant interference or facilitation. When both conditions were presented simultaneously to subjects, their trajectory error and reaction time decreased to the same extent as those of subjects who experienced the force field or sequence independently.

  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. An adaptive learning control system for aircraft

    NASA Technical Reports Server (NTRS)

    Mekel, R.; Nachmias, S.

    1976-01-01

    A learning control system is developed which blends the gain scheduling and adaptive control into a single learning system that has the advantages of both. An important feature of the developed learning control system is its capability to adjust the gain schedule in a prescribed manner to account for changing aircraft operating characteristics. Furthermore, if tests performed by the criteria of the learning system preclude any possible change in the gain schedule, then the overall system becomes an ordinary gain scheduling system. Examples are discussed.

  12. 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…

  13. 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…

  14. 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…

  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. Rate-distortion optimized adaptive transform coding

    NASA Astrophysics Data System (ADS)

    Lim, Sung-Chang; Kim, Dae-Yeon; Jeong, Seyoon; Choi, Jin Soo; Choi, Haechul; Lee, Yung-Lyul

    2009-08-01

    We propose a rate-distortion optimized transform coding method that adaptively employs either integer cosine transform that is an integer-approximated version of discrete cosine transform (DCT) or integer sine transform (IST) in a rate-distortion sense. The DCT that has been adopted in most video-coding standards is known as a suboptimal substitute for the Karhunen-Loève transform. However, according to the correlation of a signal, an alternative transform can achieve higher coding efficiency. We introduce a discrete sine transform (DST) that achieves the high-energy compactness in a correlation coefficient range of -0.5 to 0.5 and is applied to the current design of H.264/AVC (advanced video coding). Moreover, to avoid the encoder and decoder mismatch and make the implementation simple, an IST that is an integer-approximated version of the DST is developed. The experimental results show that the proposed method achieves a Bjøntegaard Delta-RATE gain up to 5.49% compared to Joint model 11.0.

  17. 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

  18. 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…

  19. Adaptive functional systems: learning with chaos.

    PubMed

    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.

  20. Adaptive Multi-Rate Compression Effects on Vowel Analysis

    PubMed Central

    Ireland, David; Knuepffer, Christina; McBride, Simon J.

    2015-01-01

    Signal processing on digitally sampled vowel sounds for the detection of pathological voices has been firmly established. This work examines compression artifacts on vowel speech samples that have been compressed using the adaptive multi-rate codec at various bit-rates. Whereas previous work has used the sensitivity of machine learning algorithm to test for accuracy, this work examines the changes in the extracted speech features themselves and thus report new findings on the usefulness of a particular feature. We believe this work will have potential impact for future research on remote monitoring as the identification and exclusion of an ill-defined speech feature that has been hitherto used, will ultimately increase the robustness of the system. PMID:26347863

  1. Concept Based Approach for Adaptive Personalized Course Learning System

    ERIC Educational Resources Information Center

    Salahli, Mehmet Ali; Özdemir, Muzaffer; Yasar, Cumali

    2013-01-01

    One of the most important factors for improving the personalization aspects of learning systems is to enable adaptive properties to them. The aim of the adaptive personalized learning system is to offer the most appropriate learning path and learning materials to learners by taking into account their profiles. In this paper, a new approach to…

  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. Social influences on adaptive criterion learning.

    PubMed

    Cassidy, Brittany S; Dubé, Chad; Gutchess, Angela H

    2015-07-01

    People adaptively shift decision criteria when given biased feedback encouraging specific types of errors. Given that work on this topic has been conducted in nonsocial contexts, we extended the literature by examining adaptive criterion learning in both social and nonsocial contexts. Specifically, we compared potential differences in criterion shifting given performance feedback from social sources varying in reliability and from a nonsocial source. Participants became lax when given false positive feedback for false alarms, and became conservative when given false positive feedback for misses, replicating prior work. In terms of a social influence on adaptive criterion learning, people became more lax in response style over time if feedback was provided by a nonsocial source or by a social source meant to be perceived as unreliable and low-achieving. In contrast, people adopted a more conservative response style over time if performance feedback came from a high-achieving and reliable source. Awareness that a reliable and high-achieving person had not provided their feedback reduced the tendency to become more conservative, relative to those unaware of the source manipulation. Because teaching and learning often occur in a social context, these findings may have important implications for many scenarios in which people fine-tune their behaviors, given cues from others.

  4. 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.…

  5. 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 during novel…

  6. 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.

  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. 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…

  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. 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.

  12. Psychosocial and Adaptive Deficits Associated With Learning Disability Subtypes.

    PubMed

    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 comprehensive evaluations for learning and/or behavior problems in two Pacific Northwest school districts. Using concordance-discordance model (C-DM) processing strengths and weaknesses SLD identification criteria, results revealed working memory SLD (n = 20), processing speed SLD (n = 30), executive SLD (n = 32), and no disability groups (n = 41). Of the SLD subtypes, repeated measures MANOVA results revealed the processing speed SLD subtype exhibited the greatest psychosocial and adaptive impairment according to teacher behavior ratings. Findings suggest processing speed deficits may be behind the cognitive and psychosocial disturbances found in what has been termed "nonverbal" SLD. Limitations, implications, and future research needs are addressed.

  13. 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.

  14. 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…

  15. Saccade adaptation as a model of learning in voluntary movements.

    PubMed

    Iwamoto, Yoshiki; Kaku, Yuki

    2010-07-01

    Motor learning ensures the accuracy of our daily movements. However, we know relatively little about its mechanisms, particularly for voluntary movements. Saccadic eye movements serve to bring the image of a visual target precisely onto the fovea. Their accuracy is maintained not by on-line sensory feedback but by a learning mechanism, called saccade adaptation. Recent studies on saccade adaptation have provided valuable additions to our knowledge of motor learning. This review summarizes what we know about the characteristics and neural mechanisms of saccade adaptation, emphasizing recent findings and new ideas. Long-term adaptation, distinct from its short-term counterpart, seems to be present in the saccadic system. Accumulating evidence indicates the involvement of the oculomotor cerebellar vermis as a learning site. The superior colliculus is now suggested not only to generate saccade commands but also to issue driving signals for motor learning. These and other significant contributions have advanced our understanding of saccade adaptation and motor learning in general.

  16. Perceptual learning reconfigures the effects of visual adaptation.

    PubMed

    McGovern, David P; Roach, Neil W; Webb, Ben S

    2012-09-26

    Our sensory experiences over a range of different timescales shape our perception of the environment. Two particularly striking short-term forms of plasticity with manifestly different time courses and perceptual consequences are those caused by visual adaptation and perceptual learning. Although conventionally treated as distinct forms of experience-dependent plasticity, their neural mechanisms and perceptual consequences have become increasingly blurred, raising the possibility that they might interact. To optimize our chances of finding a functionally meaningful interaction between learning and adaptation, we examined in humans the perceptual consequences of learning a fine discrimination task while adapting the neurons that carry most information for performing this task. Learning improved discriminative accuracy to a level that ultimately surpassed that in an unadapted state. This remarkable improvement came at a price: adapting directions that before learning had little effect elevated discrimination thresholds afterward. The improvements in discriminative accuracy grew quickly and surpassed unadapted levels within the first few training sessions, whereas the deterioration in discriminative accuracy had a different time course. This learned reconfiguration of adapted discriminative accuracy occurred without a concomitant change to the characteristic perceptual biases induced by adaptation, suggesting that the system was still in an adapted state. Our results point to a functionally meaningful push-pull interaction between learning and adaptation in which a gain in sensitivity in one adapted state is balanced by a loss of sensitivity in other adapted states.

  17. Adaptive graph construction for Isomap manifold learning

    NASA Astrophysics Data System (ADS)

    Tran, Loc; Zheng, Zezhong; Zhou, Guoqing; Li, Jiang

    2015-03-01

    Isomap is a classical manifold learning approach that preserves geodesic distance of nonlinear data sets. One of the main drawbacks of this method is that it is susceptible to leaking, where a shortcut appears between normally separated portions of a manifold. We propose an adaptive graph construction approach that is based upon the sparsity property of the l1 norm. The l1 enhanced graph construction method replaces k-nearest neighbors in the classical approach. The proposed algorithm is first tested on the data sets from the UCI data base repository which showed that the proposed approach performs better than the classical approach. Next, the proposed approach is applied to two image data sets and achieved improved performances over standard Isomap.

  18. Teacher-Led Design of an Adaptive Learning Environment

    ERIC Educational Resources Information Center

    Mavroudi, Anna; Hadzilacos, Thanasis; Kalles, Dimitris; Gregoriades, Andreas

    2016-01-01

    This paper discusses a requirements engineering process that exemplifies teacher-led design in the case of an envisioned system for adaptive learning. Such a design poses various challenges and still remains an open research issue in the field of adaptive learning. Starting from a scenario-based elicitation method, the whole process was highly…

  19. 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.

  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. 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…

  2. 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.…

  3. 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…

  4. 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…

  5. Diminished Neural Adaptation during Implicit Learning in Autism

    PubMed Central

    Schipul, Sarah E.; Just, Marcel Adam

    2015-01-01

    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. PMID:26484826

  6. 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.

  7. The rate of adaptive evolution in animal mitochondria.

    PubMed

    James, Jennifer E; Piganeau, Gwenael; Eyre-Walker, Adam

    2016-01-01

    We have investigated whether there is adaptive evolution in mitochondrial DNA, using an extensive data set containing over 500 animal species from a wide range of taxonomic groups. We apply a variety of McDonald-Kreitman style methods to the data. We find that the evolution of mitochondrial DNA is dominated by slightly deleterious mutations, a finding which is supported by a number of previous studies. However, when we control for the presence of deleterious mutations using a new method, we find that mitochondria undergo a significant amount of adaptive evolution, with an estimated 26% (95% confidence intervals: 5.7-45%) of nonsynonymous substitutions fixed by adaptive evolution. We further find some weak evidence that the rate of adaptive evolution is correlated to synonymous diversity. We interpret this as evidence that at least some adaptive evolution is limited by the supply of mutations.

  8. 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…

  9. 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.

  10. 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.

  11. Interindividual comparison of different sensor principles for rate adaptive pacing.

    PubMed

    Malinowski, K

    1998-11-01

    In recent years a multitude of rate adaptive sensor systems based on different sensor signals have been developed to adapt the pacing rate to the physical load of the patient. In contrast to those systems the closed loop stimulation (CLS) represents a new concept, which regards the pacemaker as part of the cardiocirculatory system. The pacemaker converts the regulating information of the circulatory center into a heart rate. This study compares the closed loop stimulation and the different sensor systems that evaluate external parameters for rate adaptive pacing with a control group. To this end, 27 patients and 15 patients with a healthy sinus node (control group) were subjected to physical and mental stress tests. The recorded results were analyzed with regard to the maximum rates reached during stress. The results show that none of the studied sensor-controlled systems was able to determine an adequate pacing rate under all of the various load states. The dual sensor systems experience problems in balancing the input of the two sensor signals when calculating the pacing rate. The evaluation of a single external parameter, such as the acceleration of the upper body with the accelerometer, also failed to provide an adequate pacing rate in many stress situations. In contrast to all sensor systems, CLS achieved a heart rate in agreement with those of the reference group in all physical and mental stress situations.

  12. 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…

  13. 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…

  14. 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…

  15. 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…

  16. 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…

  17. 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…

  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. 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

  20. Reinforcement Learning for the Adaptive Control of Perception and Action

    DTIC Science & Technology

    1992-02-01

    This dissertation applies reinforcement learning to the adaptive control of active sensory-motor systems. Active sensory-motor systems, in addition...distinct states in the external world. This phenomenon, called perceptual aliasing, is shown to destabilize existing reinforcement learning algorithms

  1. 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…

  2. 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…

  3. Instructional Design and Adaptation Issues in Distance Learning Via Satellite.

    ERIC Educational Resources Information Center

    Thach, Liz

    1995-01-01

    Discusses a qualitative research study conducted in a distance-learning environment using satellite delivery. Describes changes in instructional design and adaptation issues which faculty and professionals involved in satellite-delivery learning situations used to be successful. (Author/AEF)

  4. 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…

  5. Learning to adapt: Dynamics of readaptation to geometrical distortions.

    PubMed

    Yehezkel, Oren; Sagi, Dov; Sterkin, Anna; Belkin, Michael; Polat, Uri

    2010-07-21

    The visual system can adapt to optical blur, whereby the adapted image is perceived as sharp. Here we show that adaptation reduces blur-induced biases in shape perception, with repeated adaptations (perceptual learning), leading to unbiased perception upon re-exposure to blur. Observers wore a cylindrical lens of +1.00 D on one eye, thus simulating monocular astigmatism. The other eye was either masked with a translucent blurred lens (monocular) or unmasked (dichoptic). Adaptation was tested in several repeated sessions with a proximity-grouping task, using horizontally or vertically arranged dot-arrays, without feedback, before, after, and throughout the adaptation period. A robust bias in global-orientation judgment was observed with the lens, in accordance with the blur axes. After the observer wore the lens for 2 h, there was no significant change in the bias, but after 4 h, the monocular condition, but not the dichoptic, resulted in reduced bias. The adaptation effect of the monocular 4-h adaptation was preserved, and even improved, when the lens was re-applied the next day, indicating learning. After-effects were observed under all experimental conditions except for the 4-h monocular condition, where learning took place. We suggest that, with long experience, adaptation is transferred to a long-term memory that can be instantly engaged when blur is re-applied, or disengaged when blur is removed, thus leaving no after-effects. The comparison between the monocular and dichoptic conditions indicates a binocular cortical site of plasticity.

  6. Teachers' Adaptive Instruction Supporting Students' Literacy Learning

    ERIC Educational Resources Information Center

    Vaughn, Margaret; Parsons, Seth A.; Gallagher, Melissa A.; Branen, Jeneille

    2016-01-01

    Adaptive teaching is an instructional approach where differences among learners are clearly recognized. For the last decade, our research team has studied literacy teachers' instructional adaptations in numerous classrooms in different regions of the United States. In this article, we share conclusions and insights from this longitudinal research.…

  7. Fast but fleeting: adaptive motor learning processes associated with aging and cognitive decline.

    PubMed

    Trewartha, Kevin M; Garcia, Angeles; Wolpert, Daniel M; Flanagan, J Randall

    2014-10-01

    Motor learning has been shown to depend on multiple interacting learning processes. For example, learning to adapt when moving grasped objects with novel dynamics involves a fast process that adapts and decays quickly-and that has been linked to explicit memory-and a slower process that adapts and decays more gradually. Each process is characterized by a learning rate that controls how strongly motor memory is updated based on experienced errors and a retention factor determining the movement-to-movement decay in motor memory. Here we examined whether fast and slow motor learning processes involved in learning novel dynamics differ between younger and older adults. In addition, we investigated how age-related decline in explicit memory performance influences learning and retention parameters. Although the groups adapted equally well, they did so with markedly different underlying processes. Whereas the groups had similar fast processes, they had different slow processes. Specifically, the older adults exhibited decreased retention in their slow process compared with younger adults. Within the older group, who exhibited considerable variation in explicit memory performance, we found that poor explicit memory was associated with reduced retention in the fast process, as well as the slow process. These findings suggest that explicit memory resources are a determining factor in impairments in the both the fast and slow processes for motor learning but that aging effects on the slow process are independent of explicit memory declines.

  8. 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.

  9. On Development of an Adaptive Tutoring System for Calculus Learning

    NASA Astrophysics Data System (ADS)

    Yokota, Hisashi

    2010-06-01

    One-on-one tutoring is known to be an effective model for learning calculus. Therefore, implementing one-on-one tutoring system into calculus learning software is a natural thing to do. The purpose of this article is to describe how to diagnose a students' knowledge structure about calculus without asking many questions and to show how an adaptive tutoring system is implemented into our calculus learning software JCALC.

  10. 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.

  11. Using assistive technology adaptations to include students with learning disabilities in cooperative learning activities.

    PubMed

    Bryant, D P; Bryant, B R

    1998-01-01

    Cooperative learning (CL) is a common instructional arrangement that is used by classroom teachers to foster academic achievement and social acceptance of students with and without learning disabilities. Cooperative learning is appealing to classroom teachers because it can provide an opportunity for more instruction and feedback by peers than can be provided by teachers to individual students who require extra assistance. Recent studies suggest that students with LD may need adaptations during cooperative learning activities. The use of assistive technology adaptations may be necessary to help some students with LD compensate for their specific learning difficulties so that they can engage more readily in cooperative learning activities. A process for integrating technology adaptations into cooperative learning activities is discussed in terms of three components: selecting adaptations, monitoring the use of the adaptations during cooperative learning activities, and evaluating the adaptations' effectiveness. The article concludes with comments regarding barriers to and support systems for technology integration, technology and effective instructional practices, and the need to consider technology adaptations for students who have learning disabilities.

  12. Temporal Learning Analytics for Adaptive Assessment

    ERIC Educational Resources Information Center

    Papamitsiou, Zacharoula; Economides, Anastasios A.

    2014-01-01

    Accurate and early predictions of student performance could significantly affect interventions during teaching and assessment, which gradually could lead to improved learning outcomes. In our research, we seek to identify and formalize temporal parameters as predictors of performance ("temporal learning analytics" or TLA) and examine…

  13. Achieving Adaptability through Inquiry Based Learning

    DTIC Science & Technology

    2010-06-01

    knowledge. IBL is based on a different conception of learning, one traceable back to John Dewey (1910) and Jean Piaget (1972; von Glasersfeld, 1995) and...Dewey, 1910; Duffy 2009; Piaget , 1972; Schank, Fano, Bell, and Jona, 1993). If the learners are focused on figuring out what the instructor wants...errors or the inability to fully make sense of a situation provides the basis for learning ( Piaget , 1973; Schank, et al, 1993). Thus the errors

  14. 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.

  15. 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.

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

    PubMed Central

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

    2015-01-01

    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

  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. Learning to Adapt to Asymmetric Threats

    DTIC Science & Technology

    2005-08-01

    14. Gresham, Frank M., and Stephen N. Elliot , “The Relationship Between Adaptive Behavior and Social Skills: Issues in Definition and Assessment...Foundation of US Hegemony,” International Security, Vol. 28, No. 1 Summer 2003, pp 5–46. Quinn , Robert E., “Building the Bridge as you Walk on it

  19. Strategies for adapting to high rates of employee turnover.

    PubMed

    Mowday, R T

    1984-01-01

    For many organizations facing high rates of employee turnover, strategies for increasing employee retention may not be practical because employees leave for reasons beyond the control of management or the costs of reducing turnover exceed the benefits to be derived. In this situation managers need to consider strategies that can minimize or buffer the organization from the negative consequences that often follow from turnover. Strategies organizations can use to adapt to uncontrollably high employee turnover rates are presented in this article. In addition, suggestions are made for how managers should make choices among the alternative strategies.

  20. 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.

  1. Cooperative Learning Groups and the Evolution of Human Adaptability : (Another Reason) Why Hermits Are Rare in Tonga and Elsewhere.

    PubMed

    Bell, Adrian Viliami; Hernandez, Daniel

    2017-03-01

    Understanding the prevalence of adaptive culture in part requires understanding the dynamics of learning. Here we explore the adaptive value of social learning in groups and how formal social groups function as effective mediums of information exchange. We discuss the education literature on Cooperative Learning Groups (CLGs), which outlines the potential of group learning for enhancing learning outcomes. Four qualities appear essential for CLGs to enhance learning: (1) extended conversations, (2) regular interactions, (3) gathering of experts, and (4) incentives for sharing knowledge. We analyze these four qualities within the context of a small-scale agricultural society using data we collected in 2010 and 2012. Through an analysis of surveys, interviews, and observations in the Tongan islands, we describe the role CLGs likely plays in facilitating individuals' learning of adaptive information. Our analysis of group affiliation, membership, and topics of conversation suggest that the first three CLG qualities reflect conditions for adaptive learning in groups. We utilize ethnographic anecdotes to suggest the fourth quality is also conducive to adaptive group learning. Using an evolutionary model, we further explore the scope for CLGs outside the Tongan socioecological context. Model analysis shows that environmental volatility and migration rates among human groups mediate the scope for CLGs. We call for wider attention to how group structure facilitates learning in informal settings, which may be key to assessing the contribution of groups to the evolution of complex, adaptive culture.

  2. Saccade adaptation abnormalities implicate dysfunction of cerebellar-dependent learning mechanisms in Autism Spectrum Disorders (ASD).

    PubMed

    Mosconi, Matthew W; Luna, Beatriz; Kay-Stacey, Margaret; Nowinski, Caralynn V; Rubin, Leah H; Scudder, Charles; Minshew, Nancy; Sweeney, John A

    2013-01-01

    The cerebellar vermis (lobules VI-VII) has been implicated in both postmortem and neuroimaging studies of autism spectrum disorders (ASD). This region maintains the consistent accuracy of saccadic eye movements and plays an especially important role in correcting systematic errors in saccade amplitudes such as those induced by adaptation paradigms. Saccade adaptation paradigms have not yet been used to study ASD. Fifty-six individuals with ASD and 53 age-matched healthy controls performed an intrasaccadic target displacement task known to elicit saccadic adaptation reflected in an amplitude reduction. The rate of amplitude reduction and the variability of saccade amplitude across 180 adaptation trials were examined. Individuals with ASD adapted slower than healthy controls, and demonstrated more variability of their saccade amplitudes across trials prior to, during and after adaptation. Thirty percent of individuals with ASD did not significantly adapt, whereas only 6% of healthy controls failed to adapt. Adaptation rate and amplitude variability impairments were related to performance on a traditional neuropsychological test of manual motor control. The profile of impaired adaptation and reduced consistency of saccade accuracy indicates reduced neural plasticity within learning circuits of the oculomotor vermis that impedes the fine-tuning of motor behavior in ASD. These data provide functional evidence of abnormality in the cerebellar vermis that converges with previous reports of cellular and gross anatomic dysmorphology of this brain region in ASD.

  3. Human hyolaryngeal movements show adaptive motor learning during swallowing.

    PubMed

    Humbert, Ianessa A; Christopherson, Heather; Lokhande, Akshay; German, Rebecca; Gonzalez-Fernandez, Marlis; Celnik, Pablo

    2013-06-01

    The hyoid bone and larynx elevate to protect the airway during swallowing. However, it is unknown whether hyolaryngeal movements during swallowing can adjust and adapt to predict the presence of a persistent perturbation in a feed-forward manner (adaptive motor learning). We investigated adaptive motor learning in nine healthy adults. Electrical stimulation was administered to the anterior neck to reduce hyolaryngeal elevation, requiring more strength to swallow during the perturbation period of this study. We assessed peak hyoid bone and laryngeal movements using videofluoroscopy across thirty-five 5-ml water swallows. Evidence of adaptive motor learning of hyolaryngeal movements was found when (1) participants showed systematic gradual increases in elevation against the force of electrical stimulation and (2) hyolaryngeal elevation overshot the baseline (preperturbation) range of motion, showing behavioral aftereffects, when the perturbation was unexpectedly removed. Hyolaryngeal kinematics demonstrates adaptive, error-reducing movements in the presence of changing and unexpected demands. This is significant because individuals with dysphagia often aspirate due to disordered hyolaryngeal movements. Thus, if rapid motor learning is accessible during swallowing in healthy adults, patients may be taught to predict the presence of perturbations and reduce errors in swallowing before they occur.

  4. 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…

  5. Soft systems thinking and social learning for adaptive management.

    PubMed

    Cundill, G; Cumming, G S; Biggs, D; Fabricius, C

    2012-02-01

    The success of adaptive management in conservation has been questioned and the objective-based management paradigm on which it is based has been heavily criticized. Soft systems thinking and social-learning theory expose errors in the assumption that complex systems can be dispassionately managed by objective observers and highlight the fact that conservation is a social process in which objectives are contested and learning is context dependent. We used these insights to rethink adaptive management in a way that focuses on the social processes involved in management and decision making. Our approach to adaptive management is based on the following assumptions: action toward a common goal is an emergent property of complex social relationships; the introduction of new knowledge, alternative values, and new ways of understanding the world can become a stimulating force for learning, creativity, and change; learning is contextual and is fundamentally about practice; and defining the goal to be addressed is continuous and in principle never ends. We believe five key activities are crucial to defining the goal that is to be addressed in an adaptive-management context and to determining the objectives that are desirable and feasible to the participants: situate the problem in its social and ecological context; raise awareness about alternative views of a problem and encourage enquiry and deconstruction of frames of reference; undertake collaborative actions; and reflect on learning.

  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

    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.

  8. 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

  9. Motor learning cannot explain stuttering adaptation.

    PubMed

    Venkatagiri, Horabail S; Nataraja, Nuggehalli P; Deepthi, M

    2013-08-01

    When persons who stutter (PWS) read a text repeatedly, there is a progressive reduction in stutter frequency over the course of three to five readings. Recently, this phenomenon has been attributed by some researchers to motor learning-the acquisition of relatively permanent motor skills that facilitate fluency through practice in producing words. The current study tested this explanation. 23 PWS read prose passages five times in succession. The number of 'new' and 'old' stutters during repeated readings (words stuttered in the current reading but spoken fluently in the previous reading and words stuttered also in the previous reading) were analyzed. If motor learning facilitated fluency during repeated readings in PWS, words read fluently in a reading should not be stuttered in a later reading in significant numbers. Contrary to this prediction, there was no statistical difference in the number of new words stuttered across five readings. A plausible alternative explanation, which requires further study to verify, is offered.

  10. Adaptive EZW coding using a rate-distortion criterion

    NASA Astrophysics Data System (ADS)

    Yin, Che-Yi

    2001-07-01

    This work presents a new method that improves on the EZW image coding algorithm. The standard EZW image coder uses a uniform quantizer with a threshold (deadzone) that is identical in all subbands. The quantization step sizes are not optimized under the rate-distortion sense. We modify the EZW by applying the Lagrange multiplier to search for the best step size for each subband and allocate the bit rate for each subband accordingly. Then we implement the adaptive EZW codec to code the wavelet coefficients. Two coding environments, independent and dependent, are considered for the optimization process. The proposed image coder retains all the good features of the EZW, namely, embedded coding, progressive transmission, order of the important bits, and enhances it through the rate-distortion optimization with respect to the step sizes.

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

    PubMed Central

    Brudner, Samuel N.; Kethidi, Nikhit; Graeupner, Damaris; Ivry, Richard B.

    2016-01-01

    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

  12. 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.

  13. 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.

  14. Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws.

    PubMed

    Chairez, Isaac

    2016-04-05

    This paper addresses the design of a normalized convergent learning law for neural networks (NNs) with continuous dynamics. The NN is used here to obtain a nonparametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties is the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on normalized algorithms was used to adjust the weights of the NN. The adaptive algorithm was derived by means of a nonstandard logarithmic Lyapunov function (LLF). Two identifiers were designed using two variations of LLFs leading to a normalized learning law for the first identifier and a variable gain normalized learning law. In the case of the second identifier, the inclusion of normalized learning laws yields to reduce the size of the convergence region obtained as solution of the practical stability analysis. On the other hand, the velocity of convergence for the learning laws depends on the norm of errors in inverse form. This fact avoids the peaking transient behavior in the time evolution of weights that accelerates the convergence of identification error. A numerical example demonstrates the improvements achieved by the algorithm introduced in this paper compared with classical schemes with no-normalized continuous learning methods. A comparison of the identification performance achieved by the no-normalized identifier and the ones developed in this paper shows the benefits of the learning law proposed in this paper.

  15. Overseas Students' Intercultural Adaptation as Intercultural Learning: A Transformative Framework

    ERIC Educational Resources Information Center

    Gill, Scherto

    2007-01-01

    In the context of increasing recruitment of overseas students by British higher education (HE) institutions, there has been a growing need to understand the process of students' intercultural adaptation and the approaches that can be adopted by British academic institutions in order to facilitate and support these students' learning experience in…

  16. 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…

  17. 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.…

  18. ELCAT: An E-Learning Content Adaptation Toolkit

    ERIC Educational Resources Information Center

    Clements, Iain; Xu, Zhijie

    2005-01-01

    Purpose: The purpose of this paper is to present an e-learning content adaptation toolkit--ELCAT--that helps to achieve the objectives of the KTP project No. 3509. Design/methodology/approach: The chosen methodology is absolutely practical. The tool was put into motion and results were observed as university and the collaborating company members…

  19. Educational Software and Adaptive Technology for Students with Learning Disabilities.

    ERIC Educational Resources Information Center

    Payne, Mario D.; Sachs, Rose

    Technological solutions have enabled postsecondary students with learning disabilities to compete equally with nondisabled peers in the educational environment. Such solutions have included a variety of educational software, word processing applications, and adaptive technology. Educational software has many benefits over more traditional…

  20. 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…

  1. 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…

  2. Learning Rate Differences for Knowledge, Comprehension and Application Tasks.

    ERIC Educational Resources Information Center

    Lyon, Mark A.

    Many previous studies have documented the importance of time needed for learning and time spent in learning as parameters of educational achievement. However, most of this research has not considered the different types of tasks students are asked to learn. This study examined differences in student learning rates, amount of information acquired,…

  3. 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.

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

    PubMed Central

    Likhtik, Ekaterina; Paz, Rony

    2015-01-01

    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 mPFCBLA circuit during encoding, expression and extinction of adaptive learning are reviewed. The mechanisms whereby deficient mPFC-BLA interactions can lead to generalized fear are discussed in learned and innate anxiety. Findings with crossspecies validity are emphasized. PMID:25583269

  5. 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…

  6. 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.

  7. Attention modulates adaptive motor learning in the 'broken escalator' paradigm.

    PubMed

    Patel, Mitesh; Kaski, Diego; Bronstein, Adolfo M

    2014-07-01

    The physical stumble caused by stepping onto a stationary (broken) escalator represents a locomotor aftereffect (LAE) that attests to a process of adaptive motor learning. Whether such learning is primarily explicit (requiring attention resources) or implicit (independent of attention) is unknown. To address this question, we diverted attention in the adaptation (MOVING) and aftereffect (AFTER) phases of the LAE by loading these phases with a secondary cognitive task (sequential naming of a vegetable, fruit and a colour). Thirty-six healthy adults were randomly assigned to 3 equally sized groups. They performed 5 trials stepping onto a stationary sled (BEFORE), 5 with the sled moving (MOVING) and 5 with the sled stationary again (AFTER). A 'Dual-Task-MOVING (DTM)' group performed the dual-task in the MOVING phase and the 'Dual-Task-AFTEREFFECT (DTAE)' group in the AFTER phase. The 'control' group performed no dual task. We recorded trunk displacement, gait velocity and gastrocnemius muscle EMG of the left (leading) leg. The DTM, but not the DTAE group, had larger trunk displacement during the MOVING phase, and a smaller trunk displacement aftereffect compared with controls. Gait velocity was unaffected by the secondary cognitive task in either group. Thus, adaptive locomotor learning involves explicit learning, whereas the expression of the aftereffect is automatic (implicit). During rehabilitation, patients should be actively encouraged to maintain maximal attention when learning new or challenging locomotor tasks.

  8. Adaptive learning by extremal dynamics and negative feedback

    SciTech Connect

    Bak, Per; Chialvo, Dante R.

    2001-03-01

    We describe a mechanism for biological learning and adaptation based on two simple principles: (i) Neuronal activity propagates only through the network's strongest synaptic connections (extremal dynamics), and (ii) the strengths of active synapses are reduced if mistakes are made, otherwise no changes occur (negative feedback). The balancing of those two tendencies typically shapes a synaptic landscape with configurations which are barely stable, and therefore highly flexible. This allows for swift adaptation to new situations. Recollection of past successes is achieved by punishing synapses which have once participated in activity associated with successful outputs much less than neurons that have never been successful. Despite its simplicity, the model can readily learn to solve complicated nonlinear tasks, even in the presence of noise. In particular, the learning time for the benchmark parity problem scales algebraically with the problem size N, with an exponent k{approx}1.4.

  9. 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.

  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. 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…

  13. Adaptation for Regularization Operators in Learning Theory

    DTIC Science & Technology

    2006-09-10

    the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data ...sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden...constants r, s, Cr and Ds) on the unknown probability measure ρ. Unlabelled data are added to the training set in order to improve the rates for a

  14. 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,…

  15. 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

  16. 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.

  17. Applying perceptual and adaptive learning techniques for teaching introductory histopathology

    PubMed Central

    Krasne, Sally; Hillman, Joseph D.; Kellman, Philip J.; Drake, Thomas A.

    2013-01-01

    Background: Medical students are expected to master the ability to interpret histopathologic images, a difficult and time-consuming process. A major problem is the issue of transferring information learned from one example of a particular pathology to a new example. Recent advances in cognitive science have identified new approaches to address this problem. Methods: We adapted a new approach for enhancing pattern recognition of basic pathologic processes in skin histopathology images that utilizes perceptual learning techniques, allowing learners to see relevant structure in novel cases along with adaptive learning algorithms that space and sequence different categories (e.g. diagnoses) that appear during a learning session based on each learner's accuracy and response time (RT). We developed a perceptual and adaptive learning module (PALM) that utilized 261 unique images of cell injury, inflammation, neoplasia, or normal histology at low and high magnification. Accuracy and RT were tracked and integrated into a “Score” that reflected students rapid recognition of the pathologies and pre- and post-tests were given to assess the effectiveness. Results: Accuracy, RT and Scores significantly improved from the pre- to post-test with Scores showing much greater improvement than accuracy alone. Delayed post-tests with previously unseen cases, given after 6-7 weeks, showed a decline in accuracy relative to the post-test for 1st-year students, but not significantly so for 2nd-year students. However, the delayed post-test scores maintained a significant and large improvement relative to those of the pre-test for both 1st and 2nd year students suggesting good retention of pattern recognition. Student evaluations were very favorable. Conclusion: A web-based learning module based on the principles of cognitive science showed an evidence for improved recognition of histopathology patterns by medical students. PMID:24524000

  18. 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

  19. 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.

  20. Learning multiple visuomotor transformations: adaptation and context-dependent recall.

    PubMed

    Mistry, Sima; Contreras-Vidal, Jose L

    2004-10-01

    Recent motor control theories suggest that the brain uses internal models to plan and control accurate movements. An internal model is thought to represent how the biomechanics of the arm interacting with the outside world would respond to a motor command; therefore it can be seen as a predictive model of the reafference that helps the system plan ahead. Moreover, adaptation studies show that humans can learn multiple internal models. It is not clear, however, whether and how contextual cues are used to switch among competing internal models, which are required to compensate for altered environments. To investigate this question, we asked healthy participants to perform center-out pointing movements under normal and distorted visual feedback (0 degrees , 30 degrees counterclockwise, and 60 degrees clockwise rotation of hand-screen cursor relationships) conditions. The results suggest that humans can learn multiple environments simultaneously and can use contextual cues to facilitate adaptation and to recall the appropriate internal model of the visuomotor transformation.

  1. A Context-Adaptive Teacher Training Model in a Ubiquitous Learning Environment

    ERIC Educational Resources Information Center

    Chen, Min; Chiang, Feng Kuang; Jiang, Ya Na; Yu, Sheng Quan

    2017-01-01

    In view of the discrepancies in teacher training and teaching practice, this paper put forward a context-adaptive teacher training model in a ubiquitous learning (u-learning) environment. The innovative model provides teachers of different subjects with adaptive and personalized learning content in a u-learning environment, implements intra- and…

  2. 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…

  3. 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…

  4. 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.

  5. Adaptive de-blocking filter for low bit rate applications

    NASA Astrophysics Data System (ADS)

    Jin, Xin; Zhu, Guangxi

    2006-01-01

    In block-based video compression technology, blocking artifacts are obvious because of the luminance and chrominance discontinuities which are caused by block-based discrete cosine transform (DCT) and motion compensation. As a kind of solution, an in-loop filter has been successfully used in H.264 adapting to quantization parameter and video content. In this paper, blocking artifacts distribution properties are analyzed carefully to reflect the blocking effect more accurately in the low bit rate applications. Two important parameters, named blocking severity and pixel variation, are defined to describe the boundary strength and the gradient of the samples across the edge respectively. Through series of statistical data retrieval and analysis for these parameters using multiple representative video sequences, a novel blocking artifacts distribution model is concluded. Based on this distribution model, an improved filter is proposed to H.264 with novel strength determination rule and different alpha model. Comparing with H.264 anchor results, the proposed de-blocking filter shows better performance especially in subjective aspect, which could be widely used in low bit rate applications.

  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. 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.…

  10. Solar adaptive optics: specificities, lessons learned, and open alternatives

    NASA Astrophysics Data System (ADS)

    Montilla, I.; Marino, J.; Asensio Ramos, A.; Collados, M.; Montoya, L.; Tallon, M.

    2016-07-01

    the Strehl and the Point Spread Function used in night time adaptive optics but not really suitable to the solar systems, and new control strategies more complex than the ones used in nowadays solar Multi Conjugate Adaptive Optics systems. In this paper we summarize the lessons learned with past and current solar adaptive optics systems and focus on the discussion on the new alternatives to solve present open issues limiting their performance.

  11. Prey behaviour across antipredator adaptation types: how does growth trajectory influence learning of predators?

    PubMed

    Ferrari, Maud C O; Brown, Grant E; Bortolotti, Gary R; Chivers, Douglas P

    2011-11-01

    Despite the fact that the ability of animals to avoid being consumed by predators is influenced by their behaviour, morphology and life history, very few studies have attempted to integrate prey responses across these adaptation types. Here, our goal was to address the link between life-history traits (size and growth trajectory) of tadpoles and behavioural responses to predators. Specifically, we wanted to determine whether information learned about predators was influenced by prey growth trajectory before and after learning. We manipulated the size/growth trajectory of tadpoles by raising them under different temperatures. Tadpoles raised on a slow-growth trajectory (under cold conditions) and taught to recognize a salamander subsequently showed stronger responses after 2 weeks than tadpoles that were raised on a fast-growth trajectory (under warm conditions). When we account for the effect of size (r (2)=0.22) on the responses of prey to predator cues, we find that the growth trajectory pre-learning but not post-learning influences the learned responses of the tadpoles. The differences in responses to predators may reflect differential memory associated with the predator. To our knowledge, this is the first study that has attempted to link life-history traits (size and growth rate) with learning of predators. In order to gain a comprehensive understanding of antipredator responses of prey animals, we call for additional integrative studies that examine prey anti-predator responses across adaptation types.

  12. A New Approach of an Intelligent E-Learning System Based on Learners' Skill Level and Learners' Success Rate

    ERIC Educational Resources Information Center

    Mohamed, Hafidi; Lamia, Mahnane

    2015-01-01

    Learners usually meet cognitive overload and disorientation problems when using e-learning system. At present, most of the studies in e-learning either concentrate on the technological aspect or focus on adapting learner's interests or browsing behaviors, while, learner's skill level and learners' success rate is usually neglected. In this paper,…

  13. 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.

  14. 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.

  15. 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.

  16. Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

    NASA Astrophysics Data System (ADS)

    Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

    2014-06-01

    Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳104). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

  17. Efficient retrieval of landscape Hessian: forced optimal covariance adaptive learning.

    PubMed

    Shir, Ofer M; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

    2014-06-01

    Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳10^{4}). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

  18. 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…

  19. Dynamic Learner Profiling and Automatic Learner Classification for Adaptive E-Learning Environment

    ERIC Educational Resources Information Center

    Premlatha, K. R.; Dharani, B.; Geetha, T. V.

    2016-01-01

    E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…

  20. Algebraic and adaptive learning in neural control systems

    NASA Astrophysics Data System (ADS)

    Ferrari, Silvia

    A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.

  1. 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…

  2. Older adults learn less, but still reduce metabolic cost, during motor adaptation.

    PubMed

    Huang, Helen J; Ahmed, Alaa A

    2014-01-01

    The ability to learn new movements and dynamics is important for maintaining independence with advancing age. Age-related sensorimotor changes and increased muscle coactivation likely alter the trial-and-error-based process of adapting to new movement demands (motor adaptation). Here, we asked, to what extent is motor adaptation to novel dynamics maintained in older adults (≥65 yr)? We hypothesized that older adults would adapt to the novel dynamics less well than young adults. Because older adults often use muscle coactivation, we expected older adults to use greater muscle coactivation during motor adaptation than young adults. Nevertheless, we predicted that older adults would reduce muscle activity and metabolic cost with motor adaptation, similar to young adults. Seated older (n = 11, 73.8 ± 5.6 yr) and young (n = 15, 23.8 ± 4.7 yr) adults made targeted reaching movements while grasping a robotic arm. We measured their metabolic rate continuously via expired gas analysis. A force field was used to add novel dynamics. Older adults had greater movement deviations and compensated for just 65% of the novel dynamics compared with 84% in young adults. As expected, older adults used greater muscle coactivation than young adults. Last, older adults reduced muscle activity with motor adaptation and had consistent reductions in metabolic cost later during motor adaptation, similar to young adults. These results suggest that despite increased muscle coactivation, older adults can adapt to the novel dynamics, albeit less accurately. These results also suggest that reductions in metabolic cost may be a fundamental feature of motor adaptation.

  3. Older adults learn less, but still reduce metabolic cost, during motor adaptation

    PubMed Central

    Huang, Helen J.

    2013-01-01

    The ability to learn new movements and dynamics is important for maintaining independence with advancing age. Age-related sensorimotor changes and increased muscle coactivation likely alter the trial-and-error-based process of adapting to new movement demands (motor adaptation). Here, we asked, to what extent is motor adaptation to novel dynamics maintained in older adults (≥65 yr)? We hypothesized that older adults would adapt to the novel dynamics less well than young adults. Because older adults often use muscle coactivation, we expected older adults to use greater muscle coactivation during motor adaptation than young adults. Nevertheless, we predicted that older adults would reduce muscle activity and metabolic cost with motor adaptation, similar to young adults. Seated older (n = 11, 73.8 ± 5.6 yr) and young (n = 15, 23.8 ± 4.7 yr) adults made targeted reaching movements while grasping a robotic arm. We measured their metabolic rate continuously via expired gas analysis. A force field was used to add novel dynamics. Older adults had greater movement deviations and compensated for just 65% of the novel dynamics compared with 84% in young adults. As expected, older adults used greater muscle coactivation than young adults. Last, older adults reduced muscle activity with motor adaptation and had consistent reductions in metabolic cost later during motor adaptation, similar to young adults. These results suggest that despite increased muscle coactivation, older adults can adapt to the novel dynamics, albeit less accurately. These results also suggest that reductions in metabolic cost may be a fundamental feature of motor adaptation. PMID:24133222

  4. Adaptive sampling for learning gaussian processes using mobile sensor networks.

    PubMed

    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.

  5. 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.

  6. 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

  7. 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.

  8. 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…

  9. Adaptive and learning control of large space structures

    NASA Technical Reports Server (NTRS)

    Montgomery, R. C.; Thau, F. J.

    1980-01-01

    The paper describes the adaptive learning system for space operations which assumes that structural testing can be conducted during deployment and assembly. Simulation results using the solar electric propulsion array and a novel remote sensor are presented; they involve faster scan television coverage of the motions of the array from four cameras on the corners of the Space Shuttle payload bay. The description of the simulation, the filtering algorithm for processing the TV data, the parameter extraction algorithm, and the simulation results are presented.

  10. Adaptive dictionary learning in sparse gradient domain for image recovery.

    PubMed

    Liu, Qiegen; Wang, Shanshan; Ying, Leslie; Peng, Xi; Zhu, Yanjie; Liang, Dong

    2013-12-01

    Image recovery from undersampled data has always been challenging due to its implicit ill-posed nature but becomes fascinating with the emerging compressed sensing (CS) theory. This paper proposes a novel gradient based dictionary learning method for image recovery, which effectively integrates the popular total variation (TV) and dictionary learning technique into the same framework. Specifically, we first train dictionaries from the horizontal and vertical gradients of the image and then reconstruct the desired image using the sparse representations of both derivatives. The proposed method enables local features in the gradient images to be captured effectively, and can be viewed as an adaptive extension of the TV regularization. The results of various experiments on MR images consistently demonstrate that the proposed algorithm efficiently recovers images and presents advantages over the current leading CS reconstruction approaches.

  11. 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…

  12. Adaptive and Energy Efficient Walking in a Hexapod Robot Under Neuromechanical Control and Sensorimotor Learning.

    PubMed

    Xiong, Xiaofeng; Worgotter, Florentin; Manoonpong, Poramate

    2016-11-01

    The control of multilegged animal walking is a neuromechanical process, and to achieve this in an adaptive and energy efficient way is a difficult and challenging problem. This is due to the fact that this process needs in real time: 1) to coordinate very many degrees of freedom of jointed legs; 2) to generate the proper leg stiffness (i.e., compliance); and 3) to determine joint angles that give rise to particular positions at the endpoints of the legs. To tackle this problem for a robotic application, here we present a neuromechanical controller coupled with sensorimotor learning. The controller consists of a modular neural network for coordinating 18 joints and several virtual agonist-antagonist muscle mechanisms (VAAMs) for variable compliant joint motions. In addition, sensorimotor learning, including forward models and dual-rate learning processes, is introduced for predicting foot force feedback and for online tuning the VAAMs' stiffness parameters. The control and learning mechanisms enable the hexapod robot advanced mobility sensor driven-walking device (AMOS) to achieve variable compliant walking that accommodates different gaits and surfaces. As a consequence, AMOS can perform more energy efficient walking, compared to other small legged robots. In addition, this paper also shows that the tight combination of neural control with tunable muscle-like functions, guided by sensory feedback and coupled with sensorimotor learning, is a way forward to better understand and solve adaptive coordination problems in multilegged locomotion.

  13. Adaptive information retrieval: machine learning in associate networks

    SciTech Connect

    Belew, R.K.

    1986-01-01

    One interesting issue in artificial intelligence (Al) currently is the relative merits of, and relationship between, the symbolic and connectionist approaches to intelligent systems building. The performance of more-traditional symbolic systems has been striking, but getting these systems to learn truly new symbols has proven difficult. Recently, some researchers have begun to explore a distinctly different type of representation, similar in some respects to the nerve nets of several decades past. In these massively parallel, connectionist models, symbols arise implicitly, through the interactions of many simple and subsymbolic elements. The work described here was done in two phases. The first phase concentrated on mapping the information retrieval (IR) task into a connectionist network; it is shown that IR is very amendable to this representation. The second, more central phase of the research has shown that this network can also adapt. AIR translates the browsing behaviors of its users into a feedback signal used by a Hebbian-like local learning rule to change the weights on some links. Experience with a series of alternative learning rules are reported, and the results of experiments using human subjects to evaluate the results of AIR's learning are presented.

  14. Blind Domain Adaptation With Augmented Extreme Learning Machine Features.

    PubMed

    Uzair, Muhammad; Mian, Ajmal

    2016-02-11

    In practical applications, the test data often have different distribution from the training data leading to suboptimal visual classification performance. Domain adaptation (DA) addresses this problem by designing classifiers that are robust to mismatched distributions. Existing DA algorithms use the unlabeled test data from target domain during training time in addition to the source domain data. However, target domain data may not always be available for training. We propose a blind DA algorithm that does not require target domain samples for training. For this purpose, we learn a global nonlinear extreme learning machine (ELM) model from the source domain data in an unsupervised fashion. The global ELM model is then used to initialize and learn class specific ELM models from the source domain data. During testing, the target domain features are augmented with the reconstructed features from the global ELM model. The resulting enriched features are then classified using the class specific ELM models based on minimum reconstruction error. Extensive experiments on 16 standard datasets show that despite blind learning, our method outperforms six existing state-of-the-art methods in cross domain visual recognition.

  15. Chromophore Supply Rate-Limits Mammalian Photoreceptor Dark Adaptation

    PubMed Central

    Wang, Jin-shan; Nymark, Soile; Frederiksen, Rikard; Estevez, Maureen E.; Shen, Susan Q.; Corbo, Joseph C.; Cornwall, M. Carter

    2014-01-01

    Efficient regeneration of visual pigment following its destruction by light is critical for the function of mammalian photoreceptors. Here, we show that misexpression of a subset of cone genes in the rd7 mouse hybrid rods enables them to access the normally cone-specific retina visual cycle. The rapid supply of chromophore by the retina visual cycle dramatically accelerated the mouse rod dark adaptation. At the same time, the competition between rods and cones for retina-derived chromophore slowed cone dark adaptation, indicating that the cone specificity of the retina visual cycle is key for rapid cone dark adaptation. Our findings demonstrate that mammalian photoreceptor dark adaptation is dominated by the supply of chromophore. Misexpression of cone genes in rods may represent a novel approach to treating visual disorders associated with mutations of visual cycle proteins or with reduced retinal pigment epithelium function due to aging. PMID:25143602

  16. 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…

  17. 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.

  18. Employment of Adaptive Learning Techniques for the Discrimination of Acoustic Emissions.

    DTIC Science & Technology

    1983-11-01

    8D-1Ai38 142 EMPLOYMENT OP ADAPTIVE LEARNING TECHNIQUES FOR THE I DISCRIMINATION OF ACOU..(U) GENERAL ELECTRIC CORPORATE U Ch, RESEARCH AND...OFSTNDRD-96- 1.5%. 111 11 :%____ 111. %I1~.~ 11 1 - 111 -- k. -Jr -. P. -L -. b. EMPLOYMENT OF ADAPTIVE LEARNING TECHNIQUESEli FOR THE DISCRIMINATION OF...8217Include Security Claaaaficatiano Employment of Adaptive * Learning Techniques for the Discrimination Of Acoustic Emissions (Unclassified) 12.’ PE SNAU.R S

  19. Can the structure of motor variability predict learning rate?

    PubMed

    Barbado Murillo, David; Caballero Sánchez, Carla; Moreside, Janice; Vera-García, Francisco J; Moreno, Francisco J

    2017-03-01

    Recent studies show that motor variability is actively regulated as an exploration tool to promote learning in reward-based tasks. However, its role in learning processes during error-based tasks, when a reduction of the motor variability is required to achieve good performance, is still unclear. In this study, we hypothesized that error-based learning not only depends on exploration but also on the individuals' ability to measure and predict the motor error. Previous studies identified a less auto-correlated motor variability as a higher ability to perform motion adjustments. Two experiments investigated the relationship between motor learning and variability, analyzing the long-range autocorrelation of the center of pressure fluctuations through the α score of a Detrended Fluctuation Analysis in balance tasks. In Experiment 1, we assessed the relationship between variability and learning rate using a standing balance task. Based on the results of this experiment, and to maximize learning, we performed a second experiment with a more difficult sitting balance task and increased practice. The learning rate of the 2 groups with similar balance performances but different α scores was compared. Individuals with a lower α score showed a higher learning rate. Because the α scores reveal how the motor output changes over time, instead of the magnitude of those changes, the higher learning rate is mainly linked to the higher error sensitivity rather than the exploration strategies. The results of this study highlight the relevance of the structure of output motor variability as a predictor of learning rate in error-based tasks. (PsycINFO Database Record

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

    PubMed

    Liu, Zitao; Hauskrecht, Milos

    2016-02-01

    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.

  1. 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

  2. Providing QoS through machine-learning-driven adaptive multimedia applications.

    PubMed

    Ruiz, Pedro M; Botía, Juan A; Gómez-Skarmeta, Antonio

    2004-06-01

    We investigate the optimization of the quality of service (QoS) offered by real-time multimedia adaptive applications through machine learning algorithms. These applications are able to adapt in real time their internal settings (i.e., video sizes, audio and video codecs, among others) to the unpredictably changing capacity of the network. Traditional adaptive applications just select a set of settings to consume less than the available bandwidth. We propose a novel approach in which the selected set of settings is the one which offers a better user-perceived QoS among all those combinations which satisfy the bandwidth restrictions. We use a genetic algorithm to decide when to trigger the adaptation process depending on the network conditions (i.e., loss-rate, jitter, etc.). Additionally, the selection of the new set of settings is done according to a set of rules which model the user-perceived QoS. These rules are learned using the SLIPPER rule induction algorithm over a set of examples extracted from scores provided by real users. We will demonstrate that the proposed approach guarantees a good user-perceived QoS even when the network conditions are constantly changing.

  3. Active learning: effects of core training design elements on self-regulatory processes, learning, and adaptability.

    PubMed

    Bell, Bradford S; Kozlowski, Steve W J

    2008-03-01

    This article describes a comprehensive examination of the cognitive, motivational, and emotional processes underlying active learning approaches; their effects on learning and transfer; and the core training design elements (exploration, training frame, emotion control) and individual differences (cognitive ability, trait goal orientation, trait anxiety) that shape these processes. Participants (N = 350) were trained to operate a complex, computer-based simulation. Exploratory learning and error-encouragement framing had a positive effect on adaptive transfer performance and interacted with cognitive ability and dispositional goal orientation to influence trainees' metacognition and state goal orientation. Trainees who received the emotion-control strategy had lower levels of state anxiety. Implications for development of an integrated theory of active learning, learner-centered design, and research extensions are discussed.

  4. 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…

  5. Independent Ratings of Institutions of Higher Learning

    ERIC Educational Resources Information Center

    Russian Education and Society, 2007

    2007-01-01

    The ReitOR Agency has for the first time posted its ratings of colleges and universities in the Volga Federal District of Russia based on criteria of public assessment, having designated institutions in the following priority areas of training: machine building, energy, communications and telecommunications, management and economics, and gas and…

  6. 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

  7. 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…

  8. A Complex Adaptive Perspective on Learning within Innovation Projects

    ERIC Educational Resources Information Center

    Harkema, Saskia

    2003-01-01

    Innovation is the lifeblood of companies, while simultaneously being one of the most difficult and elusive processes to manage. Failure rates are high--varying between six out of ten to nine out of ten--while the need to innovate is high. Departing from a real-life case of a company, Sara Lee/Douwe Egberts, that has set learning within and from…

  9. A Self-Driven and Adaptive Adjusting Teaching Learning Method for Optimizing Optical Multicast Network Throughput

    NASA Astrophysics Data System (ADS)

    Liu, Huanlin; Xu, Yifan; Chen, Yong; Zhang, Mingjia

    2016-09-01

    With the development of one point to multiple point applications, network resources become scarcer and wavelength channels become more crowded in optical networks. To improve the bandwidth utilization, the multicast routing algorithm based on network coding can greatly increase the resource utilization, but it is most difficult to maximize the network throughput owing to ignoring the differences between the multicast receiving nodes. For making full use of the destination nodes' receives ability to maximize optical multicast's network throughput, a new optical multicast routing algorithm based on teaching-learning-based optimization (MR-iTLBO) is proposed in the paper. In order to increase the diversity of learning, a self-driven learning method is adopted in MR-iTLBO algorithm, and the mutation operator of genetic algorithm is introduced to prevent the algorithm into a local optimum. For increasing learner's learning efficiency, an adaptive learning factor is designed to adjust the learning process. Moreover, the reconfiguration scheme based on probability vector is devised to expand its global search capability in MR-iTLBO algorithm. The simulation results show that performance in terms of network throughput and convergence rate has been improved significantly with respect to the TLBO and the variant TLBO.

  10. 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…

  11. Visual discrimination and adaptation using non-linear unsupervised learning

    NASA Astrophysics Data System (ADS)

    Jiménez, Sandra; Laparra, Valero; Malo, Jesus

    2013-03-01

    Understanding human vision not only involves empirical descriptions of how it works, but also organization principles that explain why it does so. Identifying the guiding principles of visual phenomena requires learning algorithms to optimize specific goals. Moreover, these algorithms have to be flexible enough to account for the non-linear and adaptive behavior of the system. For instance, linear redundancy reduction transforms certainly explain a wide range of visual phenomena. However, the generality of this organization principle is still in question:10 it is not only that and additional constraints such as energy cost may be relevant as well, but also, statistical independence may not be the better solution to make optimal inferences in squared error terms. Moreover, linear methods cannot account for the non-uniform discrimination in different regions of the image and color space: linear learning methods necessarily disregard the non-linear nature of the system. Therefore, in order to account for the non-linear behavior, principled approaches commonly apply the trick of using (already non-linear) parametric expressions taken from empirical models. Therefore these approaches are not actually explaining the non-linear behavior, but just fitting it to image statistics. In summary, a proper explanation of the behavior of the system requires flexible unsupervised learning algorithms that (1) are tunable to different, perceptually meaningful, goals; and (2) make no assumption on the non-linearity. Over the last years we have worked on these kind of learning algorithms based on non-linear ICA,18 Gaussianization, 19 and principal curves. In this work we stress the fact that these methods can be tuned to optimize different design strategies, namely statistical independence, error minimization under quantization, and error minimization under truncation. Then, we show (1) how to apply these techniques to explain a number of visual phenomena, and (2) suggest the

  12. 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).

  13. 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…

  14. 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.

  15. 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).

  16. 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…

  17. 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.

  18. Blind separation of image sources via adaptive dictionary learning.

    PubMed

    Abolghasemi, Vahid; Ferdowsi, Saideh; Sanei, Saeid

    2012-06-01

    Sparsity has been shown to be very useful in source separation of multichannel observations. However, in most cases, the sources of interest are not sparse in their current domain and one needs to sparsify them using a known transform or dictionary. If such a priori about the underlying sparse domain of the sources is not available, then the current algorithms will fail to successfully recover the sources. In this paper, we address this problem and attempt to give a solution via fusing the dictionary learning into the source separation. We first define a cost function based on this idea and propose an extension of the denoising method in the work of Elad and Aharon to minimize it. Due to impracticality of such direct extension, we then propose a feasible approach. In the proposed hierarchical method, a local dictionary is adaptively learned for each source along with separation. This process improves the quality of source separation even in noisy situations. In another part of this paper, we explore the possibility of adding global priors to the proposed method. The results of our experiments are promising and confirm the strength of the proposed approach.

  19. 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…

  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. 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…

  2. 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…

  3. 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…

  4. Recasting Transfer as a Socio-Personal Process of Adaptable Learning

    ERIC Educational Resources Information Center

    Billett, Stephen

    2013-01-01

    Transfer is usually cast as an educational, rather than learning, problem. Yet, seeking to adapt what individuals know from one circumstance to another is a process more helpfully associated with learning, than a hybrid one called transfer. Adaptability comprises individuals construing what they experience, then aligning and reconciling with what…

  5. 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…

  6. 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…

  7. Applications of Adaptive Learning Controller to Synthetic Aperture Radar.

    DTIC Science & Technology

    1985-02-01

    FIGURE 37. Location of Two Sub- Phase Histories to be Utilized in Estimating Misfocus Coefficients A and C . . . A8 FIGURES 38.-94. ALC Learning Curves ...FIGURES (Concl uded) FIGURE 23. ALC Learning Curve .... .................. ... 45 .- " FIGURE 24. ALC Learning Curve ......... ................. 47 FIGURE...25. ALC Learning Curve .... .................. ... 48 FIGURE 26. ALC Learning Curve ....... .... ... .... 50 FIGURE 27. ALC Learning Curve

  8. Getting Ready for Mobile Learning--Adaptation Perspective

    ERIC Educational Resources Information Center

    Goh, Tiong; Kinshuk

    2006-01-01

    Emerging from e-learning, mobile learning is going to be a significant next wave of learning environments. This is an evolving research area and many issues regarding mobile learning have not yet been exhaustively covered. This article focuses on implementing m-learning modules using a simple case study. Most existing typical e-learning systems…

  9. OPUS One: An Intelligent Adaptive Learning Environment Using Artificial Intelligence Support

    NASA Astrophysics Data System (ADS)

    Pedrazzoli, Attilio

    2010-06-01

    AI based Tutoring and Learning Path Adaptation are well known concepts in e-Learning scenarios today and increasingly applied in modern learning environments. In order to gain more flexibility and to enhance existing e-learning platforms, the OPUS One LMS Extension package will enable a generic Intelligent Tutored Adaptive Learning Environment, based on a holistic Multidimensional Instructional Design Model (PENTHA ID Model), allowing AI based tutoring and adaptation functionality to existing Web-based e-learning systems. Relying on "real time" adapted profiles, it allows content- / course authors to apply a dynamic course design, supporting tutored, collaborative sessions and activities, as suggested by modern pedagogy. The concept presented combines a personalized level of surveillance, learning activity- and learning path adaptation suggestions to ensure the students learning motivation and learning success. The OPUS One concept allows to implement an advanced tutoring approach combining "expert based" e-tutoring with the more "personal" human tutoring function. It supplies the "Human Tutor" with precise, extended course activity data and "adaptation" suggestions based on predefined subject matter rules. The concept architecture is modular allowing a personalized platform configuration.

  10. 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…

  11. Bayesian active learning of neural firing rate maps with transformed gaussian process priors.

    PubMed

    Park, Mijung; Weller, J Patrick; Horwitz, Gregory D; Pillow, Jonathan W

    2014-08-01

    A firing rate map, also known as a tuning curve, describes the nonlinear relationship between a neuron's spike rate and a low-dimensional stimulus (e.g., orientation, head direction, contrast, color). Here we investigate Bayesian active learning methods for estimating firing rate maps in closed-loop neurophysiology experiments. These methods can accelerate the characterization of such maps through the intelligent, adaptive selection of stimuli. Specifically, we explore the manner in which the prior and utility function used in Bayesian active learning affect stimulus selection and performance. Our approach relies on a flexible model that involves a nonlinearly transformed gaussian process (GP) prior over maps and conditionally Poisson spiking. We show that infomax learning, which selects stimuli to maximize the information gain about the firing rate map, exhibits strong dependence on the seemingly innocuous choice of nonlinear transformation function. We derive an alternate utility function that selects stimuli to minimize the average posterior variance of the firing rate map and analyze the surprising relationship between prior parameterization, stimulus selection, and active learning performance in GP-Poisson models. We apply these methods to color tuning measurements of neurons in macaque primary visual cortex.

  12. 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…

  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. 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…

  16. 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…

  17. 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…

  18. Self-Regulation Strategies and Technologies for Adaptive Learning Management Systems for Web-based Instruction

    ERIC Educational Resources Information Center

    Heo, Heeok; Joung, Sunyoung

    2004-01-01

    The current study identifies the potential problems of current web-based instruction and learning management systems in terms of its lack of flexibility and customization required for individual learners? different goals, backgrounds, knowledge levels, and learning capabilities. Advanced adaptive learning management system technologies are able to…

  19. 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…

  20. Quantifying Rates of Evolutionary Adaptation in Response to Ocean Acidification

    PubMed Central

    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 CO2 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 CO2 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. PMID:21857962

  1. MATE: Machine Learning for Adaptive Calibration Template Detection

    PubMed Central

    Donné, Simon; De Vylder, Jonas; Goossens, Bart; Philips, Wilfried

    2016-01-01

    The problem of camera calibration is two-fold. On the one hand, the parameters are estimated from known correspondences between the captured image and the real world. On the other, these correspondences themselves—typically in the form of chessboard corners—need to be found. Many distinct approaches for this feature template extraction are available, often of large computational and/or implementational complexity. We exploit the generalized nature of deep learning networks to detect checkerboard corners: our proposed method is a convolutional neural network (CNN) trained on a large set of example chessboard images, which generalizes several existing solutions. The network is trained explicitly against noisy inputs, as well as inputs with large degrees of lens distortion. The trained network that we evaluate is as accurate as existing techniques while offering improved execution time and increased adaptability to specific situations with little effort. The proposed method is not only robust against the types of degradation present in the training set (lens distortions, and large amounts of sensor noise), but also to perspective deformations, e.g., resulting from multi-camera set-ups. PMID:27827920

  2. MATE: Machine Learning for Adaptive Calibration Template Detection.

    PubMed

    Donné, Simon; De Vylder, Jonas; Goossens, Bart; Philips, Wilfried

    2016-11-04

    The problem of camera calibration is two-fold. On the one hand, the parameters are estimated from known correspondences between the captured image and the real world. On the other, these correspondences themselves-typically in the form of chessboard corners-need to be found. Many distinct approaches for this feature template extraction are available, often of large computational and/or implementational complexity. We exploit the generalized nature of deep learning networks to detect checkerboard corners: our proposed method is a convolutional neural network (CNN) trained on a large set of example chessboard images, which generalizes several existing solutions. The network is trained explicitly against noisy inputs, as well as inputs with large degrees of lens distortion. The trained network that we evaluate is as accurate as existing techniques while offering improved execution time and increased adaptability to specific situations with little effort. The proposed method is not only robust against the types of degradation present in the training set (lens distortions, and large amounts of sensor noise), but also to perspective deformations, e.g., resulting from multi-camera set-ups.

  3. 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.

  4. 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…

  5. Evaluating the Impact of Adaptation to Learning Styles in a Web-Based Educational System

    NASA Astrophysics Data System (ADS)

    Popescu, Elvira

    Measuring the effect of providing educational experiences individualized to the learning style of the students is an open research issue. This paper aims at presenting a case study of a dedicated adaptive educational system called WELSA. First, the adaptation logic, methods and techniques employed in WELSA are briefly presented. Next, the validity and effectiveness of the system are assessed by means of an empirical evaluation approach, involving two experiments with 64 undergraduate students. The results obtained (in terms of learner behavior, performance, efficiency and satisfaction) are analyzed and discussed. The overall results of the experimental study indicate a positive effect of adaptation to learning styles on the learning process.

  6. Physically distributed learning: adapting and reinterpreting physical environments in the development of fraction concepts.

    PubMed

    Martin, Taylor; Schwartz, Daniel L

    2005-07-08

    Five studies examined how interacting with the physical environment can support the development of fraction concepts. Nine- and 10-year-old children worked on fraction problems they could not complete mentally. Experiments 1 and 2 showed that manipulating physical pieces facilitated children's ability to develop an interpretation of fractions. Experiment 3 demonstrated that when children understood a content area well, they used their interpretations to repurpose many environments to support problem solving, whereas when they needed to learn, they were prone to the structure of the environment. Experiments 4 and 5 examined transfer after children had learned by manipulating physical pieces. Children who learned by adapting relatively unstructured environments transferred to new materials better than children who learned with "well-structured" environments that did not require equivalent adaptation. Together, the findings reveal that during physically distributed learning, the opportunity to adapt an environment permits the development of new interpretations that can advance learning.

  7. 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…

  8. Masters of Adaptation: Learning in Late Life Adjustments

    ERIC Educational Resources Information Center

    Roberson, Jr., Donald N.

    2005-01-01

    The purpose of this research is to understand the relationship between human development in older adults and personal learning. Personal or self-directed learning (SDL) refers to a style of learning where the individual directs, controls, and evaluates what is learned. It may occur with formal classes, but most often takes place in non-formal…

  9. 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…

  10. 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…

  11. Multi-Agent Reinforcement Learning and Adaptive Neural Networks.

    DTIC Science & Technology

    2007-11-02

    learning method. The objective was to study the utility of reinforcement learning as an approach to complex decentralized control problems. The major...accomplishment was a detailed study of multi-agent reinforcement learning applied to a large-scale decentralized stochastic control problem. This study...included a very successful demonstration that a multi-agent reinforcement learning system using neural networks could learn high-performance

  12. 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.

  13. Adaptation paths to novel motor tasks are shaped by prior structure learning.

    PubMed

    Kobak, Dmitry; Mehring, Carsten

    2012-07-18

    After extensive practice with motor tasks sharing structural similarities (e.g., different dancing movements, or different sword techniques), new tasks of the same type can be learned faster. According to the recent "structure learning" hypothesis (Braun et al., 2009a), such rapid generalization of related motor skills relies on learning the dynamic and kinematic relationships shared by this set of skills. As a consequence, motor adaptation becomes constrained, effectively leading to a dimensionality reduction of the learning problem; at the same time, adaptation to tasks lying outside the structure becomes biased toward the structure. We tested these predictions by investigating how previously learned structures influence subsequent motor adaptation. Human subjects were making reaching movements in 3D virtual reality, experiencing perturbations either in the vertical or in the horizontal plane. Perturbations were either visuomotor rotations of varying angle or velocity-dependent forces of varying strength. We found that, after extensive training with both kinematic or dynamic perturbations, adaptation to unpracticed, diagonal, perturbations happened along the previously learned structure (vertical or horizontal), and resulting adaptation trajectories were curved. This effect is robust, can be observed on the single-subject level, and occurs during adaptation both within and across trials. Additionally, we demonstrate that structure learning changes involuntary visuomotor reflexes and therefore is not exclusively a high-level cognitive phenomenon.

  14. Adaptive online monitoring for ICU patients by combining just-in-time learning and principal component analysis.

    PubMed

    Li, Xuejian; Wang, Youqing

    2016-12-01

    Offline general-type models are widely used for patients' monitoring in intensive care units (ICUs), which are developed by using past collected datasets consisting of thousands of patients. However, these models may fail to adapt to the changing states of ICU patients. Thus, to be more robust and effective, the monitoring models should be adaptable to individual patients. A novel combination of just-in-time learning (JITL) and principal component analysis (PCA), referred to learning-type PCA (L-PCA), was proposed for adaptive online monitoring of patients in ICUs. JITL was used to gather the most relevant data samples for adaptive modeling of complex physiological processes. PCA was used to build an online individual-type model and calculate monitoring statistics, and then to judge whether the patient's status is normal or not. The adaptability of L-PCA lies in the usage of individual data and the continuous updating of the training dataset. Twelve subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and five vital signs of each subject were chosen. The proposed method was compared with the traditional PCA and fast moving-window PCA (Fast MWPCA). The experimental results demonstrated that the fault detection rates respectively increased by 20 % and 47 % compared with PCA and Fast MWPCA. L-PCA is first introduced into ICU patients monitoring and achieves the best monitoring performance in terms of adaptability to changes in patient status and sensitivity for abnormality detection.

  15. 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.

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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.

  1. Tracking of multiple targets using online learning for reference model adaptation.

    PubMed

    Pernkopf, Franz

    2008-12-01

    Recently, much work has been done in multiple object tracking on the one hand and on reference model adaptation for a single-object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and online learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e., face color, face size, and object movement. Many methods unlike our approach assume that the target region has been initialized by hand in the first frame. For tracking, a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of the reference model learning during tracking of indoor and outdoor scenes which supports a more robust tracking. Therefore, we report the average of the standard deviation of the trajectories over numerous tracking runs depending on the learning rate.

  2. Computerized Dynamic Adaptive Tests with Immediately Individualized Feedback for Primary School Mathematics Learning

    ERIC Educational Resources Information Center

    Wu, Huey-Min; Kuo, Bor-Chen; Wang, Su-Chen

    2017-01-01

    In this study, a computerized dynamic assessment test with both immediately individualized feedback and adaptively property was applied to Mathematics learning in primary school. For evaluating the effectiveness of the computerized dynamic adaptive test, the performances of three types of remedial instructions were compared by a pre-test/post-test…

  3. 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…

  4. 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).…

  5. 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,…

  6. Examining the Impact of Adaptively Faded Worked Examples on Student Learning Outcomes

    ERIC Educational Resources Information Center

    Flores, Raymond; Inan, Fethi

    2014-01-01

    The purpose of this study was to explore effective ways to design guided practices within a web-based mathematics problem solving tutorial. Specifically, this study examined student learning outcome differences between two support designs (e.g. adaptively faded and fixed). In the adaptively faded design, students were presented with problems in…

  7. An Open IMS-Based User Modelling Approach for Developing Adaptive Learning Management Systems

    ERIC Educational Resources Information Center

    Boticario, Jesus G.; Santos, Olga C.

    2007-01-01

    Adaptive LMS have not yet reached the eLearning marketplace due to methodological, technological and management open issues. At aDeNu group, we have been working on two key challenges for the last five years in related research projects. Firstly, develop the general framework and a running architecture to support the adaptive life cycle (i.e.,…

  8. 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…

  9. 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…

  10. 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

  11. Adaptive FEM with coarse initial mesh guarantees optimal convergence rates for compactly perturbed elliptic problems

    NASA Astrophysics Data System (ADS)

    Bespalov, Alex; Haberl, Alexander; Praetorius, Dirk

    2017-04-01

    We prove that for compactly perturbed elliptic problems, where the corresponding bilinear form satisfies a Garding inequality, adaptive mesh-refinement is capable of overcoming the preasymptotic behavior and eventually leads to convergence with optimal algebraic rates. As an important consequence of our analysis, one does not have to deal with the a-priori assumption that the underlying meshes are sufficiently fine. Hence, the overall conclusion of our results is that adaptivity has stabilizing effects and can overcome possibly pessimistic restrictions on the meshes. In particular, our analysis covers adaptive mesh-refinement for the finite element discretization of the Helmholtz equation from where our interest originated.

  12. 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…

  13. 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…

  14. 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…

  15. 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.…

  16. 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))…

  17. 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.…

  18. 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…

  19. 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. Three…

  20. 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…

  1. 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.

  2. 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…

  3. Adaptive User Model for Web-Based Learning Environment.

    ERIC Educational Resources Information Center

    Garofalakis, John; Sirmakessis, Spiros; Sakkopoulos, Evangelos; Tsakalidis, Athanasios

    This paper describes the design of an adaptive user model and its implementation in an advanced Web-based Virtual University environment that encompasses combined and synchronized adaptation between educational material and well-known communication facilities. The Virtual University environment has been implemented to support a postgraduate…

  4. 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…

  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. Improving Lethal Action: Learning and Adapting in U.S. Counterterrorism Operations

    DTIC Science & Technology

    2014-09-01

    learned process to ensure that it is learning and adapting its counterterrorism operations for maximum success. Yet, at least publically, this...appears to not be the case. The lack of such a process is compounded by the fact that both the conduct and oversight of these operations are divided among...analytic framework and lessons-learned process that the U.S. government could—and should—use to continually and comprehensively improve the

  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. Adaptive low-power listening MAC protocol based on transmission rates.

    PubMed

    Hwang, Kwang-il; Yi, Gangman

    2014-01-01

    Even though existing low-power listening (LPL) protocols have enabled ultra-low-power operation in wireless sensor networks (WSN), they do not address trade-off between energy and delay, since they focused only on energy aspect. However, in recent years, a growing interest in various WSN applications is requiring new design factors, such as minimum delay and higher reliability, as well as energy efficiency. Therefore, in this paper we propose a novel sensor multiple access control (MAC) protocol, transmission rate based adaptive low-power listening MAC protocol (TRA-MAC), which is a kind of preamble-based LPL but is capable of controlling preamble sensing cycle adaptively to transmission rates. Through experiments, it is demonstrated that TRA-MAC enables LPL cycle (LC) and preamble transmission length to adapt dynamically to varying transmission rates, compensating trade-off between energy and response time.

  9. 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

  10. 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

  11. Specialized hybrid learners resolve Rogers' paradox about the adaptive value of social learning.

    PubMed

    Kharratzadeh, Milad; Montrey, Marcel; Metz, Alex; Shultz, Thomas R

    2017-02-07

    Culture is considered an evolutionary adaptation that enhances reproductive fitness. A common explanation is that social learning, the learning mechanism underlying cultural transmission, enhances mean fitness by avoiding the costs of individual learning. This explanation was famously contradicted by Rogers (1988), who used a simple mathematical model to show that cheap social learning can invade a population without raising its mean fitness. He concluded that some crucial factor remained unaccounted for, which would reverse this surprising result. Here we extend this model to include a more complex environment and limited resources, where individuals cannot reliably learn everything about the environment on their own. Under such conditions, cheap social learning evolves and enhances mean fitness, via hybrid learners capable of specializing their individual learning. We then show that while spatial or social constraints hinder the evolution of hybrid learners, a novel social learning strategy, complementary copying, can mitigate these effects.

  12. Microstimulation of the midbrain tegmentum creates learning signals for saccade adaptation.

    PubMed

    Kojima, Yoshiko; Yoshida, Kaoru; Iwamoto, Yoshiki

    2007-04-04

    Error signals are vital to motor learning. However, we know little about pathways that transmit error signals for learning in voluntary movements. Here we show that microstimulation of the midbrain tegmentum can induce learning in saccadic eye movements in monkeys. Weak electrical stimuli delivered approximately 200 ms after saccades in one horizontal direction produced gradual and marked changes in saccade gain. The spatial and temporal characteristics of the produced changes were similar to those of adaptation induced by real visual error. When stimulation was applied after saccades in two different directions, endpoints of these saccades gradually shifted in the same direction in two dimensions. We conclude that microstimulation created powerful learning signals that dictate the direction of adaptive shift in movement endpoints. Our findings suggest that the error signals for saccade adaptation are conveyed in a pathway that courses through the midbrain tegmentum.

  13. Adapting Web-based Instruction to Residents’ Knowledge Improves Learning Efficiency

    PubMed Central

    Beckman, Thomas J.; Thomas, Kris G.; Thompson, Warren G.

    2008-01-01

    Summary BACKGROUND Increased clinical demands and decreased available time accentuate the need for efficient learning in postgraduate medical training. Adapting Web-based learning (WBL) to learners’ prior knowledge may improve efficiency. OBJECTIVE We hypothesized that time spent learning would be shorter and test scores not adversely affected for residents who used a WBL intervention that adapted to prior knowledge. DESIGN Randomized, crossover trial. SETTING Academic internal medicine residency program continuity clinic. PARTICIPANTS 122 internal medicine residents. INTERVENTIONS Four WBL modules on ambulatory medicine were developed in standard and adaptive formats. The adaptive format allowed learners who correctly answered case-based questions to skip the corresponding content. MEASUREMENTS and Main Results The measurements were knowledge posttest, time spent on modules, and format preference. One hundred twenty-two residents completed at least 1 module, and 111 completed all 4. Knowledge scores were similar between the adaptive format (mean ± standard error of the mean, 76.2 ± 0.9) and standard (77.2 ± 0.9, 95% confidence interval [CI] for difference −3.0 to 1.0, P = .34). However, time spent was lower for the adaptive format (29.3 minutes [CI 26.0 to 33.0] per module) than for the standard (35.6 [31.6 to 40.3]), an 18% decrease in time (CI 9 to 26%, P = .0003). Seventy-two of 96 respondents (75%) preferred the adaptive format. CONCLUSIONS Adapting WBL to learners’ prior knowledge can reduce learning time without adversely affecting knowledge scores, suggesting greater learning efficiency. In an era where reduced duty hours and growing clinical demands on trainees and faculty limit the time available for learning, such efficiencies will be increasingly important. For clinical trial registration, see http://www.clinicaltrials.gov NCT00466453 (http://www.clinicaltrials.gov/ct/show/NCT00466453?order=1). PMID:18612729

  14. 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…

  15. Complex Mobile Learning That Adapts to Learners' Cognitive Load

    ERIC Educational Resources Information Center

    Deegan, Robin

    2015-01-01

    Mobile learning is cognitively demanding and frequently the ubiquitous nature of mobile computing means that mobile devices are used in cognitively demanding environments. This paper examines the use of mobile devices from a Learning, Usability and Cognitive Load Theory perspective. It suggests scenarios where these fields interact and presents an…

  16. 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…

  17. 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.

  18. Learning Beyond the Buzzwords: Developing the Adaptable, Competent CSS Soldier

    DTIC Science & Technology

    2007-11-02

    current and past knowledge. Jean Piaget , one of the major influencers of Constructivist theory, proposed that learning occurs through an interaction...schema. Learning results from a balanced tension between these two processes. 25 Piaget believed that intelligence is shaped by experience, and

  19. 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

    2013-07-18

    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.

  20. 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.

  1. 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 ...

  2. 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…

  3. A dissociation between consolidated perceptual learning and sensory adaptation in vision

    PubMed Central

    Censor, Nitzan; Harris, Hila; Sagi, Dov

    2016-01-01

    Perceptual learning refers to improvement in perception thresholds with practice, however, extended training sessions show reduced performance during training, interfering with learning. These effects were taken to indicate a tight link between sensory adaptation and learning. Here we show a dissociation between adaptation and consolidated learning. Participants trained with a texture discrimination task, in which visual processing time is limited by a temporal target-to-mask window defined as the Stimulus-Onset-Asynchrony (SOA). An initial training phase, previously shown to produce efficient learning, was followed by training structures with varying numbers of SOAs. Largest interference with learning was found in structures containing the largest SOA density, when SOA was gradually decreased. When SOAs were largely kept unchanged, learning was effective. All training structures yielded the same within-session performance reduction, as expected from sensory adaptation. The results point to a dissociation between within-day effects, which depend on the number of trials per se regardless of their temporal structure, and consolidation effects observed on the following day, which were mediated by the temporal structure of practice. These results add a new dimension to consolidation in perceptual learning, suggesting that the degree of its effectiveness depends on variations in temporal properties of the visual stimuli. PMID:27982045

  4. Bit Rate Maximising Per-Tone Equalisation with Adaptive Implementation for DMT-Based Systems

    NASA Astrophysics Data System (ADS)

    Sitjongsataporn, Suchada; Yuvapoositanon, Peerapol

    2009-12-01

    We present a bit rate maximising per-tone equalisation (BM-PTEQ) cost function that is based on an exact subchannel SNR as a function of per-tone equaliser in discrete multitone (DMT) systems. We then introduce the proposed BM-PTEQ criterion whose derivation for solution is shown to inherit from the methodology of the existing bit rate maximising time-domain equalisation (BM-TEQ). By solving a nonlinear BM-PTEQ cost function, an adaptive BM-PTEQ approach based on a recursive Levenberg-Marquardt (RLM) algorithm is presented with the adaptive inverse square-root (iQR) algorithm for DMT-based systems. Simulation results confirm that the performance of the proposed adaptive iQR RLM-based BM-PTEQ converges close to the performance of the proposed BM-PTEQ. Moreover, the performance of both these proposed BM-PTEQ algorithms is improved as compared with the BM-TEQ.

  5. Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation.

    PubMed

    Zhang, Lei; Zhang, David

    2016-08-10

    We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.

  6. Flexible explicit but rigid implicit learning in a visuomotor adaptation task.

    PubMed

    Bond, Krista M; Taylor, Jordan A

    2015-06-01

    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.

  7. 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.

  8. 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.

  9. On the nature of cultural transmission networks: evidence from Fijian villages for adaptive learning biases.

    PubMed

    Henrich, Joseph; Broesch, James

    2011-04-12

    Unlike other animals, humans are heavily dependent on cumulative bodies of culturally learned information. Selective processes operating on this socially learned information can produce complex, functionally integrated, behavioural repertoires-cultural adaptations. To understand such non-genetic adaptations, evolutionary theorists propose that (i) natural selection has favoured the emergence of psychological biases for learning from those individuals most likely to possess adaptive information, and (ii) when these psychological learning biases operate in populations, over generations, they can generate cultural adaptations. Many laboratory experiments now provide evidence for these psychological biases. Here, we bridge from the laboratory to the field by examining if and how these biases emerge in a small-scale society. Data from three cultural domains-fishing, growing yams and using medicinal plants-show that Fijian villagers (ages 10 and up) are biased to learn from others perceived as more successful/knowledgeable, both within and across domains (prestige effects). We also find biases for sex and age, as well as proximity effects. These selective and centralized oblique transmission networks set up the conditions for adaptive cultural evolution.

  10. On the nature of cultural transmission networks: evidence from Fijian villages for adaptive learning biases

    PubMed Central

    Henrich, Joseph; Broesch, James

    2011-01-01

    Unlike other animals, humans are heavily dependent on cumulative bodies of culturally learned information. Selective processes operating on this socially learned information can produce complex, functionally integrated, behavioural repertoires—cultural adaptations. To understand such non-genetic adaptations, evolutionary theorists propose that (i) natural selection has favoured the emergence of psychological biases for learning from those individuals most likely to possess adaptive information, and (ii) when these psychological learning biases operate in populations, over generations, they can generate cultural adaptations. Many laboratory experiments now provide evidence for these psychological biases. Here, we bridge from the laboratory to the field by examining if and how these biases emerge in a small-scale society. Data from three cultural domains—fishing, growing yams and using medicinal plants—show that Fijian villagers (ages 10 and up) are biased to learn from others perceived as more successful/knowledgeable, both within and across domains (prestige effects). We also find biases for sex and age, as well as proximity effects. These selective and centralized oblique transmission networks set up the conditions for adaptive cultural evolution. PMID:21357236

  11. 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.

  12. Cerebellar contributions to reach adaptation and learning sensory consequences of action.

    PubMed

    Izawa, Jun; Criscimagna-Hemminger, Sarah E; Shadmehr, Reza

    2012-03-21

    When we use a novel tool, the motor commands may not produce the expected outcome. In healthy individuals, with practice the brain learns to alter the motor commands. This change depends critically on the cerebellum as damage to this structure impairs adaptation. However, it is unclear precisely what the cerebellum contributes to the process of adaptation in human motor learning. Is the cerebellum crucial for learning to associate motor commands with novel sensory consequences, called forward model, or is the cerebellum important for learning to associate sensory goals with novel motor commands, called inverse model? Here, we compared performance of cerebellar patients and healthy controls in a reaching task with a gradual perturbation schedule. This schedule allowed both groups to adapt their motor commands. Following training, we measured two kinds of behavior: in one case, people were presented with reach targets near the direction in which they had trained. The resulting generalization patterns of patients and controls were similar, suggesting comparable inverse models. In the second case, participants reached without a target and reported the location of their hand. In controls, the pattern of change in reported hand location was consistent with simulation results of a forward model that had learned to associate motor commands with new sensory consequences. In patients, this change was significantly smaller. Therefore, in our sample of patients, we observed that while adaptation of motor commands can take place despite cerebellar damage, cerebellar integrity appears critical for learning to predict visual sensory consequences of motor commands.

  13. Iterative learning-based decentralized adaptive tracker for large-scale systems: a digital redesign approach.

    PubMed

    Tsai, Jason Sheng-Hong; Du, Yan-Yi; Huang, Pei-Hsiang; Guo, Shu-Mei; Shieh, Leang-San; Chen, Yuhua

    2011-07-01

    In this paper, a digital redesign methodology of the iterative learning-based decentralized adaptive tracker is proposed to improve the dynamic performance of sampled-data linear large-scale control systems consisting of N interconnected multi-input multi-output subsystems, so that the system output will follow any trajectory which may not be presented by the analytic reference model initially. To overcome the interference of each sub-system and simplify the controller design, the proposed model reference decentralized adaptive control scheme constructs a decoupled well-designed reference model first. Then, according to the well-designed model, this paper develops a digital decentralized adaptive tracker based on the optimal analog control and prediction-based digital redesign technique for the sampled-data large-scale coupling system. In order to enhance the tracking performance of the digital tracker at specified sampling instants, we apply the iterative learning control (ILC) to train the control input via continual learning. As a result, the proposed iterative learning-based decentralized adaptive tracker not only has robust closed-loop decoupled property but also possesses good tracking performance at both transient and steady state. Besides, evolutionary programming is applied to search for a good learning gain to speed up the learning process of ILC.

  14. The adaptation rate of a quantitative trait in an environmental gradient

    NASA Astrophysics Data System (ADS)

    Hermsen, R.

    2016-12-01

    The spatial range of a species habitat is generally determined by the ability of the species to cope with biotic and abiotic variables that vary in space. Therefore, the species range is itself an evolvable property. Indeed, environmental gradients permit a mode of evolution in which range expansion and adaptation go hand in hand. This process can contribute to rapid evolution of drug resistant bacteria and viruses, because drug concentrations in humans and livestock treated with antibiotics are far from uniform. Here, we use a minimal stochastic model of discrete, interacting organisms evolving in continuous space to study how the rate of adaptation of a quantitative trait depends on the steepness of the gradient and various population parameters. We discuss analytical results for the mean-field limit as well as extensive stochastic simulations. These simulations were performed using an exact, event-driven simulation scheme that can deal with continuous time-, density- and coordinate-dependent reaction rates and could be used for a wide variety of stochastic systems. The results reveal two qualitative regimes. If the gradient is shallow, the rate of adaptation is limited by dispersion and increases linearly with the gradient slope. If the gradient is steep, the adaptation rate is limited by mutation. In this regime, the mean-field result is highly misleading: it predicts that the adaptation rate continues to increase with the gradient slope, whereas stochastic simulations show that it in fact decreases with the square root of the slope. This discrepancy underscores the importance of discreteness and stochasticity even at high population densities; mean-field results, including those routinely used in quantitative genetics, should be interpreted with care.

  15. Adaptivity with near-orthogonality constraint for high compression rates in lifting scheme framework

    NASA Astrophysics Data System (ADS)

    Sliwa, Tadeusz; Voisin, Yvon; Diou, Alain

    2004-01-01

    Since few years, Lifting Scheme has proven its utility in compression field. It permits to easily create fast, reversible, separable or no, not necessarily linear, multiresolution analysis for sound, image, video or even 3D graphics. An interesting feature of lifting scheme is the ability to build adaptive transforms for compression, more easily than with other decompositions. Many works have already be done in this subject, especially in lossless or near-lossless compression framework : better compression than with usually used methods can be obtained. However, most of the techniques used in adaptive near-lossless compression can not be extended to higher lossy compression rates, even in the simplest cases. Indeed, this is due to the quantization error introduced before coding, which has not controlled propagation through inverse transform. Authors have put their interest to the classical Lifting Scheme, with linear convolution filters, but they studied criterions to maintain a high level of adaptivity and a good error propagation through inverse transform. This article aims to present relatively simple criterion to obtain filters able to build image and video compression with high compression rate, tested here with the Spiht coder. For this, upgrade and predict filters are simultaneously adapted thanks to a constrained least-square method. The constraint consists in a near-orthogonality inequality, letting sufficiently high level of adaptivity. Some compression results are given, illustrating relevance of this method, even with short filters.

  16. Effects of adaptation on biodegradation rates in sediment/water cores from estuarine and freshwater environments

    SciTech Connect

    Spain, J.C.; Pritchard, P.H.; Bourquin, A.W.

    1980-10-01

    Experiments were devised to determine whether exposure to xenobiotics would cause microbial populations to degrade the compounds more rapidly during subsequent exposures. Studies were done with water/sediment systems (ecocores) taken from a salt marsh and a river. Systems were tested for adaptation to the model compounds methyl parathion and p-nitrophenol. /sup 14/CO/sub 2/ released from radioactive parent compounds was used as a measure of mineralization. River populations preexposed to p-nitrophenol at concentrations as low as 60 ..mu..g/liter degraded the nitrophenol much faster than did control populations. River populations preexposed to methyl parathion also adapted to degrade the pesticide more rapidly, but higher concentrations were required. Salt marsh populations did not adapt to degrade methyl parathion. p-nitrophenol-degrading bacteria were isolated from river samples but not from salt marsh samples. Numbers of nitrophenol-degrading bacteria increased 4 to 5 orders of magnitude during adaptation. Results indicate that the ability of populations to adapt depends on the presence of specific microorganisms. Biodegradation rates in laboratory systems can be affected by concentration and prior exposure; therefore, adaptation must be considered when such systems are used to predict the fate of xenobiotics in the environment.

  17. Rate of Adaptation in Sexuals and Asexuals: A Solvable Model of the Fisher–Muller Effect

    PubMed Central

    Park, Su-Chan; Krug, Joachim

    2013-01-01

    The adaptation of large asexual populations is hampered by the competition between independently arising beneficial mutations in different individuals, which is known as clonal interference. In classic work, Fisher and Muller proposed that recombination provides an evolutionary advantage in large populations by alleviating this competition. Based on recent progress in quantifying the speed of adaptation in asexual populations undergoing clonal interference, we present a detailed analysis of the Fisher–Muller mechanism for a model genome consisting of two loci with an infinite number of beneficial alleles each and multiplicative (nonepistatic) fitness effects. We solve the deterministic, infinite population dynamics exactly and show that, for a particular, natural mutation scheme, the speed of adaptation in sexuals is twice as large as in asexuals. This result is argued to hold for any nonzero value of the rate of recombination. Guided by the infinite population result and by previous work on asexual adaptation, we postulate an expression for the speed of adaptation in finite sexual populations that agrees with numerical simulations over a wide range of population sizes and recombination rates. The ratio of the sexual to asexual adaptation speed is a function of population size that increases in the clonal interference regime and approaches 2 for extremely large populations. The simulations also show that the imbalance between the numbers of accumulated mutations at the two loci is strongly suppressed even by a small amount of recombination. The generalization of the model to an arbitrary number L of loci is briefly discussed. If each offspring samples the alleles at each locus from the gene pool of the whole population rather than from two parents, the ratio of the sexual to asexual adaptation speed is approximately equal to L in large populations. A possible realization of this scenario is the reassortment of genetic material in RNA viruses with L genomic

  18. 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

  19. Layer-based buffer aware rate adaptation design for SHVC video streaming

    NASA Astrophysics Data System (ADS)

    Gudumasu, Srinivas; Hamza, Ahmed; Asbun, Eduardo; He, Yong; Ye, Yan

    2016-09-01

    This paper proposes a layer based buffer aware rate adaptation design which is able to avoid abrupt video quality fluctuation, reduce re-buffering latency and improve bandwidth utilization when compared to a conventional simulcast based adaptive streaming system. The proposed adaptation design schedules DASH segment requests based on the estimated bandwidth, dependencies among video layers and layer buffer fullness. Scalable HEVC video coding is the latest state-of-art video coding technique that can alleviate various issues caused by simulcast based adaptive video streaming. With scalable coded video streams, the video is encoded once into a number of layers representing different qualities and/or resolutions: a base layer (BL) and one or more enhancement layers (EL), each incrementally enhancing the quality of the lower layers. Such layer based coding structure allows fine granularity rate adaptation for the video streaming applications. Two video streaming use cases are presented in this paper. The first use case is to stream HD SHVC video over a wireless network where available bandwidth varies, and the performance comparison between proposed layer-based streaming approach and conventional simulcast streaming approach is provided. The second use case is to stream 4K/UHD SHVC video over a hybrid access network that consists of a 5G millimeter wave high-speed wireless link and a conventional wired or WiFi network. The simulation results verify that the proposed layer based rate adaptation approach is able to utilize the bandwidth more efficiently. As a result, a more consistent viewing experience with higher quality video content and minimal video quality fluctuations can be presented to the user.

  20. 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…

  1. 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…

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

    PubMed

    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.

  3. 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

  4. 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.

  5. 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.

  6. Rate of novel host invasion affects adaptability of evolving RNA virus lineages

    PubMed Central

    Morley, Valerie J.; Mendiola, Sandra Y.; Turner, Paul E.

    2015-01-01

    Although differing rates of environmental turnover should be consequential for the dynamics of adaptive change, this idea has been rarely examined outside of theory. In particular, the importance of RNA viruses in disease emergence warrants experiments testing how differing rates of novel host invasion may impact the ability of viruses to adaptively shift onto a novel host. To test whether the rate of environmental turnover influences adaptation, we experimentally evolved 144 Sindbis virus lineages in replicated tissue-culture environments, which transitioned from being dominated by a permissive host cell type to a novel host cell type. The rate at which the novel host ‘invaded’ the environment varied by treatment. The fitness (growth rate) of evolved virus populations was measured on each host type, and molecular substitutions were mapped via whole genome consensus sequencing. Results showed that virus populations more consistently reached high fitness levels on the novel host when the novel host ‘invaded’ the environment more gradually, and gradual invasion resulted in less variable genomic outcomes. Moreover, virus populations that experienced a rapid shift onto the novel host converged upon different genotypes than populations that experienced a gradual shift onto the novel host, suggesting a strong effect of historical contingency. PMID:26246544

  7. 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.

  8. 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

  9. 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.

  10. 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…

  11. 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

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

    PubMed

    Dixon, A R; Sato, H

    2014-12-02

    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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. A Mutually Adaptive Learning Paradigm (MALP) for Hmong Students

    ERIC Educational Resources Information Center

    Marshall, Helaine W.

    1998-01-01

    Numerous studies (Goldstein, 1985; Rumbaut and Ima, 1988; Walker, 1989; Trueba, Jacobs, and Kirton, 1990 and Walker-Moffat, 1995) have found that the Hmong have extreme difficulties adjusting to the American educational system as compared with other language minority groups. Underlying this difficulty is a fundamental conflict between learning in…

  18. 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…

  19. 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…

  20. 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,…

  1. 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,…

  2. Intelligent Adaptable e-Assessment for Inclusive e-Learning

    ERIC Educational Resources Information Center

    Nacheva-Skopalik, Lilyana; Green, Steve

    2016-01-01

    Access to education is one of the main human rights. Everyone should have access to education and be capable of benefiting from it. However there are a number who are excluded, not because of a lack of ability but simply because they have a disability or specific need which current education systems do not address. A learning system in which…

  3. 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.

  4. LearnSmart, Adaptive Teaching, and Student Learning Effectiveness: An Empirical Investigation

    ERIC Educational Resources Information Center

    Sun, Qin; Abdourazakou, Yann; Norman, Thomas J.

    2017-01-01

    Facing the growing number of digital natives entering the classroom, business professors look for innovative ways to enhance the student learning experience. The authors focus on the online interactive learning tool LearnSmart (McGraw-Hill, New York, NY), and examine its impact on student learning effectiveness by testing the direct and indirect…

  5. Adaptation, Learning, and the Art of War: A Cybernetic Perspective

    DTIC Science & Technology

    2014-05-14

    M Asaro, “From Mechanisms of Adaptation to Intelligence Amplifiers: The Philosophy of W. Ross Ashby,” Mechanical Mind in History (2008): 156... Greek word kybernetes, or “steersman,” cybernetics describes goal directedness or how systems move towards and maintain their goals, while countering...without resorting to the rigid tactics and formations of the ancient phalanx or the Prussian “corpse” obedience.179 Additionally, the organization of

  6. Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring.

    PubMed

    Plews, Daniel J; Laursen, Paul B; Stanley, Jamie; Kilding, Andrew E; Buchheit, Martin

    2013-09-01

    The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how

  7. Self-adaptive sampling rate data acquisition in JET's correlation reflectometer

    SciTech Connect

    Arcas, G. de; Lopez, J. M.; Ruiz, M.; Barrera, E.; Vega, J.; Fonseca, A.

    2008-10-15

    Data acquisition systems with self-adaptive sampling rate capabilities have been proposed as a solution to reduce the shear amount of data collected in every discharge of present fusion devices. This paper discusses the design of such a system for its use in the KG8B correlation reflectometer at JET. The system, which is based on the ITMS platform, continuously adapts the sample rate during the acquisition depending on the signal bandwidth. Data are acquired continuously at the expected maximum sample rate and transferred to a memory buffer in the host processor. Thereafter the rest of the process is based on software. Data are read from the memory buffer in blocks and for each block an intelligent decimation algorithm is applied. The decimation algorithm determines the signal bandwidth for each block in order to choose the optimum sample rate for that block, and from there the decimation factor to be used. Memory buffers are used to adapt the throughput of the three main software modules (data acquisition, processing, and storage) following a typical producer-consumer architecture. The system optimizes the amount of data collected while maintaining the same information. Design issues are discussed and results of performance evaluation are presented.

  8. Peripheral electrical stimulation increases corticomotor excitability and enhances the rate of visuomotor adaptation.

    PubMed

    Summers, Simon J; Schabrun, Siobhan M; Marinovic, Welber; Chipchase, Lucy S

    2017-03-30

    Peripheral electrical stimulation (PES) modulates corticomotor excitability but its effect on motor performance has not been thoroughly investigated. The purpose of this study was to assess whether increases and/or decreases in corticomotor excitability, induced by PES, influenced motor performance using a visuomotor adaptation task. Three PES interventions (motor stimulation, sensory stimulation or sham) were delivered to the first dorsal interosseous (FDI) in 30 healthy participants matched for age, gender and handedness. Motor stimulation was applied to increase corticomotor excitability, sensory stimulation to decrease corticomotor excitability, while sham stimulation acted as a control. Corticomotor excitability was assessed using the amplitude of motor evoked potentials to transcranial magnetic stimulation recorded from FDI before and after each intervention. Following PES, participants completed a visuomotor adaptation task. This required participants to move a cursor accurately towards virtual targets with index finger movements when the cursor trajectory was rotated 30° counter clockwise. Performance was assessed as angular error (a measure of movement accuracy) and reaction time. The rate of visuomotor adaptation was greater following motor PES compared to sham, but not sensory, with no difference observed between sensory and sham. However, visuomotor adaptation performance overall (the total change in performance from beginning to end) was similar across intervention groups. These findings suggest that motor PES applied prior to task acquisition can facilitate the speed of adaptation.

  9. 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.

  10. 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.

  11. 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.

  12. Learning and Adaptive Hybrid Systems for Nonlinear Control

    DTIC Science & Technology

    1991-05-01

    34 Invention Report, S81-64, File 1, Office of Technology Liscensirig, Stanford University, 1982. [Ros62J Rosenblatt, F., Principles of Neurodynamics ...Explorations in the Microstructure of Cognition , vol. 1, Rumelhart, D., and J. McClelland, ed., MIT Press, Carbnbdge, MA, 1986. [RI-1W86] Rumnelhart, D., 0...Microstructure of Cognition , vol. 1, Rumelhart, D., and J. McClelland, ed., MIT Pres, Cambridge, MA, 1986. [Sain67] Samuel, A., "Some Studies in Machine Learning

  13. 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

  14. Robust respiration rate estimation using adaptive Kalman filtering with textile ECG sensor and accelerometer.

    PubMed

    Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi; Lepine, Nicholas N; Tajima, Takuro; Ogasawara, Takayuki; Kasahara, Ryoichi; Koizumi, Hiroshi; Koizumi, Hiroshi; Ogasawara, Takayuki; Tajima, Takuro; Kasahara, Ryoichi; Lepine, Nicholas N

    2016-08-01

    An adaptive Kalman filter-based fusion algorithm capable of estimating respiration rate for unobtrusive respiratory monitoring is proposed. Using both signal characteristics and a priori information, the Kalman filter is adaptively optimized to improve accuracy. Furthermore, the system is able to combine the respiration-related signals extracted from a textile ECG sensor and an accelerometer to create a single robust measurement. We measured derived respiratory rates and, when compared to a reference, found root-mean-square error of 2.11 breaths-per-minute (BrPM) while lying down, 2.30 BrPM while sitting, 5.97 BrPM while walking, and 5.98 BrPM while running. These results demonstrate that the proposed system is applicable to unobtrusive monitoring for various applications.

  15. Adaptive sampling rate control for networked systems based on statistical characteristics of packet disordering.

    PubMed

    Li, Jin-Na; Er, Meng-Joo; Tan, Yen-Kheng; Yu, Hai-Bin; Zeng, Peng

    2015-09-01

    This paper investigates an adaptive sampling rate control scheme for networked control systems (NCSs) subject to packet disordering. The main objectives of the proposed scheme are (a) to avoid heavy packet disordering existing in communication networks and (b) to stabilize NCSs with packet disordering, transmission delay and packet loss. First, a novel sampling rate control algorithm based on statistical characteristics of disordering entropy is proposed; secondly, an augmented closed-loop NCS that consists of a plant, a sampler and a state-feedback controller is transformed into an uncertain and stochastic system, which facilitates the controller design. Then, a sufficient condition for stochastic stability in terms of Linear Matrix Inequalities (LMIs) is given. Moreover, an adaptive tracking controller is designed such that the sampling period tracks a desired sampling period, which represents a significant contribution. Finally, experimental results are given to illustrate the effectiveness and advantages of the proposed scheme.

  16. 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…

  17. 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.

  18. 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.

  19. Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation.

    PubMed

    Lopes, J S; Arenas, M; Posada, D; Beaumont, M A

    2014-03-01

    The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.

  20. Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation

    PubMed Central

    Lopes, J S; Arenas, M; Posada, D; Beaumont, M A

    2014-01-01

    The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets. PMID:24149652

  1. Molecular and Metabolic Adaptations of Lactococcus lactis at Near-Zero Growth Rates

    PubMed Central

    Ercan, Onur; Wels, Michiel; Smid, Eddy J.

    2014-01-01

    This paper describes the molecular and metabolic adaptations of Lactococcus lactis during the transition from a growing to a near-zero growth state by using carbon-limited retentostat cultivation. Transcriptomic analyses revealed that metabolic patterns shifted between lactic- and mixed-acid fermentations during retentostat cultivation, which appeared to be controlled at the level of transcription of the corresponding pyruvate dissipation-encoding genes. During retentostat cultivation, cells continued to consume several amino acids but also produced specific amino acids, which may derive from the conversion of glycolytic intermediates. We identify a novel motif containing CTGTCAG in the upstream regions of several genes related to amino acid conversion, which we propose to be the target site for CodY in L. lactis KF147. Finally, under extremely low carbon availability, carbon catabolite repression was progressively relieved and alternative catabolic functions were found to be highly expressed, which was confirmed by enhanced initial acidification rates on various sugars in cells obtained from near-zero-growth cultures. The present integrated transcriptome and metabolite (amino acids and previously reported fermentation end products) study provides molecular understanding of the adaptation of L. lactis to conditions supporting low growth rates and expands our earlier analysis of the quantitative physiology of this bacterium at near-zero growth rates toward gene regulation patterns involved in zero-growth adaptation. PMID:25344239

  2. Environmental Adaptation, Phenotypic Plasticity, and Associative Learning in Insects: The Desert Locust as a Case Study.

    PubMed

    Simões, Patrício M V; Ott, Swidbert R; Niven, Jeremy E

    2016-11-01

    The ability to learn and store information should be adapted to the environment in which animals operate to confer a selective advantage. Yet the relationship between learning, memory, and the environment is poorly understood, and further complicated by phenotypic plasticity caused by the very environment in which learning and memory need to operate. Many insect species show polyphenism, an extreme form of phenotypic plasticity, allowing them to occupy distinct environments by producing two or more alternative phenotypes. Yet how the learning and memories capabilities of these alternative phenotypes are adapted to their specific environments remains unknown for most polyphenic insect species. The desert locust can exist as one of two extreme phenotypes or phases, solitarious and gregarious. Recent studies of associative food-odor learning in this locust have shown that aversive but not appetitive learning differs between phases. Furthermore, switching from the solitarious to the gregarious phase (gregarization) prevents locusts acquiring new learned aversions, enabling them to convert an aversive memory formed in the solitarious phase to an appetitive one in the gregarious phase. This conversion provides a neuroecological mechanism that matches key changes in the behavioral environments of the two phases. These findings emphasize the importance of understanding the neural mechanisms that generate ecologically relevant behaviors and the interactions between different forms of behavioral plasticity.

  3. Sleep benefits consolidation of visuo-motor adaptation learning in older adults.

    PubMed

    Mantua, Janna; Baran, Bengi; Spencer, Rebecca M C

    2016-02-01

    Sleep is beneficial for performance across a range of memory tasks in young adults, but whether memories are similarly consolidated in older adults is less clear. Performance benefits have been observed following sleep in older adults for declarative learning tasks, but this benefit may be reduced for non-declarative, motor skill learning tasks. To date, studies of sleep-dependent consolidation of motor learning in older adults are limited to motor sequence tasks. To examine whether reduced sleep-dependent consolidation in older adults is generalizable to other forms of motor skill learning, we examined performance changes over intervals of sleep and wake in young (n = 62) and older adults (n = 61) using a mirror-tracing task, which assesses visuo-motor adaptation learning. Participants learned the task either in the morning or in evening, and performance was assessed following a 12-h interval containing overnight sleep or daytime wake. Contrary to our prediction, both young adults and older adults exhibited sleep-dependent gains in visuo-motor adaptation. There was a correlation between performance improvement over sleep and percent of the night in non-REM stage 2 sleep. These results indicate that motor skill consolidation remains intact with increasing age although this relationship may be limited to specific forms of motor skill learning.

  4. Negotiating Service Learning through Community Engagement: Adaptive Leadership, Knowledge, Dialogue and Power

    ERIC Educational Resources Information Center

    Preece, Julia

    2016-01-01

    This article builds on two recent publications (Preece 2013; 2013a) concerning the application of asset-based community development and adaptive leadership theories when negotiating university service learning placements with community organisations in one South African province. The first publication introduced the concept of 'adaptive…

  5. Teaching-Learning Patterns of Expert and Novice Adapted Physical Educators

    ERIC Educational Resources Information Center

    Everhart, Brett; Everhart, Kim; McHugh, Heather; Newman, Chelsea Dimon; Hershey, Kacie; Lorenzi, David

    2013-01-01

    This study was intended to provide a description of teaching and learning patterns seen in the lessons taught by experts and novices in Adapted Physical Education. Two experts who had won previous state teaching awards and served in leadership positions in state associations were filmed and their lessons were analyzed first to develop a systematic…

  6. 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…

  7. 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…

  8. 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…

  9. 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.…

  10. 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…

  11. 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…

  12. Investigating Purposeful Science Curriculum Adaptation as a Strategy to Improve Teaching and Learning

    ERIC Educational Resources Information Center

    Debarger, Angela Haydel; Penuel, William R.; Moorthy, Savitha; Beauvineau, Yves; Kennedy, Cathleen A.; Boscardin, Christy Kim

    2017-01-01

    In this paper, we investigate the potential and conditions for using curriculum adaptation to support reform of science teaching and learning. With each wave of reform in science education, curriculum has played a central role and the contemporary wave focused on implementation of the principles and vision of the "Framework for K-12 Science…

  13. On the Impact of Adaptive Test Question Selection for Learning Efficiency

    ERIC Educational Resources Information Center

    Barla, Michal; Bielikova, Maria; Ezzeddinne, Anna Bou; Kramar, Tomas; Simko, Marian; Vozar, Oto

    2010-01-01

    In this paper we present a method for adaptive selection of test questions according to the individual needs of students within a web-based educational system. It functions as a combination of three particular methods. The first method is based on the course structure and focuses on the selection of the most appropriate topic for learning. The…

  14. 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…

  15. 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…

  16. 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…

  17. 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…

  18. A Hybrid Approach for Supporting Adaptivity in E-Learning Environments

    ERIC Educational Resources Information Center

    Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco

    2016-01-01

    Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…

  19. Adapting and Evaluating a Tree of Life Group for Women with Learning Disabilities

    ERIC Educational Resources Information Center

    Randle-Phillips, Cathy; Farquhar, Sarah; Thomas, Sally

    2016-01-01

    Background: This study describes how a specific narrative therapy approach called 'the tree of life' was adapted to run a group for women with learning disabilities. The group consisted of four participants and ran for five consecutive weeks. Materials and Methods: Participants each constructed a tree to represent their lives and presented their…

  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. Project Adapt: A Developmental Approach to Psycho-Motor Transfer. A Guide to Movement and Learning.

    ERIC Educational Resources Information Center

    Steele, Wah-Leeta

    Described is Project ADAPT (A Developmental Approach to Psychomotor Transfer), a validated program used with 808 primary grade children, some with learning difficulties, over a 3-year period to enhance academic readiness and self esteem through psychomotor training. An introductory project summary explains program objectives, the needs assessment…

  2. 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…

  3. 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…

  4. An adaptive deep learning approach for PPG-based identification.

    PubMed

    Jindal, V; Birjandtalab, J; Pouyan, M Baran; Nourani, M

    2016-08-01

    Wearable biosensors have become increasingly popular in healthcare due to their capabilities for low cost and long term biosignal monitoring. This paper presents a novel two-stage technique to offer biometric identification using these biosensors through Deep Belief Networks and Restricted Boltzman Machines. Our identification approach improves robustness in current monitoring procedures within clinical, e-health and fitness environments using Photoplethysmography (PPG) signals through deep learning classification models. The approach is tested on TROIKA dataset using 10-fold cross validation and achieved an accuracy of 96.1%.

  5. Predicting demographically sustainable rates of adaptation: can great tit breeding time keep pace with climate change?

    PubMed Central

    Gienapp, Phillip; Lof, Marjolein; Reed, Thomas E.; McNamara, John; Verhulst, Simon; Visser, Marcel E.

    2013-01-01

    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. PMID:23209174

  6. 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.

  7. 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.

  8. 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…

  9. Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning.

    PubMed

    Zhang, Dongyu; Lin, Liang; Chen, Tianshui; Wu, Xian; Tan, Wenwei; Izquierdo, Ebroul

    2017-01-01

    Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache. Moreover, the generalization ability of current systems is somewhat limited since they usually require elaborately collecting a dictionary of examples or carefully tuning features/components. In this paper, we present a novel representation learning framework that generates an end-to-end photo-sketch mapping through structure and texture decomposition. In the training stage, we first decompose the input face photo into different components according to their representational contents (i.e., structural and textural parts) by using a pre-trained convolutional neural network (CNN). Then, we utilize a branched fully CNN for learning structural and textural representations, respectively. In addition, we design a sorted matching mean square error metric to measure texture patterns in the loss function. In the stage of sketch rendering, our approach automatically generates structural and textural representations for the input photo and produces the final result via a probabilistic fusion scheme. Extensive experiments on several challenging benchmarks suggest that our approach outperforms example-based synthesis algorithms in terms of both perceptual and objective metrics. In addition, the proposed method also has better generalization ability across data set without additional training.

  10. 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.

  11. 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.

  12. Maximum type 1 error rate inflation in multiarmed clinical trials with adaptive interim sample size modifications.

    PubMed

    Graf, Alexandra C; Bauer, Peter; Glimm, Ekkehard; Koenig, Franz

    2014-07-01

    Sample size modifications in the interim analyses of an adaptive design can inflate the type 1 error rate, if test statistics and critical boundaries are used in the final analysis as if no modification had been made. While this is already true for designs with an overall change of the sample size in a balanced treatment-control comparison, the inflation can be much larger if in addition a modification of allocation ratios is allowed as well. In this paper, we investigate adaptive designs with several treatment arms compared to a single common control group. Regarding modifications, we consider treatment arm selection as well as modifications of overall sample size and allocation ratios. The inflation is quantified for two approaches: a naive procedure that ignores not only all modifications, but also the multiplicity issue arising from the many-to-one comparison, and a Dunnett procedure that ignores modifications, but adjusts for the initially started multiple treatments. The maximum inflation of the type 1 error rate for such types of design can be calculated by searching for the "worst case" scenarios, that are sample size adaptation rules in the interim analysis that lead to the largest conditional type 1 error rate in any point of the sample space. To show the most extreme inflation, we initially assume unconstrained second stage sample size modifications leading to a large inflation of the type 1 error rate. Furthermore, we investigate the inflation when putting constraints on the second stage sample sizes. It turns out that, for example fixing the sample size of the control group, leads to designs controlling the type 1 error rate.

  13. 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

  14. 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.

  15. 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…

  16. 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.

  17. 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.

  18. 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.

  19. Data rate management and real time operation: recursive adaptive frame integration of limited data

    NASA Astrophysics Data System (ADS)

    Rafailov, Michael K.

    2006-08-01

    Recursive Limited Frame Integration was proposed as a way to improve frame integration performance and mitigate issues related to high data rate needed to support conventional frame integration. The technique uses two thresholds -one tuned for optimum probability of detection, the other to manage required false alarm rate, and places integration process between those thresholds. This configuration allows a non-linear integration process that, along with Signal-to-Noise Ratio (SNR) gain, provides system designers more capability where cost, weight, or power considerations limit system data rate, processing, or memory capability. However, Recursive Frame Integration Limited may have performance issues when single-frame SNR is really low. Recursive Adaptive Limited Frame Integration was proposed as a means to improve limited integration performance with really low single-frame SNR. It combines the benefits of nonlinear recursive limited frame integration and adaptive thresholds with a kind of conventional frame integration. Adding the third threshold may help in managing real time operations. In the paper the Recursive Frame Integration is presented in form of multiple parallel recursive integration. Such an approach can help not only in data rate management but in mitigation of low single frame SNR issue for Recursive Integration as well as in real time operations with frame integration.

  20. Rat mandibular distraction osteogenesis: latency, rate, and rhythm determine the adaptive response.

    PubMed

    Paccione, M F; Mehrara, B J; Warren, S M; Greenwald, J A; Spector, J A; Luchs, J S; Longaker, M T

    2001-03-01

    Distraction osteogenesis is a well-established technique of endogenous tissue engineering. The biomechanical factors thought to affect the quality of the distraction regenerate include the latency, rate, rhythm, and consolidation period. In an effort to understand the impact of these parameters on regenerate bone formation, this study was designed to decipher the most adaptive response in a rat model of mandibular distraction osteogenesis. Ninety-six adult Sprague-Dawley rats were divided into 16 subgroups (n = 6 per subgroup) based on variations in the distraction parameters (i.e., latency, rate, and rhythm). After a 28-day consolidation period, the mandibles were harvested, decalcified, and sectioned. A standardized histologic ranking system was used to evaluate the effect of each protocol on the adaptive response of the regenerate bone. In this study, we have demonstrated that the latency period dramatically affects the success of distraction osteogenesis. Furthermore, distraction rates up to 0.50 mm per day stimulated excellent regenerate bone formation, whereas greater distraction rates produced a fibrous union. Finally, higher frequency distraction (i.e., increased rhythm) appeared to accelerate regenerate bone formation. We believe that defining the critical parameters of this model will improve future analysis of gene expression during rat mandibular distraction osteogenesis and may facilitate the development of biologically based strategies designed to enhance regenerate bone formation.

  1. Cold climate specialization: adaptive covariation between metabolic rate and thermoregulation in pregnant vipers.

    PubMed

    Lourdais, Olivier; Guillon, Michaël; Denardo, Dale; Blouin-Demers, Gabriel

    2013-07-02

    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.

  2. 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.

  3. 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

  4. Hyperspectral imaging-based credit card verifier structure with adaptive learning.

    PubMed

    Sumriddetchkajorn, Sarun; Intaravanne, Yuttana

    2008-12-10

    We propose and experimentally demonstrate a hyperspectral imaging-based optical structure for verifying a credit card. Our key idea comes from the fact that the fine detail of the embossed hologram stamped on the credit card is hard to duplicate, and therefore its key color features can be used for distinguishing between the real and counterfeit ones. As the embossed hologram is a diffractive optical element, we shine a number of broadband light sources one at a time, each at a different incident angle, on the embossed hologram of the credit card in such a way that different color spectra per incident angle beam are diffracted and separated in space. In this way, the center of mass of the histogram on each color plane is investigated by using a feed-forward backpropagation neural-network configuration. Our experimental demonstration using two off-the-shelf broadband white light emitting diodes, one digital camera, and a three-layer neural network can effectively identify 38 genuine and 109 counterfeit credit cards with false rejection rates of 5.26% and 0.92%, respectively. Key features include low cost, simplicity, no moving parts, no need of an additional decoding key, and adaptive learning.

  5. 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,…

  6. Application of simple adaptive control to rate gyroscope stable platform system

    NASA Astrophysics Data System (ADS)

    Hu, Yonghao; Song, Xueping; Li, Bangjun; Shi, Liping

    2013-09-01

    For a class of nonlinear systems with dynamic uncertainties, adaptive stabilization problem is considered in the rate gyroscope of stable platform system. Since the uncertainties are inevitable in the practical model of systems, the robust property of the systems in the presence of parametric uncertainties is important to be considered, such as modeling error, external disturbances, etc. Due to the strong nonlinearity and coupling characteristic of systems, it is difficult to obtain the precise model, and the nonlinearity cannot be cancelled exactly so that the controller performs badly. Adaptive control (AC) can adapt to parameter variations, but it is not applicable to the transition phase. A way to optimize the overall disturbances rejection performance of the AC system in the presence of unknown external disturbances existing in the stable platform system is provided in this paper. According to the construction of stable platform system based on gyroscope stabilized platform, the coordinate systems related to stable platform system are defined, and its mathematical model of stabilized platform is build up. Using the SIMULINK of MATLAB, the model is applied to the computer simulation of the stable platform system with good results. The author designed the control law of velocity-loop respective with the method of continuous correcting net and the AC. The simulation results show that the designed adaptive control law can satisfy the required criterion, it proves that the design method is feasible. In order to compare the above two method efficiently, the author gives the seeker system step response, square wave response especially. Adaptive control law is confirmed to give better tracking performance compared with correcting net control, and a control precision comparable to seeker system and higher robustness to parameter change, despite the simple controller. The research results ensure a wider application of simple AC in real mechanical systems.

  7. Towards Individualized Online Learning: The Design and Development of an Adaptive Web Based Learning Environment

    ERIC Educational Resources Information Center

    Inan, Fethi A.; Flores, Raymond; Ari, Fatih; Arslan-Ari, Ismahan

    2011-01-01

    The purpose of this study was to document the design and development of an adaptive system which individualizes instruction such as content, interfaces, instructional strategies, and resources dependent on two factors, namely student motivation and prior knowledge levels. Combining adaptive hypermedia methods with strategies proposed by…

  8. 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'…

  9. 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…

  10. Unsupervised learning approach to adaptive differential pulse code modulation.

    PubMed

    Griswold, N C; Sayood, K

    1982-04-01

    This research is concerned with investigating the problem of data compression utilizing an unsupervised estimation algorithm. This extends previous work utilizing a hybrid source coder which combines an orthogonal transformation with differential pulse code modulation (DPCM). The data compression is achieved in the DPCM loop, and it is the quantizer of this scheme which is approached from an unsupervised learning procedure. The distribution defining the quantizer is represented as a set of separable Laplacian mixture densities for two-dimensional images. The condition of identifiability is shown for the Laplacian case and a decision directed estimate of both the active distribution parameters and the mixing parameters are discussed in view of a Bayesian structure. The decision directed estimators, although not optimum, provide a realizable structure for estimating the parameters which define a distribution which has become active. These parameters are then used to scale the optimum (in the mean square error sense) Laplacian quantizer. The decision criteria is modified to prevent convergence to a single distribution which in effect is the default condition for a variance estimator. This investigation was applied to a test image and the resulting data demonstrate improvement over other techniques using fixed bit assignments and ideal channel conditions.

  11. Frontal theta links prediction errors to behavioral adaptation in reinforcement learning.

    PubMed

    Cavanagh, James F; Frank, Michael J; Klein, Theresa J; Allen, John J B

    2010-02-15

    Investigations into action monitoring have consistently detailed a frontocentral voltage deflection in the event-related potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the feedback-related negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single-trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single-trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Mediofrontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single-trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations, with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice.

  12. [Dual sensor-controlled rate adaptation in non-dynamical exercise].

    PubMed

    Sievers, B; Meine, M; Pfitzner, P; Trappe, H J

    2001-09-01

    Single sensor and dual sensor systems are used to reach physiological rate adaptation in pacemaker therapy. The purpose of the present study was to examine the sensor-controlled heart rate reaction and adaptability of a dual sensor (QT + activity) with 3 different tests. Nine chronotropically incompetent patients (group 1), 3 females and 6 males, mean age 74.1±8.43 years, were implanted 5 VVIR and 4 DDDR pacemakers (Vitatron, The Netherlands). The control group included 10 chronotropically competent patients (group 2) (2 females, 8 males, mean age 58.2±12.6 years). Both groups underwent 3 different tests: 1) a mental test, 2) an isometric test and 3) an activity (=tap) test. Heart rate was measured every 30 seconds by the recorded surface ECG. We measured an unsatisfaying heart rate response of the pacemaker patients in all 3 tests: During isometric exercise the pacing rate decreased from 63±3.5bpm to 65±3.4bpm (-0.97±0.71bpm/min) (p=0.1829) in contrast to an increase of the heart rate in the control group: from 77.8±15.8bpm to 92.5±19.9bpm (3.76±1.29bpm/min) (p=0.0048). During mental stress testing the pacing rate increased from 63±3.5bpm to 65±3.4bpm (0.66±0.20bpm/min) (p=0.0199) in the pacemaker group, compared to an increase of the heart rate in the control group from 75.8±15.8bpm to 83.6±17.3bpm (2.04±0.74bpm/min) (p=0.0076). Tapping on the pacemaker case produced an increase of the pacing rate from 65.9±2.8bpm to 73.6±3.8bpm (2.83±0.73bpm/min) (p=0.0004), whereas the heart rate decreased from 76.8±13.0bpm to 75.0±1.9bpm (-0.19±0.61bpm/min) (p=0.7522) in the control group. Compared to the physiologically chronotropic function of the control group, the sensor-controlled heart rate response was inadaequate during these tests. The expectations of sensor cross checking could not be fulfilled with the previous sensor combination (QT + activity).

  13. Catch trials in force field learning influence adaptation and consolidation of human motor memory.

    PubMed

    Stockinger, Christian; Focke, Anne; Stein, Thorsten

    2014-01-01

    Force field studies are a common tool to investigate motor adaptation and consolidation. Thereby, subjects usually adapt their reaching movements to force field perturbations induced by a robotic device. In this context, so-called catch trials, in which the disturbing forces are randomly turned off, are commonly used to detect after-effects of motor adaptation. However, catch trials also produce sudden large motor errors that might influence the motor adaptation and the consolidation process. Yet, the detailed influence of catch trials is far from clear. Thus, the aim of this study was to investigate the influence of catch trials on motor adaptation and consolidation in force field experiments. Therefore, 105 subjects adapted their reaching movements to robot-generated force fields. The test groups adapted their reaching movements to a force field A followed by learning a second interfering force field B before retest of A (ABA). The control groups were not exposed to force field B (AA). To examine the influence of diverse catch trial ratios, subjects received catch trials during force field adaptation with a probability of either 0, 10, 20, 30, or 40%, depending on the group. First, the results on motor adaptation revealed significant differences between the diverse catch trial ratio groups. With increasing amount of catch trials, the subjects' motor performance decreased and subjects' ability to accurately predict the force field-and therefore internal model formation-was impaired. Second, our results revealed that adapting with catch trials can influence the following consolidation process as indicated by a partial reduction to interference. Here, the optimal catch trial ratio was 30%. However, detection of consolidation seems to be biased by the applied measure of performance.

  14. Identifying innovation in laboratory studies of cultural evolution: rates of retention and measures of adaptation

    PubMed Central

    Caldwell, Christine A.; Cornish, Hannah; Kandler, Anne

    2016-01-01

    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. PMID:26926283

  15. On rate-dependent mechanical model for adaptive magnetorheological elastomer base isolator

    NASA Astrophysics Data System (ADS)

    Li, Yancheng; Li, Jianchun

    2017-04-01

    This paper presents research on the phenomenological model of an adaptive base isolator. The adaptive base isolator is made of field-dependent magnetorheological elastomer (MRE) which can alter its physical property under application of magnetic field. Experimental testing demonstrated that the developed MRE base isolator possesses an amazing ability to vary its stiffness under applied magnetic field. However, several challenges have been encountered when it comes modeling such novel device. For example, under a large deformation, the MRE base isolator exhibits a clear strain stiffening effect and this behavior escalates with the increasing of applied current. In addition, the MRE base isolator has also shown typical rate-dependent behavior. Following a review on mechanical models for viscos-elastic rubber devices, a novel rate-dependent model is proposed in this paper to capture the behavior of the new MRE base isolator. To develop a generalized model, the proposed model was evaluated using its performance under random displacement input and a seismic input. It shows that the proposed rate-dependent model can successfully describe the complex behavior of the device.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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

  1. 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…

  2. 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…

  3. Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan

    2017-09-01

    It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus

  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. High fidelity adaptive vector quantization at very low bit rates for progressive transmission of radiographic images

    NASA Astrophysics Data System (ADS)

    Mitra, Sunanda; Yang, Shu Y.

    1999-01-01

    An adaptive vector quantizer (VQ) using a clustering technique known as adaptive fuzzy leader clustering (AFLC) that is similar in concept to deterministic annealing for VQ codebook design has been developed. This vector quantizer, AFLC-VQ, has been designed to vector quantize wavelet decomposed sub images with optimal bit allocation. The high- resolution sub images at each level have been statistically analyzed to conform to generalized Gaussian probability distributions by selecting the optimal number of filter taps. The adaptive characteristics of AFLC-VQ result from AFLC, an algorithm that uses self-organizing neural networks with fuzzy membership values of the input samples for upgrading the cluster centroids based on well known optimization criteria. By generating codebooks containing codewords of varying bits, AFLC-VQ is capable of compressing large color/monochrome medical images at extremely low bit rates (0.1 bpp and less) and yet yielding high fidelity reconstructed images. The quality of the reconstructed images formed by AFLC-VQ has been compared with JPEG and EZW, the standard and the well known wavelet based compression technique (using scalar quantization), respectively, in terms of statistical performance criteria as well as visual perception. AFLC-VQ exhibits much better performance than the above techniques. JPEG and EZW were chosen as comparative benchmarks since these have been used in radiographic image compression. The superior performance of AFLC-VQ over LBG-VQ has been reported in earlier papers.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Adaptation.

    PubMed

    Broom, Donald M

    2006-01-01

    The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and

  13. 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]).

  14. Adaptive λ estimation in Lagrangian rate-distortion optimization for video coding

    NASA Astrophysics Data System (ADS)

    Chen, Lulin; Garbacea, Ilie

    2006-01-01

    In this paper, adaptive Lagrangian multiplier λ estimation in Larangian R-D optimization for video coding is presented that is based on the ρ-domain linear rate model and distortion model. It yields that λ is a function of rate, distortion and coding input statistics and can be written as λ(R, D, σ2) = β(ln(σ2/D) + δ)D/R + k 0, with β, δ and k 0 as coding constants, σ2 is variance of prediction error input. λ(R, D, σ2) describes its ubiquitous relationship with coding statistics and coding input in hybrid video coding such as H.263, MPEG-2/4 and H.264/AVC. The lambda evaluation is de-coupled with quantization parameters. The proposed lambda estimation enables a fine encoder design and encoder control.

  15. A comparison of stopping rules for computerized adaptive screening measures using the rating scale model.

    PubMed

    Leroux, Audrey J; Dodd, Barbara G

    2014-01-01

    The current study evaluates three stopping rules for computerized adaptive testing (CAT): the predicted standard error reduction (PSER), the fixed-length, and the minimum SE using Andrich's rating scale model with a survey to identify at-risk students. PSER attempts to reduce the number of items administered and increase measurement precision of the trait. Several variables are manipulated, such as trait distribution and item pool size, in order to evaluate how these conditions interact and potentially help improve the correct classification of students. The findings indicate that the PSER stopping rule may be preferred when wanting to correctly diagnose or classify students at-risk and at the same time alleviate test burden for those taking screening measures based on the rating scale model with smaller item pools.

  16. 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.

  17. Reinforcement learning and counterfactual reasoning explain adaptive behavior in a changing environment.

    PubMed

    Zhang, Yunfeng; Paik, Jaehyon; Pirolli, Peter

    2015-04-01

    Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with counterfactual reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that counterfactual reasoning is key to reproducing the various effects observed in this change detection paradigm.

  18. Vocal learning beyond imitation: mechanisms of adaptive vocal development in songbirds and human infants.

    PubMed

    Tchernichovski, Ofer; Marcus, Gary

    2014-10-01

    Studies of vocal learning in songbirds typically focus on the acquisition of sensory templates for song imitation and on the consequent process of matching song production to templates. However, functional vocal development also requires the capacity to adaptively diverge from sensory templates, and to flexibly assemble vocal units. Examples of adaptive divergence include the corrective imitation of abnormal songs, and the decreased tendency to copy over-abundant syllables. Such frequency-dependent effects might mirror tradeoffs between the assimilation of group identity (culture) while establishing individual and flexibly expressive songs. Intriguingly, although the requirements for vocal plasticity vary across songbirds, and more so between birdsong and language, the capacity to flexibly assemble vocal sounds develops in a similar, stepwise manner across species. Therefore, universal features of vocal learning go well beyond the capacity to imitate.

  19. 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.

  20. Adaptive eLearning modules for cytopathology education: A review and approach.

    PubMed

    Samulski, T Danielle; La, Teresa; Wu, Roseann I

    2016-11-01

    Clinical training imposes time and resource constraints on educators and learners, making it difficult to provide and absorb meaningful instruction. Additionally, innovative and personalized education has become an expectation of adult learners. Fortunately, the development of web-based educational tools provides a possible solution to these challenges. Within this review, we introduce the utility of adaptive eLearning platforms in pathology education. In addition to a review of the current literature, we provide the reader with a suggested approach for module creation, as well as a critical assessment of an available platform, based on our experience in creating adaptive eLearning modules for teaching basic concepts in gynecologic cytopathology. Diagn. Cytopathol. 2016;44:944-951. © 2016 Wiley Periodicals, Inc.

  1. Explanatory Model to Describe School District Prevalence Rates for Mental Retardation and Learning Disabilities.

    ERIC Educational Resources Information Center

    McDermott, Suzanne

    1994-01-01

    Data reports from South Carolina's 92 independent school districts during 1980-81 were used to calculate prevalence rates of mental retardation and learning disabilities. These prevalence rates were 41.66/1,000 children enrolled for mental retardation and 33.21/1,000 children enrolled for learning disabilities. Additional analysis showed that…

  2. Predator-induced phenotypic plasticity in metabolism and rate of growth: rapid adaptation to a novel environment.

    PubMed

    Handelsman, Corey A; Broder, E Dale; Dalton, Christopher M; Ruell, Emily W; Myrick, Christopher A; Reznick, David N; Ghalambor, Cameron K

    2013-12-01

    Novel environments often impose directional selection for a new phenotypic optimum. Novel environments, however, can also change the distribution of phenotypes exposed to selection by inducing phenotypic plasticity. Plasticity can produce phenotypes that either align with or oppose the direction of selection. When plasticity and selection are parallel, plasticity is considered adaptive because it provides a better pairing between the phenotype and the environment. If the plastic response is incomplete and falls short of producing the optimum phenotype, synergistic selection can lead to genetic divergence and bring the phenotype closer to the optimum. In contrast, non-adaptive plasticity should increase the strength of selection, because phenotypes will be further from the local optimum, requiring antagonistic selection to overcome the phenotype-environment mismatch and facilitate adaptive divergence. We test these ideas by documenting predator-induced plasticity for resting metabolic rate and growth rate in populations of the Trinidadian guppy (Poecilia reticulata) adapted to high and low predation. We find reduced metabolic rates and growth rates when cues from a predator are present during development, a pattern suggestive of adaptive and non-adaptive plasticity, respectively. When we compared populations recently transplanted from a high-predation environment into four streams lacking predators, we found evidence for rapid adaptive evolution both in metabolism and growth rate. We discuss the implications for predicting how traits will respond to selection, depending on the type of plasticity they exhibit.

  3. 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.

  4. 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.

  5. Adaptive optics vision simulation and perceptual learning system based on a 35-element bimorph deformable mirror.

    PubMed

    Dai, Yun; Zhao, Lina; Xiao, Fei; Zhao, Haoxin; Bao, Hua; Zhou, Hong; Zhou, Yifeng; Zhang, Yudong

    2015-02-10

    An adaptive optics visual simulation combined with a perceptual learning (PL) system based on a 35-element bimorph deformable mirror (DM) was established. The larger stroke and smaller size of the bimorph DM made the system have larger aberration correction or superposition ability and be more compact. By simply modifying the control matrix or the reference matrix, select correction or superposition of aberrations was realized in real time similar to a conventional adaptive optics closed-loop correction. PL function was first integrated in addition to conventional adaptive optics visual simulation. PL training undertaken with high-order aberrations correction obviously improved the visual function of adult anisometropic amblyopia. The preliminary application of high-order aberrations correction with PL training on amblyopia treatment was being validated with a large scale population, which might have great potential in amblyopia treatment and visual performance maintenance.

  6. 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.

  7. 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

  8. 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.

  9. Tumor Volume Reduction Rate during Adaptive Radiation Therapy as a Prognosticator for Nasopharyngeal Cancer

    PubMed Central

    Lee, Hyebin; Ahn, Yong Chan; Oh, Dongryul; Nam, Heerim; Noh, Jae Myoung; Park, Su Yeon

    2016-01-01

    Purpose The purpose of this study is to evaluate the prognostic significance of the tumor volume reduction rate (TVRR) measured during adaptive definitive radiation therapy (RT) for nasopharyngeal cancer (NPC). Materials and Methods We reviewed the RT records of 159 NPC patients treated with definitive RT with or without concurrent chemotherapy between January 2006 and February 2013. Adaptive re-planning was performed in all patients at the third week of RT. The pre- and mid-RT gross tumor volumes (GTVs) of the primary tumor and the metastatic lymph nodes were measured and analyzed for prognostic implications. Results After a median follow-up period of 41.5 months (range, 11.2 to 91.8 months) for survivors, there were 43 treatment failures. The overall survival and progression-free survival (PFS) rates at 5 years were 89.6% and 69.7%, respectively. The mean pre-RT GTV, mid-RT GTV, and TVRR were 45.9 cm3 (range, 1.5 to 185.3 cm3), 26.7 cm3 (1.0 to 113.8 cm3), and –41.9% (range, –87% to 78%), respectively. Patients without recurrence had higher TVRR than those with recurrence (44.3% in the no recurrence group vs. 34.0% in the recurrence group, p=0.004), and those with TVRR > 35% achieved a significantly higher rate of PFS at 5 years (79.2% in TVRR > 35% vs. 53.2% in TVRR ≤ 35%; p < 0.001). In multivariate analysis, TVRR was a significant factor affecting PFS (hazard ratio, 2.877; 95% confidence interval, 1.555 to 5.326; p=0.001). Conclusion TVRR proved to be a significant prognostic factor in NPC patients treated with definitive RT, and could be used as a potential indicator for early therapeutic modification during the RT course. PMID:26194371

  10. 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.

  11. Corticospinal excitability is enhanced after visuomotor adaptation and depends on learning rather than performance or error

    PubMed Central

    Bagce, Hamid F.; Saleh, Soha; Adamovich, Sergei V.; Krakauer, John W.

    2013-01-01

    We used adaptation to high and low gains in a virtual reality setup of the hand to test competing hypotheses about the excitability changes that accompany motor learning. Excitability was assayed through changes in amplitude of motor evoked potentials (MEPs) in relevant hand muscles elicited with single-pulse transcranial magnetic stimulation (TMS). One hypothesis is that MEPs will either increase or decrease, directly reflecting the effect of low or high gain on motor output. The alternative hypothesis is that MEP changes are not sign dependent but rather serve as a marker of visuomotor learning, independent of performance or visual-to-motor mismatch (i.e., error). Subjects were required to make flexion movements of a virtual forefinger to visual targets. A gain of 1 meant that the excursions of their real finger and virtual finger matched. A gain of 0.25 (“low gain”) indicated a 75% reduction in visual versus real finger displacement, a gain of 1.75 (“high gain”) the opposite. MEP increases (>40%) were noted in the tonically activated task-relevant agonist muscle for both high- and low-gain perturbations after adaptation reached asymptote with kinematics matched to veridical levels. Conversely, only small changes in excitability occurred in a control task of pseudorandom gains that required adjustments to large errors but in which learning could not accumulate. We conclude that changes in corticospinal excitability are related to learning rather than performance or error. PMID:23197454

  12. Congruency sequence effects and previous response times: conflict adaptation or temporal learning?

    PubMed

    Schmidt, James R; Weissman, Daniel H

    2016-07-01

    In the present study, we followed up on a recent report of two experiments in which the congruency sequence effect-the reduction of the congruency effect after incongruent relative to congruent trials in Stroop-like tasks-was observed without feature repetition or contingency learning confounds. Specifically, we further scrutinized these data to determine the plausibility of a temporal learning account as an alternative to the popular conflict adaptation account. To this end, we employed a linear mixed effects model to investigate the role of previous response time in producing the congruency sequence effect, because previous response time is thought to influence temporal learning. Interestingly, slower previous response times were associated with a reduced current-trial congruency effect, but only when the previous trial was congruent. An adapted version of the parallel episodic processing (PEP) model was able to fit these data if it was additionally assumed that attention "wanders" during different parts of the experiment (e.g., due to fatigue or other factors). Consistent with this assumption, the magnitude of the congruency effect was correlated across small blocks of trials. These findings demonstrate that a temporal learning mechanism provides a plausible account of the congruency sequence effect.

  13. In vivo human left-to-right ventricular differences in rate adaptation transiently increase pro-arrhythmic risk following rate acceleration.

    PubMed

    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 τ(s) =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 (r(2) =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.

  14. 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.

  15. Adaptive sparse signal processing of on-orbit lightning data using learned dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, Daniela I.; Smith, David A.; Hamlin, Timothy D.; Light, Tess E.; Suszcynsky, David M.

    2013-05-01

    For the past two decades, there has been an ongoing research effort at Los Alamos National Laboratory to learn more about the Earth's radiofrequency (RF) background utilizing satellite-based RF observations of terrestrial lightning. The Fast On-orbit Recording of Transient Events (FORTE) satellite provided a rich RF lighting database, comprising of five years of data recorded from its two RF payloads. While some classification work has been done previously on the FORTE RF database, application of modern pattern recognition techniques may advance lightning research in the scientific community and potentially improve on-orbit processing and event discrimination capabilities for future satellite payloads. We now develop and implement new event classification capability on the FORTE database using state-of-the-art adaptive signal processing combined with compressive sensing and machine learning techniques. The focus of our work is improved feature extraction using sparse representations in learned dictionaries. Conventional localized data representations for RF transients using analytical dictionaries, such as a short-time Fourier basis or wavelets, can be suitable for analyzing some types of signals, but not others. Instead, we learn RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics, using several established machine learning algorithms. Sparse classification features are extracted via matching pursuit search over the learned dictionaries, and used in conjunction with a statistical classifier to distinguish between lightning types. We present preliminary results of our work and discuss classification scenarios and future development.

  16. Application of an automatic adaptive filter for Heart Rate Variability analysis.

    PubMed

    Dos Santos, Laurita; Barroso, Joaquim J; Macau, Elbert E N; de Godoy, Moacir F

    2013-12-01

    The presence of artifacts and noise effects in temporal series can seriously hinder the analysis of Heart Rate Variability (HRV). The tachograms should be carefully edited to avoid erroneous interpretations. The physician should carefully analyze the tachogram in order to detect points that might be associated with unlikely biophysical behavior and manually eliminate them from the data series. However, this is a time-consuming procedure. To facilitate the pre-analysis of the tachogram, this study uses a method of data filtering based on an adaptive filter which is quickly able to analyze a large amount of data. The method was applied to 229 time series from a database of patients with different clinical conditions: premature newborns, full-term newborns, healthy young adults, adults submitted to a very-low-calorie diet, and adults under preoperative evaluation for coronary artery bypass grafting. This proposed method is compared to the demanding conventional method, wherein the corrections of occasional ectopic beats and artifacts are usually manually executed by a specialist. To confirm the reliability of the results obtained, correlation coefficients were calculated, using both automatic and manual methods of ltering for each HRV index selected. A high correlation between the results was found, with highly significant p values, for all cases, except for some parameters analyzed in the premature newborns group, an issue that is thoroughly discussed. The authors concluded that the proposed adaptive filtering method helps to efficiently handle the task of editing temporal series for HRV analysis.

  17. An adaptive weighted ensemble procedure for efficient computation of free energies and first passage rates

    PubMed Central

    Bhatt, Divesh; Bahar, Ivet

    2012-01-01

    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. PMID:22979844

  18. 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

  19. 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…

  20. Learner's Attitudes towards Online Language Learning; and Corresponding Success Rates

    ERIC Educational Resources Information Center

    Cinkara, Emrah; Bagceci, Birsen

    2013-01-01

    Online teaching has long been a key area of interest recently in every field of education as well as English language teaching. Numerous hardware tools, such as, mp3 players, mobile devices, and so on; and software applications, such as, podcasts, wikis, learning management systems, and so on, have been used in distance and online instruction and…

  1. An Examination of Listening Rate. Auditory Learning Monograph Series 1.

    ERIC Educational Resources Information Center

    Levine, S. Joseph

    Summarized are results of an investigation of elementary age listeners' preferences for the rate of presentation of recorded information in which it was found that children, when given the opportunity, will manipulate the rate of presentation of recorded information, that they have a preference for rates of presentation, and that individual…

  2. Neural activity in the dorsal medial superior temporal area of monkeys represents retinal error during adaptive motor learning

    PubMed Central

    Takemura, Aya; Ofuji, Tomoyo; Miura, Kenichiro; Kawano, Kenji

    2017-01-01

    To adapt to variable environments, humans regulate their behavior by modulating gains in sensory-to-motor processing. In this study, we measured a simple eye movement, the ocular following response (OFR), in monkeys to study the neuronal basis of adaptive motor learning in the visuomotor processing stream. The medial superior temporal (MST) area of the cerebral cortex is a critical site for contextual gain modulation of the OFR. However, the role of MST neurons in adaptive gain modulation of the OFR remains unknown. We adopted a velocity step-down sequence paradigm that was designed to promote adaptive gain modulation of the OFR to investigate the role of the dorsal MST (MSTd) in adaptive motor learning. In the initial learning stage, we observed a reduction in the OFR but no significant change in the “open-loop” responses for the majority of the MSTd neurons. However, in the late learning stage, some MSTd neurons exhibited significantly enhanced “closed-loop” responses in association with increases in retinal error velocity. These results indicate that the MSTd area primarily encodes visual motion, suggesting that MSTd neurons function upstream of the motor learning site to provide sensory signals to the downstream structures involved in adaptive motor learning. PMID:28102342

  3. 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.

  4. Considering Learning Styles and Context-Awareness for Mobile Adaptive Learning

    ERIC Educational Resources Information Center

    Tortorella, Richard A. W.; Graf, Sabine

    2017-01-01

    Mobile devices are becoming ubiquitous in our society and more so with school aged children. In order to get the most out of the portable computing power present at students' fingertips, this paper proposes an approach for providing mobile, personalized course content tailored to each individual's learning style while incorporating adaptive…

  5. Third Age Learning: Adapting the Idea to a Thailand Context of Lifelong Learning

    ERIC Educational Resources Information Center

    Ratana-Ubol, Archanya; Richards, Cameron

    2016-01-01

    The concept of the university of the third age (U3A) is well established overseas and a key international focus for emerging global networks of senior citizen (i.e. seniors) lifelong learning. However it is yet to become so in Thailand although it too is in the process of becoming an ageing society. Moreover, this is despite the extent to which…

  6. 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

  7. Enhancing Collaborative Learning through Dynamic Forms of Support: The Impact of an Adaptive Domain-Specific Support Strategy

    ERIC Educational Resources Information Center

    Karakostas, A.; Demetriadis, S.

    2011-01-01

    Research on computer-supported collaborative learning (CSCL) has strongly emphasized the value of providing student support of either fixed (e.g. collaboration scripts) or dynamic form (e.g. adaptive supportive interventions). Currently, however, there is not sufficient evidence corroborating the potential of adaptive support methods to improve…

  8. Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples.

    PubMed

    Piastra, Marco

    2013-05-01

    Competitive Hebbian Learning (CHL) (Martinetz, 1993) is a simple and elegant method for estimating the topology of a manifold from point samples. The method has been adopted in a number of self-organizing networks described in the literature and has given rise to related studies in the fields of geometry and computational topology. Recent results from these fields have shown that a faithful reconstruction can be obtained using the CHL method only for curves and surfaces. Within these limitations, these findings constitute a basis for defining a CHL-based, growing self-organizing network that produces a faithful reconstruction of an input manifold. The SOAM (Self-Organizing Adaptive Map) algorithm adapts its local structure autonomously in such a way that it can match the features of the manifold being learned. The adaptation process is driven by the defects arising when the network structure is inadequate, which cause a growth in the density of units. Regions of the network undergo a phase transition and change their behavior whenever a simple, local condition of topological regularity is met. The phase transition is eventually completed across the entire structure and the adaptation process terminates. In specific conditions, the structure thus obtained is homeomorphic to the input manifold. During the adaptation process, the network also has the capability to focus on the acquisition of input point samples in critical regions, with a substantial increase in efficiency. The behavior of the network has been assessed experimentally with typical data sets for surface reconstruction, including suboptimal conditions, e.g. with undersampling and noise.

  9. 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

  10. The local enhancement conundrum: in search of the adaptive value of a social learning mechanism.

    PubMed

    Arbilly, Michal; Laland, Kevin N

    2014-02-01

    Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima facie appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer-scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed.

  11. 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

  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.

  13. Adaptive Local Spatiotemporal Features from RGB-D Data for One-Shot Learning Gesture Recognition.

    PubMed

    Lin, Jia; Ruan, Xiaogang; Yu, Naigong; Yang, Yee-Hong

    2016-12-17

    Noise and constant empirical motion constraints affect the extraction of distinctive spatiotemporal features from one or a few samples per gesture class. To tackle these problems, an adaptive local spatiotemporal feature (ALSTF) using fused RGB-D data is proposed. First, motion regions of interest (MRoIs) are adaptively extracted using grayscale and depth velocity variance information to greatly reduce the impact of noise. Then, corners are used as keypoints if their depth, and velocities of grayscale and of depth meet several adaptive local constraints in each MRoI. With further filtering of noise, an accurate and sufficient number of keypoints is obtained within the desired moving body parts (MBPs). Finally, four kinds of multiple descriptors are calculated and combined in extended gradient and motion spaces to represent the appearance and motion features of gestures. The experimental results on the ChaLearn gesture, CAD-60 and MSRDailyActivity3D datasets demonstrate that the proposed feature achieves higher performance compared with published state-of-the-art approaches under the one-shot learning setting and comparable accuracy under the leave-one-out cross validation.

  14. Adaptive behaviour and feedback processing integrate experience and instruction in reinforcement learning.

    PubMed

    Schiffer, Anne-Marike; Siletti, Kayla; Waszak, Florian; Yeung, Nick

    2017-02-01

    In any non-deterministic environment, unexpected events can indicate true changes in the world (and require behavioural adaptation) or reflect chance occurrence (and must be discounted). Adaptive behaviour requires distinguishing these possibilities. We investigated how humans achieve this by integrating high-level information from instruction and experience. In a series of EEG experiments, instructions modulated the perceived informativeness of feedback: Participants performed a novel probabilistic reinforcement learning task, receiving instructions about reliability of feedback or volatility of the environment. Importantly, our designs de-confound informativeness from surprise, which typically co-vary. Behavioural results indicate that participants used instructions to adapt their behaviour faster to changes in the environment when instructions indicated that negative feedback was more informative, even if it was simultaneously less surprising. This study is the first to show that neural markers of feedback anticipation (stimulus-preceding negativity) and of feedback processing (feedback-related negativity; FRN) reflect informativeness of unexpected feedback. Meanwhile, changes in P3 amplitude indicated imminent adjustments in behaviour. Collectively, our findings provide new evidence that high-level information interacts with experience-driven learning in a flexible manner, enabling human learners to make informed decisions about whether to persevere or explore new options, a pivotal ability in our complex environment.

  15. Adaptive Local Spatiotemporal Features from RGB-D Data for One-Shot Learning Gesture Recognition

    PubMed Central

    Lin, Jia; Ruan, Xiaogang; Yu, Naigong; Yang, Yee-Hong

    2016-01-01

    Noise and constant empirical motion constraints affect the extraction of distinctive spatiotemporal features from one or a few samples per gesture class. To tackle these problems, an adaptive local spatiotemporal feature (ALSTF) using fused RGB-D data is proposed. First, motion regions of interest (MRoIs) are adaptively extracted using grayscale and depth velocity variance information to greatly reduce the impact of noise. Then, corners are used as keypoints if their depth, and velocities of grayscale and of depth meet several adaptive local constraints in each MRoI. With further filtering of noise, an accurate and sufficient number of keypoints is obtained within the desired moving body parts (MBPs). Finally, four kinds of multiple descriptors are calculated and combined in extended gradient and motion spaces to represent the appearance and motion features of gestures. The experimental results on the ChaLearn gesture, CAD-60 and MSRDailyActivity3D datasets demonstrate that the proposed feature achieves higher performance compared with published state-of-the-art approaches under the one-shot learning setting and comparable accuracy under the leave-one-out cross validation. PMID:27999337

  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. 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…

  18. 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…

  19. 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…

  20. The Dependence of Auditory Nerve Rate Adaptation on Electric Stimulus Parameters, Electrode Position, and Fiber Diameter: A Computer Model Study

    PubMed Central

    Woo, Jihwan; Abbas, Paul J.

    2009-01-01

    This paper describes results from a stochastic computational neuron model that simulates the effects of rate adaptation on the responses to electrical stimulation in the form of pulse trains. We recently reported results from a single-node computational model that included a novel element that tracks external potassium ion concentration so as to modify membrane voltage and cause adaptation-like responses. Here, we report on an improved version of the model that incorporates the anatomical components of a complete feline auditory nerve fiber (ANF) so that conduction velocity and effects of manipulating the site of excitation can be evaluated. Model results demonstrate rate adaptation and changes in spike amplitude similar to those reported for feline ANFs. Changing the site of excitation from a central to a peripheral axonal site resulted in plausible changes in latency and relative spread (i.e., dynamic range). Also, increasing the distance between a modeled ANF and a stimulus electrode tended to decrease the degree of rate adaptation observed in pulse-train responses. This effect was clearly observed for high-rate (5,000 pulse/s) trains but not low-rate (250 pulse/s) trains. Finally, for relatively short electrode-to-ANF distances, increases in modeled ANF diameter increased the degree of rate adaptation. These results are compared against available feline ANF data, and possible effects of individual parameters are discussed. PMID:20033248

  1. 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

  2. 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.

  3. Tracking the Turn Maneuvering Target Using the Multi-Target Bayes Filter with an Adaptive Estimation of Turn Rate.

    PubMed

    Liu, Zong-Xiang; Wu, De-Hui; Xie, Wei-Xin; Li, Liang-Qun

    2017-02-15

    Tracking the target that maneuvers at a variable turn rate is a challenging problem. The traditional solution for this problem is the use of the switching multiple models technique, which includes several dynamic models with different turn rates for matching the motion mode of the target at each point in time. However, the actual motion mode of a target at any time may be different from all of the dynamic models, because these models are usually limited. To address this problem, we establish a formula for estimating the turn rate of a maneuvering target. By applying the estimation method of the turn rate to the multi-target Bayes (MB) filter, we develop a MB filter with an adaptive estimation of the turn rate, in order to track multiple maneuvering targets. Simulation results indicate that the MB filter with an adaptive estimation of the turn rate, is better than the existing filter at tracking the target that maneuvers at a variable turn rate.

  4. Adaptive Adolescent Flexibility: Neurodevelopment of Decision-making and Learning in a Risky Context.

    PubMed

    McCormick, Ethan M; Telzer, Eva H

    2017-03-01

    Research on adolescence has largely focused on the particular biological and neural changes that place teens at risk for negative outcomes linked to increases in sensation-seeking and risky behavior. However, there is a growing interest in the adaptive function of adolescence, with work highlighting the dual nature of adolescence as a period of potential risk and opportunity. We examined how behavioral and neural sensitivity to risk and reward varies as a function of age using the Balloon Analog Risk Task. Seventy-seven children and adolescents (ages 8-17 years) completed the Balloon Analog Risk Task during an fMRI session. Results indicate that adolescents show greater learning throughout the task. Furthermore, older participants showed increased neural responses to reward in the OFC and ventral striatum, increased activation to risk in the mid-cingulate cortex, as well as increased functional OFC-medial PFC coupling in both risk and reward contexts. Age-related changes in regional activity and interregional connectivity explain the link between age and increases in flexible learning. These results support the idea that adolescents' sensitivity to risk and reward supports adaptive learning and behavioral approaches for reward acquisition.

  5. Adaptive Adolescent Flexibility: Neurodevelopment of Decision-making and Learning in a Risky Context

    PubMed Central

    McCormick, Ethan M.; Telzer, Eva H.

    2017-01-01

    Research on adolescence has largely focused on the particular biological and neural changes that place teens at risk for negative outcomes linked to increases in sensation-seeking and risky behavior. However, there is a growing interest in the adaptive function of adolescence, with work highlighting the dual nature of adolescence as a period of potential risk and opportunity. We examined how behavioral and neural sensitivity to risk and reward varies as a function of age using the Balloon Analog Risk Task. Seventy-seven children and adolescents (ages 8–17 years) completed the Balloon Analog Risk Task during an fMRI session. Results indicate that adolescents show greater learning throughout the task. Furthermore, older participants showed increased neural responses to reward in the OFC and ventral striatum, increased activation to risk in the mid-cingulate cortex, as well as increased functional OFC–medial PFC coupling in both risk and reward contexts. Age-related changes in regional activity and interregional connectivity explain the link between age and increases in flexible learning. These results support the idea that adolescents’ sensitivity to risk and reward supports adaptive learning and behavioral approaches for reward acquisition. PMID:28129057

  6. 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.

  7. 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…

  8. 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…

  9. 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.

  10. Rate Adaptive Based Resource Allocation with Proportional Fairness Constraints in OFDMA Systems.

    PubMed

    Yin, Zhendong; Zhuang, Shufeng; Wu, Zhilu; Ma, Bo

    2015-09-25

    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.

  11. 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

  12. Adaptive disturbance attenuation via output feedback for nonlinear systems with polynomial-of-output growth rate

    NASA Astrophysics Data System (ADS)

    Shang, Fang; Liu, Yungang

    2014-03-01

    In this paper, the adaptive disturbance attenuation via output feedback is investigated for a class of nonlinear systems with uncertain control coefficients. Notably, the considered systems depend on unmeasured states with polynomial-of-output growth rate, and hence the systems have severe unknowns. Due to the existence of both the virtual and actual uncertain control coefficients, we first introduce a suitable state transformation, such that the control design becomes convenient. Then, we construct an appropriate and simple state observer with two important gains, and one gain is large enough to handle the constants appeared in the design procedure, and the other is updated on-line to deal with the polynomial-of-output growth rate of the system. After that, an output feedback controller is proposed based on the observer in a step-by-step manner. Finally, it is shown that, by appropriate choice of the design parameters, the states of the closed-loop system are globally bounded, and the disturbance attenuation is achieved in the sense of ?-gain.

  13. 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

  14. 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.

  15. 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…

  16. 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.

  17. 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…

  18. 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…

  19. 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 University…

  20. Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints.

    PubMed

    Liu, Yan-Jun; Li, Jing; Tong, Shaocheng; Chen, C L Philip

    2016-07-01

    In order to stabilize a class of uncertain nonlinear strict-feedback systems with full-state constraints, an adaptive neural network control method is investigated in this paper. The state constraints are frequently emerged in the real-life plants and how to avoid the violation of state constraints is an important task. By introducing a barrier Lyapunov function (BLF) to every step in a backstepping procedure, a novel adaptive backstepping design is well developed to ensure that the full-state constraints are not violated. At the same time, one remarkable feature is that the minimal learning parameters are employed in BLF backstepping design. By making use of Lyapunov analysis, we can prove that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded and the output is well driven to follow the desired output. Finally, a simulation is given to verify the effectiveness of the method.

  1. Learning Curves Observed in Establishing Targeted Rate of Force Application in Pressure Pain Algometry.

    PubMed

    Emerson Kavchak, Alicia J; Sault, Josiah D; Vendrely, Ann

    2016-01-01

    Purpose: To determine whether learning curves can be observed with deliberate practice when the goal is to apply a consistent rate of force at 5 N/second during pressure pain threshold (PPT) testing in healthy volunteers. Methods: In this prospective study, 17 clinician participants completed PPT targeted rate-of-application testing with healthy volunteers using three different feedback paradigms. The resultant performances of ramp rate during 36 trials were plotted on a graph and examined to determine whether learning curves were observed. Results: Clinicians were not consistent in the rate of force applied. None demonstrated a learning curve over the course of 36 trials and three testing paradigms. Conclusion: The results of this study indicate that applying a consistent 5 N/second of force is difficult for practising clinicians. The lack of learning curves observed suggests that educational strategies for clinicians using PPT may need to change.

  2. Learning Curves Observed in Establishing Targeted Rate of Force Application in Pressure Pain Algometry

    PubMed Central

    Sault, Josiah D.; Vendrely, Ann

    2016-01-01

    Purpose: To determine whether learning curves can be observed with deliberate practice when the goal is to apply a consistent rate of force at 5 N/second during pressure pain threshold (PPT) testing in healthy volunteers. Methods: In this prospective study, 17 clinician participants completed PPT targeted rate-of-application testing with healthy volunteers using three different feedback paradigms. The resultant performances of ramp rate during 36 trials were plotted on a graph and examined to determine whether learning curves were observed. Results: Clinicians were not consistent in the rate of force applied. None demonstrated a learning curve over the course of 36 trials and three testing paradigms. Conclusion: The results of this study indicate that applying a consistent 5 N/second of force is difficult for practising clinicians. The lack of learning curves observed suggests that educational strategies for clinicians using PPT may need to change. PMID:27909360

  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. Effect of element directivity on adaptive beamforming applied to high-frame-rate ultrasound.

    PubMed

    Hasegawa, Hideyuki; Kanai, Hiroshi

    2015-03-01

    High-frame-rate ultrasound is a promising technique for measurement and imaging of cardiovascular dynamics. In high-frame-rate ultrasonic imaging, unfocused ultrasonic beams are used in transmit and multiple focused receiving beams are created by parallel beamforming using the delay and sum (DAS) method. However, the spatial resolution and contrast are degraded compared with conventional beamforming using focused transmit beams. In the present study, the minimum variance beamformer was examined for improvement of the spatial resolution in high-frame-rate ultrasound. In conventional minimum variance beamforming, the spatial covariance matrix of ultrasonic echo signals received by individual transducer elements is obtained without considering the directivity of the transducer element. By omitting the element directivity, the error in estimation of the desired signal (i.e., the echo from the focal point) increases, and as a result, the improvement of the spatial resolution is degraded. In the present study, the element directivity was taken into account in estimation of the spatial covariance matrix used in minimum variance beamforming. The effect of the element directivity on adaptive beamforming was evaluated by computer simulation and basic experiments using a phantom. In parallel beamforming with the conventional DAS beamformer, the lateral spatial resolution, which was evaluated from the lateral full width at half maximum of the echo amplitude profile in the basic experiment, was 0.50 mm. Using conventional amplitude and phase estimation (APES) beamforming, the lateral spatial resolution was improved to 0.37 mm. The lateral spatial resolution was further improved to 0.30 mm using the modified APES beamforming by considering the element directivity. Image contrast and contrast-to-noise ratios, respectively, were -12.3 and 6.5 dB (DAS), -32.8 and -11.3 dB (APES), and -7.0 and 3.1 dB (modified APES).

  5. The hidden shift: how do night shift nurses learn to adapt to circadian disruption?

    PubMed

    Lawson Carney, Mary

    2013-01-01

    Disruption of circadian rhythm has a significant impact on the physical, psychosocial, and professional lives of night shift nurses. How night shift nurses learn the coping techniques they employ to adapt to this disruption was examined in a qualitative, cross-sectional survey of 42 nurses. A template analysis technique was used to categorize the responses, which were then compared to industry "best practices" in fatigue countermeasures. Results documented the variety of sleep/wake routines nurses employ to cope with circadian disruption as well as a wide-ranging variety of behaviors related to driving while fatigued.

  6. 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.

  7. 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.

  8. Adapting the Learning-Cycle to Enrich Undergraduate Neuroscience Education for All Students

    PubMed Central

    Stewart, Mark; Stavrianeas, Stasinos

    2008-01-01

    A learning-cycle approach to science instruction is not new to science educators (Karplus, 1977; Kolb, 1984; Bergquist, 1991; Zollman, 1990; Allard and Barman, 1994). Somewhat less known, however, is the usefulness of this approach for creating lab activities for a broad audience of undergraduates. The following paper presents a brief overview of a laboratory activity that can be adapted for use by instructors of introductory neuroscience courses. The three-hour activity is geared towards tapping key elements of the learning-cycle approach, with a particular emphasis on the exploration phase of the model. Students work as members of small teams to explore a contemporary issue involving memory and gain hands-on experience from the outset, to which conceptual information is then added during lecture the following week. The approach is in marked contrast to the more traditional practice in the sciences where laboratory activities generally serve to punctuate already presented lecture material. PMID:23493626

  9. 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…

  10. Adaptive, fast walking in a biped robot under neuronal control and learning.

    PubMed

    Manoonpong, Poramate; Geng, Tao; Kulvicius, Tomas; Porr, Bernd; Wörgötter, Florentin

    2007-07-01

    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.

  11. [Jefferson scale of physician lifelong learning: translation and adaptation for the portuguese medical population].

    PubMed

    Salgueira, Ana Paula; Frada, Tiago; Aguiar, Pedro; Costa, Manuel João

    2009-01-01

    The competence and professionalism of doctors depend on the process of Lifelong Learning (LLL). In the Portuguese settings, in which the re-certification of physicians' skills or knowledge is currently not required, the exercise of LLL is left to personal motivation and initiative. The importance of LLL has been highlighted in numerous international recommendations and has already led, in the United States, to the development and validation of a scale for measuring physician LLL - the Jefferson Scale of Physician Life Long Learning (JSPLL). The lack of valid instruments to measure LLL adapted to the Portuguese contexts was the basis for this work, which presents the translation and adaptation of JSPLL, and the subsequent validation of the translated version to the Portuguese medical community. The translation and validation of the English version of JSPLL (JSPLL-VP) was conducted with physicians of Health Care institutions in the District of Braga, Portugal, in 2007. Methods of both qualitative (translation, assessment of the translation, retro translation) and quantitative nature (internal consistency analysis, factor analysis and analysis of response frequencies) resulted in a factor analysis that replicated, with the exception of three items, the distribution of the original scale by four factors: professional learning beliefs and motivation, scholarly activities, attention to learning opportunities and technical skills in seeking information. The results show that the JSPLL-VP is a valid Scale fit for purpose. Cronbach's alpha coefficients for the whole scale (.89) and for each factor, confirmed the internal consistency of the Scale. Additionally, differences were found between mean and standard deviations for different Scale factors. In summary, this work provides a new validated tool to monitor physician's LLL in Portugal. The transversal characterization of LLL of specific medical professionals - by specialty or by type of institution - or longitudinal

  12. 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

  13. Adaptive sparse signal processing of on-orbit lightning data using learned dictionaries

    NASA Astrophysics Data System (ADS)

    Moody, D. I.; Hamlin, T.; Light, T. E.; Loveland, R. C.; Smith, D. A.; Suszcynsky, D. M.

    2012-12-01

    For the past two decades, there has been an ongoing research effort at Los Alamos National Laboratory (LANL) to learn more about the Earth's radiofrequency (RF) background utilizing satellite-based RF observations of terrestrial lightning. Arguably the richest satellite lightning database ever recorded is that from the Fast On-orbit Recording of Transient Events (FORTE) satellite, which returned at least five years of data from its two RF payloads after launch in 1997. While some classification work has been done previously on the LANL FORTE RF database, application of modern pattern recognition techniques may further lightning research in the scientific community and potentially improve on-orbit processing and event discrimination capabilities for future satellite payloads. We now develop and implement new event classification capability on the FORTE database using state-of-the-art adaptive signal processing combined with compressive sensing and machine learning techniques. The focus of our work is improved feature extraction using sparse representations in learned dictionaries. Extracting classification features from RF signals typically relies on knowledge of the application domain in order to find feature vectors unique to a signal class and robust against background noise. Conventional localized data representations for RF transients using analytical dictionaries, such as a short-time Fourier basis or wavelets, can be suitable for analyzing some types of signals, but not others. Instead, we learn RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics, using several established machine learning algorithms. Sparse classification features are extracted via matching pursuit search over the learned dictionaries, and used in conjunction with a statistical classifier to distinguish between lightning types. We present preliminary results of our work and discuss classification performance

  14. 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.

  15. 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.

  16. An Adaptive Resonance Theory account of the implicit learning of orthographic word forms.

    PubMed

    Glotin, H; Warnier, P; Dandurand, F; Dufau, S; Lété, B; Touzet, C; Ziegler, J C; Grainger, J

    2010-01-01

    An Adaptive Resonance Theory (ART) network was trained to identify unique orthographic word forms. Each word input to the model was represented as an unordered set of ordered letter pairs (open bigrams) that implement a flexible prelexical orthographic code. The network learned to map this prelexical orthographic code onto unique word representations (orthographic word forms). The network was trained on a realistic corpus of reading textbooks used in French primary schools. The amount of training was strictly identical to children's exposure to reading material from grade 1 to grade 5. Network performance was examined at each grade level. Adjustment of the learning and vigilance parameters of the network allowed us to reproduce the developmental growth of word identification performance seen in children. The network exhibited a word frequency effect and was found to be sensitive to the order of presentation of word inputs, particularly with low frequency words. These words were better learned with a randomized presentation order compared with the order of presentation in the school books. These results open up interesting perspectives for the application of ART networks in the study of the dynamics of learning to read.

  17. 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.

  18. Noiseless imaging detector for adaptive optics with kHz frame rates

    NASA Astrophysics Data System (ADS)

    Vallerga, John V.; McPhate, Jason; Mikulec, Bettina; Tremsin, Anton; Clark, Allan; Siegmund, Oswald

    2004-10-01

    A new hybrid optical detector is described that has many of the attributes desired for the next generation AO wavefront sensors. The detector consists of a proximity focused MCP read out by four multi-pixel application specific integrated circuit (ASIC) chips developed at CERN ("Medipix2") with individual pixels that amplify, discriminate and count input events. The detector has 512 x 512 pixels, zero readout noise (photon counting) and can be read out at 1kHz frame rates. The Medipix2 readout chips can be electronically shuttered down to a temporal window of a few microseconds with an accuracy of 10 nanoseconds. When used in a Shack-Hartman style wavefront sensor, it should be able to centroid approximately 5000 spots using 7 x 7 pixel sub-apertures resulting in very linear, off-null error correction terms. The quantum efficiency depends on the optical photocathode chosen for the bandpass of interest. A three year development effort for this detector technology has just been funded as part of the first Adaptive Optics Development Program managed by the National Optical Astronomy Observatory.

  19. Millimeter Wave MIMO Channel Estimation Using Overlapped Beam Patterns and Rate Adaptation

    NASA Astrophysics Data System (ADS)

    Kokshoorn, Matthew; Chen, He; Wang, Peng; Li, Yonghui; Vucetic, Branka

    2017-02-01

    This paper is concerned with the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we first propose a fast channel estimation (FCE) algorithm based on a novel overlapped beam pattern design, which can increase the amount of information carried by each channel measurement and thus reduce the required channel estimation time compared to the existing non-overlapped designs. We develop a maximum likelihood (ML) estimator to optimally extract the path information from the channel measurements. Then, we propose a novel rate-adaptive channel estimation (RACE) algorithm, which can dynamically adjust the number of channel measurements based on the expected probability of estimation error (PEE). The performance of both proposed algorithms is analyzed. For the FCE algorithm, an approximate closed-form expression for the PEE is derived. For the RACE algorithm, a lower bound for the minimum signal energy-to-noise ratio required for a given number of channel measurements is developed based on the Shannon-Hartley theorem. Simulation results show that the FCE algorithm significantly reduces the number of channel estimation measurements compared to the existing algorithms using non-overlapped beam patterns. By adopting the RACE algorithm, we can achieve up to a 6dB gain in signal energy-to-noise ratio for the same PEE compared to the existing algorithms.

  20. Learned cardiac control with heart rate biofeedback transfers to emotional reactions.

    PubMed

    Peira, Nathalie; Pourtois, Gilles; Fredrikson, Mats

    2013-01-01

    Emotions involve subjective feelings, action tendencies and physiological reactions. Earlier findings suggest that biofeedback might provide a way to regulate the physiological components of emotions. The present study investigates if learned heart rate regulation with biofeedback transfers to emotional situations without biofeedback. First, participants learned to decrease heart rate using biofeedback. Then, inter-individual differences in the acquired skill predicted how well they could decrease heart rate reactivity when later exposed to negative arousing pictures without biofeedback. These findings suggest that (i) short lasting biofeedback training improves heart rate regulation and (ii) the learned ability transfers to emotion challenging situations without biofeedback. Thus, heart rate biofeedback training may enable regulation of bodily aspects of emotion also when feedback is not available.