Active Learning Approaches by Visualizing ICT Devices with Milliseconds Resolution for Deeper Understanding in Physics

NASA Astrophysics Data System (ADS)

Kobayashi, Akizo; Okiharu, Fumiko

2010-07-01

We are developing various modularized materials in physics education to overcome students' misconceptions by use of ICT, i.e. video analysis software and ultra-high-speed digital movies, motion detector, force sensors, current and voltage probes, temperature sensors etc. Furthermore, we also present some new modules of active learning approaches on electric circuit using high speed camera and voltage probes with milliseconds resolution. We are now especially trying to improve conceptual understanding by use of ICT devices with milliseconds resolution in various areas of physics education We give some modules of mass measurements by video analysis of collision phenomena by using high speed cameras—Casio EX-F1(1200 fps), EX-FH20(1000 fps) and EX-FC100/150(1000 fps). We present several new modules on collision phenomena to establish deeper understanding of conservation laws of momentum. We discuss some effective results of trial on a physics education training courses for science educators, and those for science teachers during the renewal years of teacher's license after every ten years in Japan. Finally, we discuss on some typical results of pre-test and post-test in our active learning approaches based on ICT, i.e. some evidence on improvements of physics education (increasing ratio of correct answer are 50%-level).

Linear hypergeneralization of learned dynamics across movement speeds reveals anisotropic, gain-encoding primitives for motor adaptation.

PubMed

Joiner, Wilsaan M; Ajayi, Obafunso; Sing, Gary C; Smith, Maurice A

2011-01-01

The ability to generalize learned motor actions to new contexts is a key feature of the motor system. For example, the ability to ride a bicycle or swing a racket is often first developed at lower speeds and later applied to faster velocities. A number of previous studies have examined the generalization of motor adaptation across movement directions and found that the learned adaptation decays in a pattern consistent with the existence of motor primitives that display narrow Gaussian tuning. However, few studies have examined the generalization of motor adaptation across movement speeds. Following adaptation to linear velocity-dependent dynamics during point-to-point reaching arm movements at one speed, we tested the ability of subjects to transfer this adaptation to short-duration higher-speed movements aimed at the same target. We found near-perfect linear extrapolation of the trained adaptation with respect to both the magnitude and the time course of the velocity profiles associated with the high-speed movements: a 69% increase in movement speed corresponded to a 74% extrapolation of the trained adaptation. The close match between the increase in movement speed and the corresponding increase in adaptation beyond what was trained indicates linear hypergeneralization. Computational modeling shows that this pattern of linear hypergeneralization across movement speeds is not compatible with previous models of adaptation in which motor primitives display isotropic Gaussian tuning of motor output around their preferred velocities. Instead, we show that this generalization pattern indicates that the primitives involved in the adaptation to viscous dynamics display anisotropic tuning in velocity space and encode the gain between motor output and motion state rather than motor output itself.

Motivational State, Reward Value, and Pavlovian Cues Differentially Affect Skilled Forelimb Grasping in Rats

ERIC Educational Resources Information Center

Mosberger, Alice C.; de Clauser, Larissa; Kasper, Hansjörg; Schwab, Martin E.

2016-01-01

Motor skills represent high-precision movements performed at optimal speed and accuracy. Such motor skills are learned with practice over time. Besides practice, effects of motivation have also been shown to influence speed and accuracy of movements, suggesting that fast movements are performed to maximize gained reward over time as noted in…

MHEG Based Distance Learning System on Information Superhighway.

ERIC Educational Resources Information Center

Lee, SeiHoon; Yoon, KyungSeob; Wang, ChangJong

As the need for distance education grows, requirements for the development of high-speed network-based real-time distance learning systems increases. MHEG-5 is the fifth part of the MHEG (Multimedia and Hypermedia information coding Experts Group) standard, and it defines a final-form representation for application interchange. This paper…

The influence of learning and updating speed on the growth of commercial websites

NASA Astrophysics Data System (ADS)

Wan, Xiaoji; Deng, Guishi; Bai, Yang; Xue, Shaowei

2012-08-01

In this paper, we study the competition model of commercial websites with learning and updating speed, and further analyze the influence of learning and updating speed on the growth of commercial websites from a nonlinear dynamics perspective. Using the center manifold theory and the normal form method, we give the explicit formulas determining the stability and periodic fluctuation of commercial sites. Numerical simulations reveal that sites periodically fluctuate as the speed of learning and updating crosses one threshold. The study provides reference and evidence for website operators to make decisions.

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

NASA Astrophysics Data System (ADS)

Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

2017-05-01

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high-definition video exploitation.

The role of speed versus working memory in predicting learning new information in multiple sclerosis.

PubMed

Chiaravalloti, Nancy D; Stojanovic-Radic, Jelena; DeLuca, John

2013-01-01

The most common cognitive impairments in multiple sclerosis (MS) have been documented in specific domains, including new learning and memory, working memory, and information processing speed. However, little attempt has been made to increase our understanding of their relationship to one another. While recent studies have shown that processing speed impacts new learning and memory abilities in MS, the role of working memory in this relationship has received less attention. The present study examines the relative contribution of impaired working memory versus processing speed in new learning and memory functions in MS. Participants consisted of 51 individuals with clinically definite MS. Participants completed two measures of processing speed, two measures of working memory, and two measures of episodic memory. Data were analyzed via correlational and multiple regression analysis. Results indicate that the variance in new learning abilities in this sample was primarily associated with processing speed, with working memory exerting much less of an influence. Results are discussed in terms of the role of cognitive rehabilitation of new learning and memory abilities in persons with MS.

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes

PubMed Central

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M.; Zimmermann, Ulrich S.; Schlagenhauf, Florian; Smolka, Michael N.; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities. PMID:28642696

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes.

PubMed

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M; Zimmermann, Ulrich S; Schlagenhauf, Florian; Smolka, Michael N; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.

Instantaneous Assessment Of Athletic Performance Using High Speed Video

NASA Astrophysics Data System (ADS)

Hubbard, Mont; Alaways, LeRoy W.

1988-02-01

We describe the use of high speed video to provide quantitative assessment of motion in athletic performance. Besides the normal requirement for accuracy, an essential feature is that the information be provided rapidly enough so that it my serve as valuable feedback in the learning process. The general considerations which must be addressed in the development of such a computer based system are discussed. These ideas are illustrated specifically through the description of a prototype system which has been designed for the javelin throw.

Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and Controls.

PubMed

Kiiski, Hanni; Jollans, Lee; Donnchadha, Seán Ó; Nolan, Hugh; Lonergan, Róisín; Kelly, Siobhán; O'Brien, Marie Claire; Kinsella, Katie; Bramham, Jessica; Burke, Teresa; Hutchinson, Michael; Tubridy, Niall; Reilly, Richard B; Whelan, Robert

2018-05-01

Event-related potentials (ERPs) show promise to be objective indicators of cognitive functioning. The aim of the study was to examine if ERPs recorded during an oddball task would predict cognitive functioning and information processing speed in Multiple Sclerosis (MS) patients and controls at the individual level. Seventy-eight participants (35 MS patients, 43 healthy age-matched controls) completed visual and auditory 2- and 3-stimulus oddball tasks with 128-channel EEG, and a neuropsychological battery, at baseline (month 0) and at Months 13 and 26. ERPs from 0 to 700 ms and across the whole scalp were transformed into 1728 individual spatio-temporal datapoints per participant. A machine learning method that included penalized linear regression used the entire spatio-temporal ERP to predict composite scores of both cognitive functioning and processing speed at baseline (month 0), and months 13 and 26. The results showed ERPs during the visual oddball tasks could predict cognitive functioning and information processing speed at baseline and a year later in a sample of MS patients and healthy controls. In contrast, ERPs during auditory tasks were not predictive of cognitive performance. These objective neurophysiological indicators of cognitive functioning and processing speed, and machine learning methods that can interrogate high-dimensional data, show promise in outcome prediction.

Towards high-speed autonomous navigation of unknown environments

NASA Astrophysics Data System (ADS)

Richter, Charles; Roy, Nicholas

2015-05-01

In this paper, we summarize recent research enabling high-speed navigation in unknown environments for dynamic robots that perceive the world through onboard sensors. Many existing solutions to this problem guarantee safety by making the conservative assumption that any unknown portion of the map may contain an obstacle, and therefore constrain planned motions to lie entirely within known free space. In this work, we observe that safety constraints may significantly limit performance and that faster navigation is possible if the planner reasons about collision with unobserved obstacles probabilistically. Our overall approach is to use machine learning to approximate the expected costs of collision using the current state of the map and the planned trajectory. Our contribution is to demonstrate fast but safe planning using a learned function to predict future collision probabilities.

Emotional Learning Based Intelligent Controllers for Rotor Flux Oriented Control of Induction Motor

NASA Astrophysics Data System (ADS)

Abdollahi, Rohollah; Farhangi, Reza; Yarahmadi, Ali

2014-08-01

This paper presents design and evaluation of a novel approach based on emotional learning to improve the speed control system of rotor flux oriented control of induction motor. The controller includes a neuro-fuzzy system with speed error and its derivative as inputs. A fuzzy critic evaluates the present situation, and provides the emotional signal (stress). The controller modifies its characteristics so that the critics stress is reduced. The comparative simulation results show that the proposed controller is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.

High-speed 3D imaging of cellular activity in the brain using axially-extended beams and light sheets.

PubMed

Hillman, Elizabeth Mc; Voleti, Venkatakaushik; Patel, Kripa; Li, Wenze; Yu, Hang; Perez-Campos, Citlali; Benezra, Sam E; Bruno, Randy M; Galwaduge, Pubudu T

2018-06-01

As optical reporters and modulators of cellular activity have become increasingly sophisticated, the amount that can be learned about the brain via high-speed cellular imaging has increased dramatically. However, despite fervent innovation, point-scanning microscopy is facing a fundamental limit in achievable 3D imaging speeds and fields of view. A range of alternative approaches are emerging, some of which are moving away from point-scanning to use axially-extended beams or sheets of light, for example swept confocally aligned planar excitation (SCAPE) microscopy. These methods are proving effective for high-speed volumetric imaging of the nervous system of small organisms such as Drosophila (fruit fly) and D. Rerio (Zebrafish), and are showing promise for imaging activity in the living mammalian brain using both single and two-photon excitation. This article describes these approaches and presents a simple model that demonstrates key advantages of axially-extended illumination over point-scanning strategies for high-speed volumetric imaging, including longer integration times per voxel, improved photon efficiency and reduced photodamage. Copyright © 2018 Elsevier Ltd. All rights reserved.

Lessons Learned in the High-Speed Aerodynamic Research Programs of the NACA/NASA

NASA Technical Reports Server (NTRS)

Spearman, M. Leroy

2004-01-01

The achievement of flight with manned, powered, heavier-than-air aircraft in 1903 marked the beginning of a new era in the means of transportation. A special advantage for aircraft was in speed. However, when an aircraft penetrates the air at very high speeds, the disturbed air is compressed and there are changes in the density, pressure and temperature of the air. These compressibility effects change the aerodynamic characteristics of an aircraft and introduce problems in drag, stability and control. Many aircraft designed in the post-World War II era were plagued with the effects of compressibility. Accordingly, the study of the aerodynamic behavior of aircraft, spacecraft and missiles at high-speed became a major part of the research activity of the NACA/NASA. The intent of the research was to determine the causes and provide some solutions for the aerodynamic problems resulting from the effects of compressibility. The purpose of this paper is to review some of the high-speed aerodynamic research work conducted at the Langley Research Center from the viewpoint of the author who has been active in much of the effort.

Estimating the circuit delay of FPGA with a transfer learning method

NASA Astrophysics Data System (ADS)

Cui, Xiuhai; Liu, Datong; Peng, Yu; Peng, Xiyuan

2017-10-01

With the increase of FPGA (Field Programmable Gate Array, FPGA) functionality, FPGA has become an on-chip system platform. Due to increase the complexity of FPGA, estimating the delay of FPGA is a very challenge work. To solve the problems, we propose a transfer learning estimation delay (TLED) method to simplify the delay estimation of different speed grade FPGA. In fact, the same style different speed grade FPGA comes from the same process and layout. The delay has some correlation among different speed grade FPGA. Therefore, one kind of speed grade FPGA is chosen as a basic training sample in this paper. Other training samples of different speed grade can get from the basic training samples through of transfer learning. At the same time, we also select a few target FPGA samples as training samples. A general predictive model is trained by these samples. Thus one kind of estimation model is used to estimate different speed grade FPGA circuit delay. The framework of TRED includes three phases: 1) Building a basic circuit delay library which includes multipliers, adders, shifters, and so on. These circuits are used to train and build the predictive model. 2) By contrasting experiments among different algorithms, the forest random algorithm is selected to train predictive model. 3) The target circuit delay is predicted by the predictive model. The Artix-7, Kintex-7, and Virtex-7 are selected to do experiments. Each of them includes -1, -2, -2l, and -3 different speed grade. The experiments show the delay estimation accuracy score is more than 92% with the TLED method. This result shows that the TLED method is a feasible delay assessment method, especially in the high-level synthesis stage of FPGA tool, which is an efficient and effective delay assessment method.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

PubMed

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

PubMed Central

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-01-01

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. PMID:28394270

An ASIC memory buffer controller for a high speed disk system

NASA Technical Reports Server (NTRS)

Hodson, Robert F.; Campbell, Steve

1993-01-01

The need for large capacity, high speed mass memory storage devices has become increasingly evident at NASA during the past decade. High performance mass storage systems are crucial to present and future NASA systems. Spaceborne data storage system requirements have grown in response to the increasing amounts of data generated and processed by orbiting scientific experiments. Predictions indicate increases in the volume of data by orders of magnitude during the next decade. Current predictions are for storage capacities on the order of terabits (Tb), with data rates exceeding one gigabit per second (Gbps). As part of the design effort for a state of the art mass storage system, NASA Langley has designed a 144 CMOS ASIC to support high speed data transfers. This paper discusses the system architecture, ASIC design and some of the lessons learned in the development process.

Effect of a coteaching handwriting program for first graders: one-group pretest-posttest design.

PubMed

Case-Smith, Jane; Holland, Terri; Lane, Alison; White, Susan

2012-01-01

We examined the effects of a cotaught handwriting and writing program on first-grade students grouped by low, average, and high baseline legibility. The program's aim was to increase legibility, handwriting speed, writing fluency, and written expression in students with diverse learning needs. Thirty-six first-grade students in two classrooms participated in a 12-wk handwriting and writing program cotaught by teachers and an occupational therapist. Students were assessed at pretest, posttest, and 6-mo follow-up using the Evaluation Tool of Children's Handwriting-Manuscript (ETCH-M) and the Woodcock-Johnson Writing Fluency and Writing Samples tests. Students made large gains in ETCH-M legibility (η² = .74), speed (η²s = .52-.65), Writing Fluency (η² = .58), and Writing Samples (η² = .59). Students with initially low legibility improved most in legibility; progress on the other tests was similar across low-, average-, and high-performing groups. This program appeared to benefit first-grade students with diverse learning needs and to increase handwriting legibility and speed and writing fluency. Copyright © 2012 by the American Occupational Therapy Association, Inc.

High-Speed Photonic Reservoir Computing Using a Time-Delay-Based Architecture: Million Words per Second Classification

NASA Astrophysics Data System (ADS)

Larger, Laurent; Baylón-Fuentes, Antonio; Martinenghi, Romain; Udaltsov, Vladimir S.; Chembo, Yanne K.; Jacquot, Maxime

2017-01-01

Reservoir computing, originally referred to as an echo state network or a liquid state machine, is a brain-inspired paradigm for processing temporal information. It involves learning a "read-out" interpretation for nonlinear transients developed by high-dimensional dynamics when the latter is excited by the information signal to be processed. This novel computational paradigm is derived from recurrent neural network and machine learning techniques. It has recently been implemented in photonic hardware for a dynamical system, which opens the path to ultrafast brain-inspired computing. We report on a novel implementation involving an electro-optic phase-delay dynamics designed with off-the-shelf optoelectronic telecom devices, thus providing the targeted wide bandwidth. Computational efficiency is demonstrated experimentally with speech-recognition tasks. State-of-the-art speed performances reach one million words per second, with very low word error rate. Additionally, to record speed processing, our investigations have revealed computing-efficiency improvements through yet-unexplored temporal-information-processing techniques, such as simultaneous multisample injection and pitched sampling at the read-out compared to information "write-in".

Combining High-Speed Cameras and Stop-Motion Animation Software to Support Students' Modeling of Human Body Movement

NASA Astrophysics Data System (ADS)

Lee, Victor R.

2015-04-01

Biomechanics, and specifically the biomechanics associated with human movement, is a potentially rich backdrop against which educators can design innovative science teaching and learning activities. Moreover, the use of technologies associated with biomechanics research, such as high-speed cameras that can produce high-quality slow-motion video, can be deployed in such a way to support students' participation in practices of scientific modeling. As participants in classroom design experiment, fifteen fifth-grade students worked with high-speed cameras and stop-motion animation software (SAM Animation) over several days to produce dynamic models of motion and body movement. The designed series of learning activities involved iterative cycles of animation creation and critique and use of various depictive materials. Subsequent analysis of flipbooks of human jumping movements created by the students at the beginning and end of the unit revealed a significant improvement in both the epistemic fidelity of students' representations. Excerpts from classroom observations highlight the role that the teacher plays in supporting students' thoughtful reflection of and attention to slow-motion video. In total, this design and research intervention demonstrates that the combination of technologies, activities, and teacher support can lead to improvements in some of the foundations associated with students' modeling.

Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

NASA Astrophysics Data System (ADS)

Zhou, XueFei

2018-04-01

With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

Hubble Space Telescope high speed photometer orbital verification

NASA Technical Reports Server (NTRS)

Richards, Evan E.

1991-01-01

The purpose of this report is to provide a summary of the results of the HSP (High Speed Photometer) Orbital Verification (OV) tests and to report conclusions and lessons learned from the initial operations of the HSP. The HSP OV plan covered the activities through fine (phase 3) alignment. This report covers all activities (OV, SV, and SAO) from launch to the completion of phase 3 alignment. Those activities in this period that are not OV tests are described to the extent that they relate to OV activities.

Developing and Testing a Mobile Learning Games Framework

ERIC Educational Resources Information Center

Busch, Carsten; Claßnitz, Sabine; Selmanagic,, André; Steinicke, Martin

2015-01-01

In 2010 1.1 million pupils took private lessons in Germany, with 25% of all German children by the age of 17 having attended paid private lessons at some point in their school career (Klemm & Klemm, 2010). The high demand for support for learning curricular content led us to consider an integrated solution that speeds up both the design of…

Hierarchical representation and machine learning from faulty jet engine behavioral examples to detect real time abnormal conditions

NASA Technical Reports Server (NTRS)

Gupta, U. K.; Ali, M.

1988-01-01

The theoretical basis and operation of LEBEX, a machine-learning system for jet-engine performance monitoring, are described. The behavior of the engine is modeled in terms of four parameters (the rotational speeds of the high- and low-speed sections and the exhaust and combustion temperatures), and parameter variations indicating malfunction are transformed into structural representations involving instances and events. LEBEX extracts descriptors from a set of training data on normal and faulty engines, represents them hierarchically in a knowledge base, and uses them to diagnose and predict faults on a real-time basis. Diagrams of the system architecture and printouts of typical results are shown.

ATM: Restructing Learning for Deaf Students.

ERIC Educational Resources Information Center

Keefe, Barbara; Stockford, David

Governor Baxter School for the Deaf is one of six Maine pilot sites chosen by NYNEX to showcase asynchronous transfer mode (ATM) technology. ATM is a network connection that allows high bandwidth transmission of data, voice, and video. Its high speed capability allows for high quality two-way full-motion video, which is especially beneficial to a…

Effects of information processing speed on learning, memory, and executive functioning in people living with HIV/AIDS.

PubMed

Fellows, Robert P; Byrd, Desiree A; Morgello, Susan

2014-01-01

It is unclear whether or to what degree literacy, aging, and other neurologic abnormalities relate to cognitive deficits among people living with HIV/AIDS in the combined antiretroviral therapy (CART) era. The primary aim of this study was to simultaneously examine the association of age, HIV-associated motor abnormalities, major depressive disorder, and reading level with information processing speed, learning, memory, and executive functions, and to determine whether processing speed mediated any of the relationships between cognitive and noncognitive variables. Participants were 186 racially and ethnically diverse men and women living with HIV/AIDS who underwent comprehensive neurological, neuropsychological, and medical evaluations. Structural equation modeling was utilized to assess the extent to which information processing speed mediated the relationship between age, motor abnormalities, major depressive disorder, and reading level with other cognitive abilities. Age, motor dysfunction, reading level, and current major depressive disorder were all significantly associated with information processing speed. Information processing speed fully mediated the effects of age on learning, memory, and executive functioning and partially mediated the effect of major depressive disorder on learning and memory. The effect of motor dysfunction on learning and memory was fully mediated by processing speed. These findings provide support for information processing speed as a primary deficit, which may account, at least in part, for many of the other cognitive abnormalities recognized in complex HIV/AIDS populations. The association of age and information processing speed may account for HIV/aging synergies in the generation of CART-era cognitive abnormalities.

Low visual information-processing speed and attention are predictors of fatigue in elementary and junior high school students

PubMed Central

2011-01-01

Background Fatigue is a common complaint among elementary and junior high school students, and is known to be associated with reduced academic performance. Recently, we demonstrated that fatigue was correlated with decreased cognitive function in these students. However, no studies have identified cognitive predictors of fatigue. Therefore, we attempted to determine independent cognitive predictors of fatigue in these students. Methods We performed a prospective cohort study. One hundred and forty-two elementary and junior high school students without fatigue participated. They completed a variety of paper-and-pencil tests, including list learning and list recall tests, kana pick-out test, semantic fluency test, figure copying test, digit span forward test, and symbol digit modalities test. The participants also completed computerized cognitive tests (tasks A to E on the modified advanced trail making test). These cognitive tests were used to evaluate motor- and information-processing speed, immediate and delayed memory function, auditory and visual attention, divided and switching attention, retrieval of learned material, and spatial construction. One year after the tests, a questionnaire about fatigue (Japanese version of the Chalder Fatigue Scale) was administered to all the participants. Results After the follow-up period, we confirmed 40 cases of fatigue among 118 students. In multivariate logistic regression analyses adjusted for grades and gender, poorer performance on visual information-processing speed and attention tasks was associated with increased risk of fatigue. Conclusions Reduced visual information-processing speed and poor attention are independent predictors of fatigue in elementary and junior high school students. PMID:21672212

Low visual information-processing speed and attention are predictors of fatigue in elementary and junior high school students.

PubMed

Mizuno, Kei; Tanaka, Masaaki; Fukuda, Sanae; Yamano, Emi; Shigihara, Yoshihito; Imai-Matsumura, Kyoko; Watanabe, Yasuyoshi

2011-06-14

Fatigue is a common complaint among elementary and junior high school students, and is known to be associated with reduced academic performance. Recently, we demonstrated that fatigue was correlated with decreased cognitive function in these students. However, no studies have identified cognitive predictors of fatigue. Therefore, we attempted to determine independent cognitive predictors of fatigue in these students. We performed a prospective cohort study. One hundred and forty-two elementary and junior high school students without fatigue participated. They completed a variety of paper-and-pencil tests, including list learning and list recall tests, kana pick-out test, semantic fluency test, figure copying test, digit span forward test, and symbol digit modalities test. The participants also completed computerized cognitive tests (tasks A to E on the modified advanced trail making test). These cognitive tests were used to evaluate motor- and information-processing speed, immediate and delayed memory function, auditory and visual attention, divided and switching attention, retrieval of learned material, and spatial construction. One year after the tests, a questionnaire about fatigue (Japanese version of the Chalder Fatigue Scale) was administered to all the participants. After the follow-up period, we confirmed 40 cases of fatigue among 118 students. In multivariate logistic regression analyses adjusted for grades and gender, poorer performance on visual information-processing speed and attention tasks was associated with increased risk of fatigue. Reduced visual information-processing speed and poor attention are independent predictors of fatigue in elementary and junior high school students. © 2011 Mizuno et al; licensee BioMed Central Ltd.

Learning in Neural Networks: VLSI Implementation Strategies

NASA Technical Reports Server (NTRS)

Duong, Tuan Anh

1995-01-01

Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

Different propagation speeds of recalled sequences in plastic spiking neural networks

NASA Astrophysics Data System (ADS)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in experiments.

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

NASA Astrophysics Data System (ADS)

Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics.

PubMed

Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

Predicting Word Reading and Comprehension with Executive Function and Speed Measures Across Development: A Latent Variable Analysis

PubMed Central

Christopher, Micaela E.; Miyake, Akira; Keenan, Janice M.; Pennington, Bruce; DeFries, John C.; Wadsworth, Sally J.; Willcutt, Erik; Olson, Richard K.

2012-01-01

The present study explored whether different executive control and speed measures (working memory, inhibition, processing speed, and naming speed) independently predict individual differences in word reading and reading comprehension. Although previous studies suggest these cognitive constructs are important for reading, we analyze the constructs simultaneously to test whether each is a unique predictor. We used latent variables from 483 participants (ages 8 to 16) to portion each cognitive and reading construct into its unique and shared variance. In these models we address two specific issues: (a) given that our wide age range may span the theoretical transition from “learning to read” to “reading to learn,” we first test whether the relation between word reading and reading comprehension is stable across two age groups (ages 8 to 10 and 11 to 16); and (b) the main theoretical question of interest: whether what is shared and what is separable for word reading and reading comprehension are associated with individual differences in working memory, inhibition, and measures of processing and naming speed. The results indicated that: (a) the relation between word reading and reading comprehension is largely invariant across the age groups; (b) working memory and general processing speed, but not inhibition or the speeded naming of non-alphanumeric stimuli, are unique predictors of both word reading and comprehension, with working memory equally important for both reading abilities and processing speed more important for word reading. These results have implications for understanding why reading comprehension and word reading are highly correlated yet separable. PMID:22352396

Cognitive Risk Factors for Specific Learning Disorder: Processing Speed, Temporal Processing, and Working Memory.

PubMed

Moll, Kristina; Göbel, Silke M; Gooch, Debbie; Landerl, Karin; Snowling, Margaret J

2016-01-01

High comorbidity rates between reading disorder (RD) and mathematics disorder (MD) indicate that, although the cognitive core deficits underlying these disorders are distinct, additional domain-general risk factors might be shared between the disorders. Three domain-general cognitive abilities were investigated in children with RD and MD: processing speed, temporal processing, and working memory. Since attention problems frequently co-occur with learning disorders, the study examined whether these three factors, which are known to be associated with attention problems, account for the comorbidity between these disorders. The sample comprised 99 primary school children in four groups: children with RD, children with MD, children with both disorders (RD+MD), and typically developing children (TD controls). Measures of processing speed, temporal processing, and memory were analyzed in a series of ANCOVAs including attention ratings as covariate. All three risk factors were associated with poor attention. After controlling for attention, associations with RD and MD differed: Although deficits in verbal memory were associated with both RD and MD, reduced processing speed was related to RD, but not MD; and the association with RD was restricted to processing speed for familiar nameable symbols. In contrast, impairments in temporal processing and visuospatial memory were associated with MD, but not RD. © Hammill Institute on Disabilities 2014.

No Trade-Off between Learning Speed and Associative Flexibility in Bumblebees: A Reversal Learning Test with Multiple Colonies

PubMed Central

Raine, Nigel E.; Chittka, Lars

2012-01-01

Potential trade-offs between learning speed and memory-related performance could be important factors in the evolution of learning. Here, we test whether rapid learning interferes with the acquisition of new information using a reversal learning paradigm. Bumblebees (Bombus terrestris) were trained to associate yellow with a floral reward. Subsequently the association between colour and reward was reversed, meaning bees then had to learn to visit blue flowers. We demonstrate that individuals that were fast to learn yellow as a predictor of reward were also quick to reverse this association. Furthermore, overnight memory retention tests suggest that faster learning individuals are also better at retaining previously learned information. There is also an effect of relatedness: colonies whose workers were fast to learn the association between yellow and reward also reversed this association rapidly. These results are inconsistent with a trade-off between learning speed and the reversal of a previously made association. On the contrary, they suggest that differences in learning performance and cognitive (behavioural) flexibility could reflect more general differences in colony learning ability. Hence, this study provides additional evidence to support the idea that rapid learning and behavioural flexibility have adaptive value. PMID:23028779

Verbal Knowledge, Working Memory, and Processing Speed as Predictors of Verbal Learning in Older Adults

ERIC Educational Resources Information Center

Rast, Philippe

2011-01-01

The present study aimed at modeling individual differences in a verbal learning task by means of a latent structured growth curve approach based on an exponential function that yielded 3 parameters: initial recall, learning rate, and asymptotic performance. Three cognitive variables--speed of information processing, verbal knowledge, working…

Constructing Global Competence through Relationship Building in Mexican High Schools

ERIC Educational Resources Information Center

Petro, Lisa; Garin, Maria Jose Pineda

2017-01-01

As globalization speeds forward, there is immense pressure on school systems to keep up with the changing world. School leaders and teachers must continuously reevaluate their students' needs and consider the forces that will shape their futures. Learn how a dynamic, multi-campus high school in Mexico reimagined its approach to global competence…

What It Has or What It Does Not Have? Signposts from US Data for Rural Children's Digital Access to Informal Learning

ERIC Educational Resources Information Center

Mardis, Marcia A.

2013-01-01

A fifth of US children live in rural areas with limited access to the informal learning opportunities available to their metropolitan counterparts. High-speed broadband internet access can be an important vehicle for delivering opportunities at home and outside of the classroom. In an attempt to explore what current data say about children's…

Above-real-time training (ARTT) improves transfer to a simulated flight control task.

PubMed

Donderi, D C; Niall, Keith K; Fish, Karyn; Goldstein, Benjamin

2012-06-01

The aim of this study was to measure the effects of above-real-time-training (ARTT) speed and screen resolution on a simulated flight control task. ARTT has been shown to improve transfer to the criterion task in some military simulation experiments. We tested training speed and screen resolution in a project, sponsored by Defence Research and Development Canada, to develop components for prototype air mission simulators. For this study, 54 participants used a single-screen PC-based flight simulation program to learn to chase and catch an F-18A fighter jet with another F-18A while controlling the chase aircraft with a throttle and side-stick controller. Screen resolution was varied between participants, and training speed was varied factorially across two sessions within participants. Pretest and posttest trials were at high resolution and criterion (900 knots) speed. Posttest performance was best with high screen resolution training and when one ARTT training session was followed by a session of criterion speed training. ARTT followed by criterion training improves performance on a visual-motor coordination task. We think that ARTT influences known facilitators of transfer, including similarity to the criterion task and contextual interference. Use high-screen resolution, start with ARTT, and finish with criterion speed training when preparing a mission simulation.

INDIVIDUAL DIFFERENCES IN TASK-SPECIFIC PAIRED ASSOCIATES LEARNING IN OLDER ADULTS: THE ROLE OF PROCESSING SPEED AND WORKING MEMORY

PubMed Central

Kurtz, Tanja; Mogle, Jacqueline; Sliwinski, Martin J.; Hofer, Scott M.

2013-01-01

Background The role of processing speed and working memory was investigated in terms of individual differences in task-specific paired associates learning in a sample of older adults. Task-specific learning, as distinct from content-oriented item-specific learning, refers to gains in performance due to repeated practice on a learning task in which the to-be-learned material changes over trials. Methods Learning trajectories were modeled within an intensive repeated-measures design based on participants obtained from an opt-in internet-based sampling service (Mage = 65.3, SD = 4.81). Participants completed an eight-item paired associates task daily over a seven-day period. Results Results indicated that a three-parameter hyperbolic model (i.e., initial level, learning rate, and asymptotic performance) best described learning trajectory. After controlling for age-related effects, both higher working memory and higher processing speed had a positive effect on all three learning parameters. Conclusion These results emphasize the role of cognitive abilities for individual differences in task-specific learning of older adults. PMID:24151913

The Effect of Interactive, Three Dimensional, High Speed Simulations on High School Science Students' Conceptions of the Molecular Structure of Water.

ERIC Educational Resources Information Center

Hakerem, Gita; And Others

The Water and Molecular Networks (WAMNet) Project uses graduate student written Reduced Instruction Set Computing (RISC) computer simulations of the molecular structure of water to assist high school students learn about the nature of water. This study examined: (1) preconceptions concerning the molecular structure of water common among high…

Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations.

PubMed

Richmond, Paul; Buesing, Lars; Giugliano, Michele; Vasilaki, Eleni

2011-05-04

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated.

ARC-2006-ACD06-0179-003

NASA Image and Video Library

2006-10-12

Ames holds a Media Day at the Hypervelocity Free Flight facility where Ames is conducting high-speed tests of small models of the agency's new Orion CEV to learn about stability during flight. The hypervelocity test facility uses a gun to shoot Orion models between 0.5 and l.5 inches (1.25 - 3.75 centimeters in diameter. The facility can conduct experiments with speeds up to 19,000 miles per hour (30,400 kilometers per hour) with John Bluck (Ames PAO) and Chuck Cornelison Ames Engineer

ARC-2006-ACD06-0179-016

NASA Image and Video Library

2006-10-12

Ames holds a Media Day at the Hypervelocity Free Flight facility where Ames is conducting high-speed tests of small models of the agency's new Orion CEV to learn about stability during flight. The hypervelocity test facility uses a gun to shoot Orion models between 0.5 and l.5 inches (1.25 - 3.75 centimeters in diameter. The facility can conduct experiments with speeds up to 19,000 miles per hour (30,400 kilometers per hour) - Wayne Freedman, ABC Channel 7 news inerviews Jeff Brown of Ames

ARC-2006-ACD06-0179-005

NASA Image and Video Library

2006-10-12

Ames holds a Media Day at the Hypervelocity Free Flight facility where Ames is conducting high-speed tests of small models of the agency's new Orion CEV to learn about stability during flight. The hypervelocity test facility uses a gun to shoot Orion models between 0.5 and l.5 inches (1.25 - 3.75 centimeters in diameter. The facility can conduct experiments with speeds up to 19,000 miles per hour (30,400 kilometers per hour) - Gary Reyes, San Jose mercury New interviews Chuck Cornelison

Super Power for Effortless and Efficient Digest of Language for Expression with Alpha Rhythms in Nature [SPEED LEARN].

ERIC Educational Resources Information Center

Matthews, Doris B.

Project "SPEED LEARN," a study which compared learning rates in beginning French and other behavior characteristics between adolescents with alpha training and comparable students without such training, was conducted with 62 rural subjects randomly divided into experimental and control groups. Both groups participated in an 8-week…

Noise-enhanced clustering and competitive learning algorithms.

PubMed

Osoba, Osonde; Kosko, Bart

2013-01-01

Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning. Copyright © 2012 Elsevier Ltd. All rights reserved.

A reward optimization method based on action subrewards in hierarchical reinforcement learning.

PubMed

Fu, Yuchen; Liu, Quan; Ling, Xionghong; Cui, Zhiming

2014-01-01

Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are "trial and error" and "related reward." A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of "curse of dimensionality," which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The "curse of dimensionality" problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

PSP Measurement of Stator Vane Surface Pressures in a High Speed Fan

NASA Technical Reports Server (NTRS)

Lepicovsky, Jan

1998-01-01

This paper presents measurements of static pressures on the stator vane suction side of a high-speed single stage fan using the technique of pressure sensitive paint (PSP). The paper illustrates development in application of the relatively new experimental technique to the complex environment of internal flows in turbomachines. First, there is a short explanation of the physics of the PSP technique and a discussion of calibration methods for pressure sensitive paint in the turbomachinery environment. A description of the image conversion process follows. The recorded image of the stator vane pressure field is skewed due to the limited optical access and must be converted to the meridional plane projection for comparison with analytical predictions. The experimental results for seven operating conditions along an off-design rotational speed line are shown in a concise form, including performance map points, mindspan static tap pressure distributions, and vane suction side pressure fields. Then, a comparison between static tap and pressure sensitive paint data is discussed. Finally, the paper lists shortcomings of the pressure sensitive paint technology and lessons learned in this high-speed fan application.

Machine learning of molecular properties: Locality and active learning

NASA Astrophysics Data System (ADS)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

The Science Resource Area in the State-of-the-Art High School.

ERIC Educational Resources Information Center

Biehle, James T.

2000-01-01

Examines areas that are part of a flexible and integrated science facility within state-of-the-art high schools that allow students to progress at their own speed and learn in their most effective manner. Areas described include outdoor, greenhouse, biological wastewater treatment, controlled environment, and student and faculty meeting areas. (GR)

Effects of the dietary approaches to stop hypertension diet, exercise, and caloric restriction on neurocognition in overweight adults with high blood pressure.

PubMed

Smith, Patrick J; Blumenthal, James A; Babyak, Michael A; Craighead, Linda; Welsh-Bohmer, Kathleen A; Browndyke, Jeffrey N; Strauman, Timothy A; Sherwood, Andrew

2010-06-01

High blood pressure increases the risks of stroke, dementia, and neurocognitive dysfunction. Although aerobic exercise and dietary modifications have been shown to reduce blood pressure, no randomized trials have examined the effects of aerobic exercise combined with dietary modification on neurocognitive functioning in individuals with high blood pressure (ie, prehypertension and stage 1 hypertension). As part of a larger investigation, 124 participants with elevated blood pressure (systolic blood pressure 130 to 159 mm Hg or diastolic blood pressure 85 to 99 mm Hg) who were sedentary and overweight or obese (body mass index: 25 to 40 kg/m(2)) were randomized to the Dietary Approaches to Stop Hypertension (DASH) diet alone, DASH combined with a behavioral weight management program including exercise and caloric restriction, or a usual diet control group. Participants completed a battery of neurocognitive tests of executive function-memory-learning and psychomotor speed at baseline and again after the 4-month intervention. Participants on the DASH diet combined with a behavioral weight management program exhibited greater improvements in executive function-memory-learning (Cohen's D=0.562; P=0.008) and psychomotor speed (Cohen's D=0.480; P=0.023), and DASH diet alone participants exhibited better psychomotor speed (Cohen's D=0.440; P=0.036) compared with the usual diet control. Neurocognitive improvements appeared to be mediated by increased aerobic fitness and weight loss. Also, participants with greater intima-medial thickness and higher systolic blood pressure showed greater improvements in executive function-memory-learning in the group on the DASH diet combined with a behavioral weight management program. In conclusion, combining aerobic exercise with the DASH diet and caloric restriction improves neurocognitive function among sedentary and overweight/obese individuals with prehypertension and hypertension.

Fast Back-Propagation Learning Using Steep Activation Functions and Automatic Weight

Treesearch

Tai-Hoon Cho; Richard W. Conners; Philip A. Araman

1992-01-01

In this paper, several back-propagation (BP) learning speed-up algorithms that employ the ãgainä parameter, i.e., steepness of the activation function, are examined. Simulations will show that increasing the gain seemingly increases the speed of convergence and that these algorithms can converge faster than the standard BP learning algorithm on some problems. However,...

The Relationship between Multiplication Fact Speed-Recall and Fluency and Higher Level Mathematics Learning with Eighth Grade Middle School Students

ERIC Educational Resources Information Center

Curry, Steven James

2012-01-01

This quantitative study investigated relationships between higher level mathematics learning and multiplication fact fluency, multiplication fact speed-recall, and reading grade equivalency of eighth grade students in Algebra I and Pre-Algebra. Higher level mathematics learning was indicated by an average score of 80% or higher on first and second…

Using Multiple Big Datasets and Machine Learning to Produce a New Global Particulate Dataset: A Technology Challenge Case Study

NASA Astrophysics Data System (ADS)

Lary, D. J.

2013-12-01

A BigData case study is described where multiple datasets from several satellites, high-resolution global meteorological data, social media and in-situ observations are combined using machine learning on a distributed cluster using an automated workflow. The global particulate dataset is relevant to global public health studies and would not be possible to produce without the use of the multiple big datasets, in-situ data and machine learning.To greatly reduce the development time and enhance the functionality a high level language capable of parallel processing has been used (Matlab). A key consideration for the system is high speed access due to the large data volume, persistence of the large data volumes and a precise process time scheduling capability.

Hierarchical extreme learning machine based reinforcement learning for goal localization

NASA Astrophysics Data System (ADS)

AlDahoul, Nouar; Zaw Htike, Zaw; Akmeliawati, Rini

2017-03-01

The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB.

There is plenty of room for motor learning at the bottom of the Fugl-Meyer: Acquisition of a novel bimanual wheelchair skill after chronic stroke using an unmasking technology.

PubMed

Sarigul-Klijn, Yasemin; Lobo-Prat, Joan; Smith, Brendan W; Thayer, Sage; Zondervan, Daniel; Chan, Vicky; Stoller, Oliver; Reinkensmeyer, David J

2017-07-01

Many people with a stroke have a severely paretic arm, and it is often assumed that they are unable to learn novel, skilled behaviors that incorporate use of that arm. Here, we show that a group of people with chronic stroke (n = 5, upper extremity Fugl-Meyer scores: 31, 30, 26, 22, 8) learned to use their impaired arm to propel a novel, yoked-clutch lever drive wheelchair. Over six daily training sessions, each involving about 134 training movements with their "useless" arm, the users gradually achieved a 3-fold increase in wheelchair speed on average, with a 4-6 fold increase for three of the participants. They did this by learning a bimanual skill: pushing the levers with both arms while activating the yoked-clutches at the right time with their ipsilesional (i.e. "good") hand to propel the wheelchair forward. They perceived the task as highly motivating and useful. The speed improvements exceeded a 1.5-factor improvement observed when young, unimpaired users learned to propel the chair. The learning rate also exceeded a sample of learning rates from a variety of classic learning studies. These results suggest that appropriately-designed assistive technologies (or "unmasking technologies - UTs") can unleash a powerful, latent ability for motor learning even for severely paretic arms. While UTs may not reduce clinical impairment, they may facilitate large improvements in a specific functional ability.

A single exercise bout and locomotor learning after stroke: physiological, behavioural, and computational outcomes.

PubMed

Charalambous, Charalambos C; Alcantara, Carolina C; French, Margaret A; Li, Xin; Matt, Kathleen S; Kim, Hyosub E; Morton, Susanne M; Reisman, Darcy S

2018-05-15

Previous work demonstrated an effect of a single high-intensity exercise bout coupled with motor practice on the retention of a newly acquired skilled arm movement, in both neurologically intact and impaired adults. In the present study, using behavioural and computational analyses we demonstrated that a single exercise bout, regardless of its intensity and timing, did not increase the retention of a novel locomotor task after stroke. Considering both present and previous work, we postulate that the benefits of exercise effect may depend on the type of motor learning (e.g. skill learning, sensorimotor adaptation) and/or task (e.g. arm accuracy-tracking task, walking). Acute high-intensity exercise coupled with motor practice improves the retention of motor learning in neurologically intact adults. However, whether exercise could improve the retention of locomotor learning after stroke is still unknown. Here, we investigated the effect of exercise intensity and timing on the retention of a novel locomotor learning task (i.e. split-belt treadmill walking) after stroke. Thirty-seven people post stroke participated in two sessions, 24 h apart, and were allocated to active control (CON), treadmill walking (TMW), or total body exercise on a cycle ergometer (TBE). In session 1, all groups exercised for a short bout (∼5 min) at low (CON) or high (TMW and TBE) intensity and before (CON and TMW) or after (TBE) the locomotor learning task. In both sessions, the locomotor learning task was to walk on a split-belt treadmill in a 2:1 speed ratio (100% and 50% fast-comfortable walking speed) for 15 min. To test the effect of exercise on 24 h retention, we applied behavioural and computational analyses. Behavioural data showed that neither high-intensity group showed greater 24 h retention compared to CON, and computational data showed that 24 h retention was attributable to a slow learning process for sensorimotor adaptation. Our findings demonstrated that acute exercise coupled with a locomotor adaptation task, regardless of its intensity and timing, does not improve retention of the novel locomotor task after stroke. We postulate that exercise effects on motor learning may be context specific (e.g. type of motor learning and/or task) and interact with the presence of genetic variant (BDNF Val66Met). © 2018 The Authors. The Journal of Physiology © 2018 The Physiological Society.

Learned value and object perception: Accelerated perception or biased decisions?

PubMed

Rajsic, Jason; Perera, Harendri; Pratt, Jay

2017-02-01

Learned value is known to bias visual search toward valued stimuli. However, some uncertainty exists regarding the stage of visual processing that is modulated by learned value. Here, we directly tested the effect of learned value on preattentive processing using temporal order judgments. Across four experiments, we imbued some stimuli with high value and some with low value, using a nonmonetary reward task. In Experiment 1, we replicated the value-driven distraction effect, validating our nonmonetary reward task. Experiment 2 showed that high-value stimuli, but not low-value stimuli, exhibit a prior-entry effect. Experiment 3, which reversed the temporal order judgment task (i.e., reporting which stimulus came second), showed no prior-entry effect, indicating that although a response bias may be present for high-value stimuli, they are still reported as appearing earlier. However, Experiment 4, using a simultaneity judgment task, showed no shift in temporal perception. Overall, our results support the conclusion that learned value biases perceptual decisions about valued stimuli without speeding preattentive stimulus processing.

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems

PubMed Central

Tuo, Shouheng; Yong, Longquan; Deng, Fang’an; Li, Yanhai; Lin, Yong; Lu, Qiuju

2017-01-01

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application. PMID:28403224

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

PubMed

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an; Li, Yanhai; Lin, Yong; Lu, Qiuju

2017-01-01

Harmony Search (HS) and Teaching-Learning-Based Optimization (TLBO) as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

Precision Parameter Estimation and Machine Learning

NASA Astrophysics Data System (ADS)

Wandelt, Benjamin D.

2008-12-01

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

Path integration of head direction: updating a packet of neural activity at the correct speed using axonal conduction delays.

PubMed

Walters, Daniel; Stringer, Simon; Rolls, Edmund

2013-01-01

The head direction cell system is capable of accurately updating its current representation of head direction in the absence of visual input. This is known as the path integration of head direction. An important question is how the head direction cell system learns to perform accurate path integration of head direction. In this paper we propose a model of velocity path integration of head direction in which the natural time delay of axonal transmission between a linked continuous attractor network and competitive network acts as a timing mechanism to facilitate the correct speed of path integration. The model effectively learns a "look-up" table for the correct speed of path integration. In simulation, we show that the model is able to successfully learn two different speeds of path integration across two different axonal conduction delays, and without the need to alter any other model parameters. An implication of this model is that, by learning look-up tables for each speed of path integration, the model should exhibit a degree of robustness to damage. In simulations, we show that the speed of path integration is not significantly affected by degrading the network through removing a proportion of the cells that signal rotational velocity.

Path Integration of Head Direction: Updating a Packet of Neural Activity at the Correct Speed Using Axonal Conduction Delays

PubMed Central

Walters, Daniel; Stringer, Simon; Rolls, Edmund

2013-01-01

The head direction cell system is capable of accurately updating its current representation of head direction in the absence of visual input. This is known as the path integration of head direction. An important question is how the head direction cell system learns to perform accurate path integration of head direction. In this paper we propose a model of velocity path integration of head direction in which the natural time delay of axonal transmission between a linked continuous attractor network and competitive network acts as a timing mechanism to facilitate the correct speed of path integration. The model effectively learns a “look-up” table for the correct speed of path integration. In simulation, we show that the model is able to successfully learn two different speeds of path integration across two different axonal conduction delays, and without the need to alter any other model parameters. An implication of this model is that, by learning look-up tables for each speed of path integration, the model should exhibit a degree of robustness to damage. In simulations, we show that the speed of path integration is not significantly affected by degrading the network through removing a proportion of the cells that signal rotational velocity. PMID:23526976

The time course of explicit and implicit categorization.

PubMed

Smith, J David; Zakrzewski, Alexandria C; Herberger, Eric R; Boomer, Joseph; Roeder, Jessica L; Ashby, F Gregory; Church, Barbara A

2015-10-01

Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization.

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

PubMed

Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

2018-01-10

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

Time and Associative Learning.

PubMed

Balsam, Peter D; Drew, Michael R; Gallistel, C R

2010-01-01

In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.

Time and Associative Learning

PubMed Central

Balsam, Peter D; Drew, Michael R.; Gallistel, C.R.

2010-01-01

In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by “temporal pairing” and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities. PMID:21359131

University Students Use of Computers and Mobile Devices for Learning and Their Reading Speed on Different Platforms

ERIC Educational Resources Information Center

Mpofu, Bongeka

2016-01-01

This research was aimed at the investigation of mobile device and computer use at a higher learning institution. The goal was to determine the current use of computers and mobile devices for learning and the students' reading speed on different platforms. The research was contextualised in a sample of students at the University of South Africa.…

Reliability and validity of bilateral ankle accelerometer algorithms for activity recognition and walking speed after stroke.

PubMed

Dobkin, Bruce H; Xu, Xiaoyu; Batalin, Maxim; Thomas, Seth; Kaiser, William

2011-08-01

Outcome measures of mobility for large stroke trials are limited to timed walks for short distances in a laboratory, step counters and ordinal scales of disability and quality of life. Continuous monitoring and outcome measurements of the type and quantity of activity in the community would provide direct data about daily performance, including compliance with exercise and skills practice during routine care and clinical trials. Twelve adults with impaired ambulation from hemiparetic stroke and 6 healthy controls wore triaxial accelerometers on their ankles. Walking speed for repeated outdoor walks was determined by machine-learning algorithms and compared to a stopwatch calculation of speed for distances not known to the algorithm. The reliability of recognizing walking, exercise, and cycling by the algorithms was compared to activity logs. A high correlation was found between stopwatch-measured outdoor walking speed and algorithm-calculated speed (Pearson coefficient, 0.98; P=0.001) and for repeated measures of algorithm-derived walking speed (P=0.01). Bouts of walking >5 steps, variations in walking speed, cycling, stair climbing, and leg exercises were correctly identified during a day in the community. Compared to healthy subjects, those with stroke were, as expected, more sedentary and slower, and their gait revealed high paretic-to-unaffected leg swing ratios. Test-retest reliability and concurrent and construct validity are high for activity pattern-recognition Bayesian algorithms developed from inertial sensors. This ratio scale data can provide real-world monitoring and outcome measurements of lower extremity activities and walking speed for stroke and rehabilitation studies.

Transfer of piano practice in fast performance of skilled finger movements.

PubMed

Furuya, Shinichi; Nakamura, Ayumi; Nagata, Noriko

2013-11-01

Transfer of learning facilitates the efficient mastery of various skills without practicing all possible sensory-motor repertoires. The present study assessed whether motor practice at a submaximal speed, which is typical in sports and music performance, results in an increase in a maximum speed of finger movements of trained and untrained skills. Piano practice of sequential finger movements at a submaximal speed over days progressively increased the maximum speed of trained movements. This increased maximum speed of finger movements was maintained two months after the practice. The learning transferred within the hand to some extent, but not across the hands. The present study confirmed facilitation of fast finger movements following a piano practice at a submaximal speed. In addition, the findings indicated the intra-manual transfer effects of piano practice on the maximum speed of skilled finger movements.

Effects Of Frame Rates In Video Displays

NASA Technical Reports Server (NTRS)

Kellogg, Gary V.; Wagner, Charles A.

1991-01-01

Report describes experiment on subjective effects of rates at which display on cathode-ray tube in flight simulator updated and refreshed. Conducted to learn more about jumping, blurring, flickering, and multiple lines that observer perceives when line moves at high speed across screen of a calligraphic CRT.

Mathematics Learning Development: The Role of Long-Term Retrieval

ERIC Educational Resources Information Center

Calderón-Tena, Carlos O.; Caterino, Linda C.

2016-01-01

This study assessed the relation between long-term memory retrieval and mathematics calculation and mathematics problem solving achievement among elementary, middle, and high school students in nationally representative sample of US students, when controlling for fluid and crystallized intelligence, short-term memory, and processing speed. As…

Learning multiple variable-speed sequences in striatum via cortical tutoring.

PubMed

Murray, James M; Escola, G Sean

2017-05-08

Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.

ARC-2006-ACD06-0179-007

NASA Image and Video Library

2006-10-12

Ames holds a Media Day at the Hypervelocity Free Flight facility where Ames is conducting high-speed tests of small models of the agency's new Orion CEV to learn about stability during flight. The hypervelocity test facility uses a gun to shoot Orion models between 0.5 and l.5 inches (1.25 - 3.75 centimeters in diameter. The facility can conduct experiments with speeds up to 19,000 miles per hour (30,400 kilometers per hour) - ABC Camerman in forground, Wayne Freedman ABC reporter, Jeff Brown (Ames-ASA), John Bluck (AMES PAO)

ARC-2006-ACD06-0179-002

NASA Image and Video Library

2006-10-12

Ames holds a Media Day at the Hypervelocity Free Flight facility where Ames is conducting high-speed tests of small models of the agency's new Orion CEV to learn about stability during flight. The hypervelocity test facility uses a gun to shoot Orion models between 0.5 and l.5 inches (1.25 - 3.75 centimeters in diameter. The facility can conduct experiments with speeds up to 19,000 miles per hour (30,400 kilometers per hour) - NBC Channel 11 Technology/Business reporter Scott Budman at the gun range (w/C Acosta in bkgrd)

ARC-2006-ACD06-0179-001

NASA Image and Video Library

2006-10-12

Ames holds a Media Day at the Hypervelocity Free Flight facility where Ames is conducting high-speed tests of small models of the agency's new Orion CEV to learn about stability during flight. The hypervelocity test facility uses a gun to shoot Orion models between 0.5 and l.5 inches (1.25 - 3.75 centimeters in diameter. The facility can conduct experiments with speeds up to 19,000 miles per hour (30,400 kilometers per hour) - Cesar Acosta, NASA photographer in forground and a news camera men taking shot of the gun facility

Didactical Design Material Units of Distance and Speed to Developed Mathematical Connection in Elementary School

NASA Astrophysics Data System (ADS)

Jannah, R. R.; Apriliya, S.; Karlimah

2017-03-01

This study aims to develop alternative instructional design based of barriers learning which identified by developing mathematical connection capabilities to the material unit of distance and speed. The research was conducted in the fifth grade elementary school Instructional design is complemented with a hypothetical learning trajectory in the form of a pedagogical didactic anticipation. The method used is descriptive method with qualitative approach. Techniques data collection used were observation, interviews, and documentation. The instrument used the researchers themselves are equipped with an instrument written test. The data were analyzed qualitatively to determine the student learning obstacles, then arrange hypothetical learning trajectory and pedagogical didactic anticipation. Learning obstacle are identified, it is learning obstacle related the connections between mathematical topics, learning obstacle related with other disciplines, and learning obstacle related with everyday life. The results of this research are improvement and development of didactic design in mathematics which has activities mathematical connection to the material unit of distance and speed in elementary school. The learning activities are carried out is using varied methods include method lectures, demonstrations, practice and exercise, as well as using the modified instructional media.

The Time Course of Explicit and Implicit Categorization

PubMed Central

Zakrzewski, Alexandria C.; Herberger, Eric; Boomer, Joseph; Roeder, Jessica; Ashby, F. Gregory; Church, Barbara A.

2015-01-01

Contemporary theory in cognitive neuroscience distinguishes, among the processes and utilities that serve categorization, explicit and implicit systems of category learning that learn, respectively, category rules by active hypothesis testing or adaptive behaviors by association and reinforcement. Little is known about the time course of categorization within these systems. Accordingly, the present experiments contrasted tasks that fostered explicit categorization (because they had a one-dimensional, rule-based solution) or implicit categorization (because they had a two-dimensional, information-integration solution). In Experiment 1, participants learned categories under unspeeded or speeded conditions. In Experiment 2, they applied previously trained category knowledge under unspeeded or speeded conditions. Speeded conditions selectively impaired implicit category learning and implicit mature categorization. These results illuminate the processing dynamics of explicit/implicit categorization. PMID:26025556

WAMDII: The Wide Angle Michelson Doppler Imaging Interferometer

NASA Technical Reports Server (NTRS)

1992-01-01

As part of an effort to learn more about the upper atmosphere and how it is linked to the weather experienced each day, NASA and NRCC are jointly sponsoring the Wide Angle Michelson Doppler Imaging Interferometer (WAMDII) Mission. WAMDII will measure atmospheric temperature and wind speed in the upper atmosphere. In addition to providing data on the upper atmosphere, the wind speed and temperature readings WAMDII takes will also be highly useful in developing and updating computer simulated models of the upper atmosphere. These models are used in the design and testing of equipment and software for Shuttles, satellites, and reentry vehicles. In making its wind speed and temperature measurements, WAMDII examines the Earth's airglow, a faint photochemical luminescence caused by the influx of solar ultraviolet energy into the upper atmosphere. During periods of high solar flare activity, the amount of this UV energy entering the upper atmosphere increases, and this increase may effect airglow emissions.

Development, Analysis and Testing of the High Speed Research Flexible Semispan Model

NASA Technical Reports Server (NTRS)

Schuster, David M.; Spain, Charles V.; Turnock, David L.; Rausch, Russ D.; Hamouda, M-Nabil; Vogler, William A.; Stockwell, Alan E.

1999-01-01

This report presents the work performed by Lockheed Martin Engineering and Sciences (LMES) in support of the High Speed Research (HSR) Flexible Semispan Model (FSM) wind-tunnel test. The test was conducted in order to assess the aerodynamic and aeroelastic character of a flexible high speed civil transport wing. Data was acquired for the purpose of code validation and trend evaluation for this type of wing. The report describes a number of activities in preparing for and conducting the wind-tunnel test. These included coordination of the design and fabrication, development of analytical models, analysis/hardware correlation, performance of laboratory tests, monitoring of model safety issues, and wind-tunnel data acquisition and reduction. Descriptions and relevant evaluations associated with the pretest data are given in sections 1 through 6, followed by pre- and post-test flutter analysis in section 7, and the results of the aerodynamics/loads test in section 8. Finally, section 9 provides some recommendations based on lessons learned throughout the FSM program.

Overview of High Speed Close-Up Imaging in an Icing Environment

NASA Technical Reports Server (NTRS)

Miller, Dean R.; Lynch, Christopher J.; Tate, Peter A.

2004-01-01

The Icing Branch and Imaging Technology Center at NASA Glenn Research Center have recently been involved in several projects where high speed close-up imaging was used to investigate water droplet impact/splash, and also ice particle impact/bounce in an icing wind tunnel. The combination of close-up and high speed imaging capabilities were required because the particles being studied were relatively small (d < 1 mm in diameter), and the impact process occurred in a very short time period (t(sub impact) << 1 sec). High speed close-up imaging was utilized to study the dynamics of droplet impact and splash in simulated Supercooled Large Droplet (SLD) icing conditions. The objective of this test was to evaluate the capability of a ultra high speed camera system to acquire quantitative information about the impact process (e.g., droplet size, velocity). Imaging data were obtained in an icing wind tunnel for spray cloud MVD > 50 m. High speed close-up imaging was also utilized to characterize the impact of ice particles on an airfoil with a thermally protected leading edge. The objective of this investigation was to determine whether ice particles tend to "stick" or "bounce" after impact. Imaging data were obtained for cases where the airfoil surface was heated and unheated. Based on the results from this test, follow on tests were conducted to investigate ice particle impact on the sensing elements of water content measurement devices. This paper will describe the use of the imaging systems to support these experimental investigations, present some representative results, and summarize what was learned about the use of these systems in an icing environment.

Statistical Mechanical Analysis of Online Learning with Weight Normalization in Single Layer Perceptron

NASA Astrophysics Data System (ADS)

Yoshida, Yuki; Karakida, Ryo; Okada, Masato; Amari, Shun-ichi

2017-04-01

Weight normalization, a newly proposed optimization method for neural networks by Salimans and Kingma (2016), decomposes the weight vector of a neural network into a radial length and a direction vector, and the decomposed parameters follow their steepest descent update. They reported that learning with the weight normalization achieves better converging speed in several tasks including image recognition and reinforcement learning than learning with the conventional parameterization. However, it remains theoretically uncovered how the weight normalization improves the converging speed. In this study, we applied a statistical mechanical technique to analyze on-line learning in single layer linear and nonlinear perceptrons with weight normalization. By deriving order parameters of the learning dynamics, we confirmed quantitatively that weight normalization realizes fast converging speed by automatically tuning the effective learning rate, regardless of the nonlinearity of the neural network. This property is realized when the initial value of the radial length is near the global minimum; therefore, our theory suggests that it is important to choose the initial value of the radial length appropriately when using weight normalization.

Transfer of piano practice in fast performance of skilled finger movements

PubMed Central

2013-01-01

Background Transfer of learning facilitates the efficient mastery of various skills without practicing all possible sensory-motor repertoires. The present study assessed whether motor practice at a submaximal speed, which is typical in sports and music performance, results in an increase in a maximum speed of finger movements of trained and untrained skills. Results Piano practice of sequential finger movements at a submaximal speed over days progressively increased the maximum speed of trained movements. This increased maximum speed of finger movements was maintained two months after the practice. The learning transferred within the hand to some extent, but not across the hands. Conclusions The present study confirmed facilitation of fast finger movements following a piano practice at a submaximal speed. In addition, the findings indicated the intra-manual transfer effects of piano practice on the maximum speed of skilled finger movements. PMID:24175946

Review of Fluorescence-Based Velocimetry Techniques to Study High-Speed Compressible Flows

NASA Technical Reports Server (NTRS)

Bathel, Brett F.; Johansen, Criag; Inman, Jennifer A.; Jones, Stephen B.; Danehy, Paul M.

2013-01-01

This paper reviews five laser-induced fluorescence-based velocimetry techniques that have been used to study high-speed compressible flows at NASA Langley Research Center. The techniques discussed in this paper include nitric oxide (NO) molecular tagging velocimetry (MTV), nitrogen dioxide photodissociation (NO2-to-NO) MTV, and NO and atomic oxygen (O-atom) Doppler-shift-based velocimetry. Measurements of both single-component and two-component velocity have been performed using these techniques. This paper details the specific application and experiment for which each technique has been used, the facility in which the experiment was performed, the experimental setup, sample results, and a discussion of the lessons learned from each experiment.

High Speed Research Program Structural Acoustics Multi-Year Summary Report

NASA Technical Reports Server (NTRS)

Beier, Theodor H.; Bhat, Waman V.; Rizzi, Stephen A.; Silcox, Richard J.; Simpson, Myles A.

2005-01-01

This report summarizes the work conducted by the Structural Acoustics Integrated Technology Development (ITD) Team under NASA's High Speed Research (HSR) Phase II program from 1993 to 1999. It is intended to serve as a reference for future researchers by documenting the results of the interior noise and sonic fatigue technology development activities conducted during this period. For interior noise, these activities included excitation modeling, structural acoustic response modeling, development of passive treatments and active controls, and prediction of interior noise. For sonic fatigue, these activities included loads prediction, materials characterization, sonic fatigue code development, development of response reduction techniques, and generation of sonic fatigue design requirements. Also included are lessons learned and recommendations for future work.

Maximization of Learning Speed in the Motor Cortex Due to Neuronal Redundancy

PubMed Central

Takiyama, Ken; Okada, Masato

2012-01-01

Many redundancies play functional roles in motor control and motor learning. For example, kinematic and muscle redundancies contribute to stabilizing posture and impedance control, respectively. Another redundancy is the number of neurons themselves; there are overwhelmingly more neurons than muscles, and many combinations of neural activation can generate identical muscle activity. The functional roles of this neuronal redundancy remains unknown. Analysis of a redundant neural network model makes it possible to investigate these functional roles while varying the number of model neurons and holding constant the number of output units. Our analysis reveals that learning speed reaches its maximum value if and only if the model includes sufficient neuronal redundancy. This analytical result does not depend on whether the distribution of the preferred direction is uniform or a skewed bimodal, both of which have been reported in neurophysiological studies. Neuronal redundancy maximizes learning speed, even if the neural network model includes recurrent connections, a nonlinear activation function, or nonlinear muscle units. Furthermore, our results do not rely on the shape of the generalization function. The results of this study suggest that one of the functional roles of neuronal redundancy is to maximize learning speed. PMID:22253586

Rapid motor learning in the translational vestibulo-ocular reflex

NASA Technical Reports Server (NTRS)

Zhou, Wu; Weldon, Patrick; Tang, Bingfeng; King, W. M.; Shelhamer, M. J. (Principal Investigator)

2003-01-01

Motor learning was induced in the translational vestibulo-ocular reflex (TVOR) when monkeys were repeatedly subjected to a brief (0.5 sec) head translation while they tried to maintain binocular fixation on a visual target for juice rewards. If the target was world-fixed, the initial eye speed of the TVOR gradually increased; if the target was head-fixed, the initial eye speed of the TVOR gradually decreased. The rate of learning acquisition was very rapid, with a time constant of approximately 100 trials, which was equivalent to <1 min of accumulated stimulation. These learned changes were consolidated over >or=1 d without any reinforcement, indicating induction of long-term synaptic plasticity. Although the learning generalized to targets with different viewing distances and to head translations with different accelerations, it was highly specific for the particular combination of head motion and evoked eye movement associated with the training. For example, it was specific to the modality of the stimulus (translation vs rotation) and the direction of the evoked eye movement in the training. Furthermore, when one eye was aligned with the heading direction so that it remained motionless during training, learning was not expressed in this eye, but only in the other nonaligned eye. These specificities show that the learning sites are neither in the sensory nor the motor limb of the reflex but in the sensory-motor transformation stage of the reflex. The dependence of the learning on both head motion and evoked eye movement suggests that Hebbian learning may be one of the underlying cellular mechanisms.

Stimulus fear relevance and the speed, magnitude, and robustness of vicariously learned fear.

PubMed

Dunne, Güler; Reynolds, Gemma; Askew, Chris

2017-08-01

Superior learning for fear-relevant stimuli is typically indicated in the laboratory by faster acquisition of fear responses, greater learned fear, and enhanced resistance to extinction. Three experiments investigated the speed, magnitude, and robustness of UK children's (6-10 years; N = 290; 122 boys, 168 girls) vicariously learned fear responses for three types of stimuli. In two experiments, children were presented with pictures of novel animals (Australian marsupials) and flowers (fear-irrelevant stimuli) alone (control) or together with faces expressing fear or happiness. To determine learning speed the number of stimulus-face pairings seen by children was varied (1, 10, or 30 trials). Robustness of learning was examined via repeated extinction procedures over 3 weeks. A third experiment compared the magnitude and robustness of vicarious fear learning for snakes and marsupials. Significant increases in fear responses were found for snakes, marsupials and flowers. There was no indication that vicarious learning for marsupials was faster than for flowers. Moreover, vicariously learned fear was neither greater nor more robust for snakes compared to marsupials, or for marsupials compared to flowers. These findings suggest that for this age group stimulus fear relevance may have little influence on vicarious fear learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

Video on phone lines: technology and applications

NASA Astrophysics Data System (ADS)

Hsing, T. Russell

1996-03-01

Recent advances in communications signal processing and VLSI technology are fostering tremendous interest in transmitting high-speed digital data over ordinary telephone lines at bit rates substantially above the ISDN Basic Access rate (144 Kbit/s). Two new technologies, high-bit-rate digital subscriber lines and asymmetric digital subscriber lines promise transmission over most of the embedded loop plant at 1.544 Mbit/s and beyond. Stimulated by these research promises and rapid advances on video coding techniques and the standards activity, information networks around the globe are now exploring possible business opportunities of offering quality video services (such as distant learning, telemedicine, and telecommuting etc.) through this high-speed digital transport capability in the copper loop plant. Visual communications for residential customers have become more feasible than ever both technically and economically.

Democratic Population Decisions Result in Robust Policy-Gradient Learning: A Parametric Study with GPU Simulations

PubMed Central

Richmond, Paul; Buesing, Lars; Giugliano, Michele; Vasilaki, Eleni

2011-01-01

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a “non-democratic” mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons “vote” independently (“democratic”) for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated. PMID:21572529

Performance of a visuomotor walking task in an augmented reality training setting.

PubMed

Haarman, Juliet A M; Choi, Julia T; Buurke, Jaap H; Rietman, Johan S; Reenalda, Jasper

2017-12-01

Visual cues can be used to train walking patterns. Here, we studied the performance and learning capacities of healthy subjects executing a high-precision visuomotor walking task, in an augmented reality training set-up. A beamer was used to project visual stepping targets on the walking surface of an instrumented treadmill. Two speeds were used to manipulate task difficulty. All participants (n = 20) had to change their step length to hit visual stepping targets with a specific part of their foot, while walking on a treadmill over seven consecutive training blocks, each block composed of 100 stepping targets. Distance between stepping targets was varied between short, medium and long steps. Training blocks could either be composed of random stepping targets (no fixed sequence was present in the distance between the stepping targets) or sequenced stepping targets (repeating fixed sequence was present). Random training blocks were used to measure non-specific learning and sequenced training blocks were used to measure sequence-specific learning. Primary outcome measures were performance (% of correct hits), and learning effects (increase in performance over the training blocks: both sequence-specific and non-specific). Secondary outcome measures were the performance and stepping-error in relation to the step length (distance between stepping target). Subjects were able to score 76% and 54% at first try for lower speed (2.3 km/h) and higher speed (3.3 km/h) trials, respectively. Performance scores did not increase over the course of the trials, nor did the subjects show the ability to learn a sequenced walking task. Subjects were better able to hit targets while increasing their step length, compared to shortening it. In conclusion, augmented reality training by use of the current set-up was intuitive for the user. Suboptimal feedback presentation might have limited the learning effects of the subjects. Copyright © 2017 Elsevier B.V. All rights reserved.

Individual differences and predictors of forgetting in old age: the role of processing speed and working memory.

PubMed

Zimprich, Daniel; Kurtz, Tanja

2013-01-01

The goal of the present study was to examine whether individual differences in basic cognitive abilities, processing speed, and working memory, are reliable predictors of individual differences in forgetting rates in old age. The sample for the present study comprised 364 participants aged between 65 and 80 years from the Zurich Longitudinal Study on Cognitive Aging. The impact of basic cognitive abilities on forgetting was analyzed by modeling working memory and processing speed as predictors of the amount of forgetting of 27 words, which had been learned across five trials. Forgetting was measured over a 30-minute interval by using parceling and a latent change model, in which the latent difference between recall performance after five learning trials and a delayed recall was modeled. Results implied reliable individual differences in forgetting. These individual differences in forgetting were strongly related to processing speed and working memory. Moreover, an age-related effect, which was significantly stronger for forgetting than for learning, emerged even after controlling effects of processing speed and working memory.

Family Science and Community-Based Learning: Using Speed Networking

ERIC Educational Resources Information Center

Payne, Pamela B.; Hubler, Daniel S.

2017-01-01

Students in Family Science often feel that they have an uphill battle to finding career opportunities that maximize their experiences from degree programs. The hallmark of successful programs in Family Science needs to be the development and maintenance of high-quality field experiences for students that align with national standards and…

Laminar-flow flight experiments

NASA Technical Reports Server (NTRS)

Wagner, Richard D.; Maddalon, Dal V.; Bartlett, D. W.; Collier, F. S., Jr.; Braslow, A. L.

1989-01-01

The flight testing conducted over the past 10 years in the NASA laminar-flow control (LFC) will be reviewed. The LFC program was directed towards the most challenging technology application, the high supersonic speed transport. To place these recent experiences in perspective, earlier important flight tests will first be reviewed to recall the lessons learned at that time.

Cognitive Risk Factors for Specific Learning Disorder: Processing Speed, Temporal Processing, and Working Memory

ERIC Educational Resources Information Center

Moll, Kristina; Göbel, Silke M.; Gooch, Debbie; Landerl, Karin; Snowling, Margaret J.

2016-01-01

High comorbidity rates between reading disorder (RD) and mathematics disorder (MD) indicate that, although the cognitive core deficits underlying these disorders are distinct, additional domain-general risk factors might be shared between the disorders. Three domain-general cognitive abilities were investigated in children with RD and MD:…

Microcomputer Keyboarding Curriculum for Middle and Junior High School Students. Final Report.

ERIC Educational Resources Information Center

Regional School District No. 10, Burlington, CT.

This project report describes the development of a seventh-grade curriculum to promote microcomputer keyboarding skills, i.e., learning correct alpha-numeric reaches, developing proficiency in making appropriate reaches, using correct fingering without looking at the keyboard, and attaining a degree of speed and accuracy. Although the curriculum…

The Relationship of Learning Traits, Motivation and Performance-Learning Response Dynamics

ERIC Educational Resources Information Center

Hwang, Wu-Yuin; Chang, Chen-Bin; Chen, Gan-Jung

2004-01-01

This paper proposes a model of learning dynamics and learning energy, one that analyzes learning systems scientifically. This model makes response to the learner action by means of some equations relating to learning dynamics, learning energy, learning speed, learning force, and learning acceleration, which is analogous to the notion of Newtonian…

Energy management using virtual reality improves 2000-m rowing performance.

PubMed

Hoffmann, Charles P; Filippeschi, Alessandro; Ruffaldi, Emanuele; Bardy, Benoit G

2014-01-01

Elite-standard rowers tend to use a fast-start strategy followed by an inverted parabolic-shaped speed profile in 2000-m races. This strategy is probably the best to manage energy resources during the race and maximise performance. This study investigated the use of virtual reality (VR) with novice rowers as a means to learn about energy management. Participants from an avatar group (n = 7) were instructed to track a virtual boat on a screen, whose speed was set individually to follow the appropriate to-be-learned speed profile. A control group (n = 8) followed an indoor training programme. In spite of similar physiological characteristics in the groups, the avatar group learned and maintained the required profile, resulting in an improved performance (i.e. a decrease in race duration), whereas the control group did not. These results suggest that VR is a means to learn an energy-related skill and improve performance.

A learning-based autonomous driver: emulate human driver's intelligence in low-speed car following

NASA Astrophysics Data System (ADS)

Wei, Junqing; Dolan, John M.; Litkouhi, Bakhtiar

2010-04-01

In this paper, an offline learning mechanism based on the genetic algorithm is proposed for autonomous vehicles to emulate human driver behaviors. The autonomous driving ability is implemented based on a Prediction- and Cost function-Based algorithm (PCB). PCB is designed to emulate a human driver's decision process, which is modeled as traffic scenario prediction and evaluation. This paper focuses on using a learning algorithm to optimize PCB with very limited training data, so that PCB can have the ability to predict and evaluate traffic scenarios similarly to human drivers. 80 seconds of human driving data was collected in low-speed (< 30miles/h) car-following scenarios. In the low-speed car-following tests, PCB was able to perform more human-like carfollowing after learning. A more general 120 kilometer-long simulation showed that PCB performs robustly even in scenarios that are not part of the training set.

Connecting Entrepreneurial and Action Learning in Student-Initiated New Business Ventures: The Case of SPEED

ERIC Educational Resources Information Center

Rae, David

2009-01-01

The Student Placements for Entrepreneurs in Education (SPEED) project ran in 12 higher education institutes in the UK between 2006 and 2008, providing an innovative, action learning-based route that enabled students to start new business ventures as self-started work experience, and has influenced successor programmes. The paper addresses three…

Mechanical-plowing-based high-speed patterning on hard material via advanced-control and ultrasonic probe vibration.

PubMed

Wang, Zhihua; Tan, Jun; Zou, Qingze; Jiang, Wei

2013-11-01

In this paper, we present a high-speed direct pattern fabrication on hard materials (e.g., a tungsten-coated quartz substrate) via mechanical plowing. Compared to other probe-based nanolithography techniques based on chemical- and/or physical-reactions (e.g., the Dip-pen technique), mechanical plowing is meritorious for its low cost, ease of process control, and capability of working with a wide variety of materials beyond conductive and/or soft materials. However, direct patterning on hard material faces two daunting challenges. First, the patterning throughput is ultimately hindered by the "writing" (plowing) speed, which, in turn, is limited by the adverse effects that can be excited/induced during high-speed, and/or large-range plowing, including the vibrational dynamics of the actuation system (the piezoelectric actuator, the cantilever, and the mechanical fixture connecting the cantilever to the actuator), the dynamic cross-axis coupling between different axes of motion, and the hysteresis and the drift effects related to the piezoelectric actuators. Secondly, it is very challenging to directly pattern on ultra-hard materials via plowing. Even with a diamond probe, the line depth of the pattern via continuous plowing on ultra-hard materials such as tungsten, is still rather small (<0.5 nm), particularly when the "writing" speed becomes high. To overcome these two challenges, we propose to utilize a novel iterative learning control technique to achieve precision tracking of the desired pattern during high-speed, large-range plowing, and introduce ultrasonic vibration of the probe in the normal (vertical) direction during the plowing process to enable direct patterning on ultra hard materials. The proposed approach was implemented to directly fabricate patterns on a mask with tungsten coating and quartz substrate. The experimental results demonstrated that a large-size pattern of four grooves (20 μm in length with 300 nm spacing between lines) can be fabricated at a high speed of ~5 mm/s, with the line width and the line depth at ~95 nm and 2 nm, respectively. A fine pattern of the word "NANO" is also fabricated at the speed of ~5 mm/s.

Initial Skill Acquisition of Handrim Wheelchair Propulsion: A New Perspective.

PubMed

Vegter, Riemer J K; de Groot, Sonja; Lamoth, Claudine J; Veeger, Dirkjan Hej; van der Woude, Lucas H V

2014-01-01

To gain insight into cyclic motor learning processes, hand rim wheelchair propulsion is a suitable cyclic task, to be learned during early rehabilitation and novel to almost every individual. To propel in an energy efficient manner, wheelchair users must learn to control bimanually applied forces onto the rims, preserving both speed and direction of locomotion. The purpose of this study was to evaluate mechanical efficiency and propulsion technique during the initial stage of motor learning. Therefore, 70 naive able-bodied men received 12-min uninstructed wheelchair practice, consisting of three 4-min blocks separated by 2 min rest. Practice was performed on a motor-driven treadmill at a fixed belt speed and constant power output relative to body mass. Energy consumption and the kinetics of propulsion technique were continuously measured. Participants significantly increased their mechanical efficiency and changed their propulsion technique from a high frequency mode with a lot of negative work to a longer-slower movement pattern with less power losses. Furthermore a multi-level model showed propulsion technique to relate to mechanical efficiency. Finally improvers and non-improvers were identified. The non-improving group was already more efficient and had a better propulsion technique in the first block of practice (i.e., the fourth minute). These findings link propulsion technique to mechanical efficiency, support the importance of a correct propulsion technique for wheelchair users and show motor learning differences.

Does Extensive Reading Promote Reading Speed?

ERIC Educational Resources Information Center

He, Mu

2014-01-01

Research has shown a wide range of learning benefits accruing from extensive reading. Not only is there improvement in reading, but also in a wide range of language uses and areas of language knowledge. However, few research studies have examined reading speed. The existing literature on reading speed focused on students' reading speed without…

Using deep learning for content-based medical image retrieval

NASA Astrophysics Data System (ADS)

Sun, Qinpei; Yang, Yuanyuan; Sun, Jianyong; Yang, Zhiming; Zhang, Jianguo

2017-03-01

Content-Based medical image retrieval (CBMIR) is been highly active research area from past few years. The retrieval performance of a CBMIR system crucially depends on the feature representation, which have been extensively studied by researchers for decades. Although a variety of techniques have been proposed, it remains one of the most challenging problems in current CBMIR research, which is mainly due to the well-known "semantic gap" issue that exists between low-level image pixels captured by machines and high-level semantic concepts perceived by human[1]. Recent years have witnessed some important advances of new techniques in machine learning. One important breakthrough technique is known as "deep learning". Unlike conventional machine learning methods that are often using "shallow" architectures, deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple stages of transformation and representation. This means that we do not need to spend enormous energy to extract features manually. In this presentation, we propose a novel framework which uses deep learning to retrieval the medical image to improve the accuracy and speed of a CBIR in integrated RIS/PACS.

Sparse Representation with Spatio-Temporal Online Dictionary Learning for Efficient Video Coding.

PubMed

Dai, Wenrui; Shen, Yangmei; Tang, Xin; Zou, Junni; Xiong, Hongkai; Chen, Chang Wen

2016-07-27

Classical dictionary learning methods for video coding suer from high computational complexity and interfered coding eciency by disregarding its underlying distribution. This paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to speed up the convergence rate of dictionary learning with a guarantee of approximation error. The proposed algorithm incorporates stochastic gradient descents to form a dictionary of pairs of 3-D low-frequency and highfrequency spatio-temporal volumes. In each iteration of the learning process, it randomly selects one sample volume and updates the atoms of dictionary by minimizing the expected cost, rather than optimizes empirical cost over the complete training data like batch learning methods, e.g. K-SVD. Since the selected volumes are supposed to be i.i.d. samples from the underlying distribution, decomposition coecients attained from the trained dictionary are desirable for sparse representation. Theoretically, it is proved that the proposed STOL could achieve better approximation for sparse representation than K-SVD and maintain both structured sparsity and hierarchical sparsity. It is shown to outperform batch gradient descent methods (K-SVD) in the sense of convergence speed and computational complexity, and its upper bound for prediction error is asymptotically equal to the training error. With lower computational complexity, extensive experiments validate that the STOL based coding scheme achieves performance improvements than H.264/AVC or HEVC as well as existing super-resolution based methods in ratedistortion performance and visual quality.

The active learning hypothesis of the job-demand-control model: an experimental examination.

PubMed

Häusser, Jan Alexander; Schulz-Hardt, Stefan; Mojzisch, Andreas

2014-01-01

The active learning hypothesis of the job-demand-control model [Karasek, R. A. 1979. "Job Demands, Job Decision Latitude, and Mental Strain: Implications for Job Redesign." Administration Science Quarterly 24: 285-307] proposes positive effects of high job demands and high job control on performance. We conducted a 2 (demands: high vs. low) × 2 (control: high vs. low) experimental office workplace simulation to examine this hypothesis. Since performance during a work simulation is confounded by the boundaries of the demands and control manipulations (e.g. time limits), we used a post-test, in which participants continued working at their task, but without any manipulation of demands and control. This post-test allowed for examining active learning (transfer) effects in an unconfounded fashion. Our results revealed that high demands had a positive effect on quantitative performance, without affecting task accuracy. In contrast, high control resulted in a speed-accuracy tradeoff, that is participants in the high control conditions worked slower but with greater accuracy than participants in the low control conditions.

Efficient Learning for the Poor: New Insights into Literacy Acquisition for Children

ERIC Educational Resources Information Center

Abadzi, Helen

2008-01-01

Reading depends on the speed of visual recognition and capacity of short-term memory. To understand a sentence, the mind must read it fast enough to capture it within the limits of the short-term memory. This means that children must attain a minimum speed of fairly accurate reading to understand a passage. Learning to read involves "tricking" the…

First saccadic eye movement reveals persistent attentional guidance by implicit learning

PubMed Central

Jiang, Yuhong V.; Won, Bo-Yeong; Swallow, Khena M.

2014-01-01

Implicit learning about where a visual search target is likely to appear often speeds up search. However, whether implicit learning guides spatial attention or affects post-search decisional processes remains controversial. Using eye tracking, this study provides compelling evidence that implicit learning guides attention. In a training phase, participants often found the target in a high-frequency, “rich” quadrant of the display. When subsequently tested in a phase during which the target was randomly located, participants were twice as likely to direct the first saccadic eye movement to the previously rich quadrant than to any of the sparse quadrants. The attentional bias persisted for nearly 200 trials after training and was unabated by explicit instructions to distribute attention evenly. We propose that implicit learning guides spatial attention but in a qualitatively different manner than goal-driven attention. PMID:24512610

An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction

PubMed Central

Li, Yiyang; Jin, Weiqi; Zhu, Jin; Zhang, Xu; Li, Shuo

2018-01-01

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods. PMID:29342857

An Adaptive Deghosting Method in Neural Network-Based Infrared Detectors Nonuniformity Correction.

PubMed

Li, Yiyang; Jin, Weiqi; Zhu, Jin; Zhang, Xu; Li, Shuo

2018-01-13

The problems of the neural network-based nonuniformity correction algorithm for infrared focal plane arrays mainly concern slow convergence speed and ghosting artifacts. In general, the more stringent the inhibition of ghosting, the slower the convergence speed. The factors that affect these two problems are the estimated desired image and the learning rate. In this paper, we propose a learning rate rule that combines adaptive threshold edge detection and a temporal gate. Through the noise estimation algorithm, the adaptive spatial threshold is related to the residual nonuniformity noise in the corrected image. The proposed learning rate is used to effectively and stably suppress ghosting artifacts without slowing down the convergence speed. The performance of the proposed technique was thoroughly studied with infrared image sequences with both simulated nonuniformity and real nonuniformity. The results show that the deghosting performance of the proposed method is superior to that of other neural network-based nonuniformity correction algorithms and that the convergence speed is equivalent to the tested deghosting methods.

The Broadband Imperative: Recommendations to Address K-12 Education Infrastructure Needs

ERIC Educational Resources Information Center

Fox, Christine; Waters, John; Fletcher, Geoff; Levin, Douglas

2012-01-01

It is a simple fact that access to high-speed broadband is now as vital a component of K-12 school infrastructure as electricity, air conditioning, and heating. The same tools and resources that have transformed educators' personal, civic, and professional lives must be part of learning experiences intended to prepare today's students for college…

The Hands-On and Far-Out Physics Team: It Starts Out Walking.

ERIC Educational Resources Information Center

Albrecht, Bob; Firedrake, George

1998-01-01

The Hands-On and Far-Out Physics project is part of the Center for Technology, Environment, and Communication (C-TEC), a project-based learning community at Piner High School in Santa Rosa (California). This article introduces the project team, discusses member activities, presents a walking-speed experiment, and describes a Mars Colony course…

Fitness-Specific Epistemic Beliefs, Effort Regulation, Outcomes, and Indices of Motivation in High School Physical Education

ERIC Educational Resources Information Center

Lodewyk, Ken R.; Gao, Zan

2013-01-01

Epistemic beliefs are deeply held convictions about the nature of knowledge, knowing, and learning. In this study, approximately 500 ninth and tenth-grade physical education (PE) students completed fitness-specific measures assessing their epistemic beliefs in the simplicity and stability of knowledge and the speed of its acquisition along with…

A Comparison of Speed Achievement of Students in Grades 3-6 Who Learn Keyboarding on the Microcomputer.

ERIC Educational Resources Information Center

Sormunen, Carolee

1988-01-01

A study concluded that there were no significant differences in posttest speed achievement of students in grades 3 through 6 when pretest typewriting speed score was used as a covariate. Fifteen or fewer hours of instruction allows development of typewriting speed at the lowest level of skill acquisition. (JOW)

Intelligent Hybrid Vehicle Power Control - Part 1: Machine Learning of Optimal Vehicle Power

DTIC Science & Technology

2012-06-30

time window ),[ tWt DT : vave, vmax, vmin, ac, vst and vend, where the first four parameters are, respectively, the average speed, maximum speed...minimum speed and average acceleration, during the time period ),[ tWt DT , vst is the vehicle speed at )( DTWt  , and vend is the vehicle

Assessment of noise levels of the equipments used in the dental teaching institution, Bangalore.

PubMed

Kadanakuppe, Sushi; Bhat, Padma K; Jyothi, C; Ramegowda, C

2011-01-01

In dental practical classes, the acoustic environment is characterized by high noise levels in relation to other teaching areas, due to the exaggerated noise produced by some of these devices and use of dental equipment by many users at the same time. To measure, analyze and compare noise levels of equipments among dental learning areas under different working conditions and also to measure and compare noise levels between used and brand new handpieces under different working conditions. Noise levels were measured and analyzed in different dental learning areas that included clinical, pre-clinical areas and laboratories selected as representatives of a variety of learning-teaching activities. The noise levels were determined using a precision noise level meter (CENTER® 325 IEC 651 TYPE II) with a microphone. The mean of the maxima was determined. The data were collected, tabulated, and statistically analyzed using t tests. The noise levels measured varied between 64 and 97 dB(A).The differences in sound levels when the equipment was merely turned on and during cutting operations and also between used and brand new equipments were recorded. The laboratory engines had the highest noise levels, whereas the noise levels in high-speed turbine handpieces and the low-speed contra angle handpieces were decreased. The noise levels detected in this study are considered to be close to the limit of risk of hearing loss.

Automatic multi-organ segmentation using learning-based segmentation and level set optimization.

PubMed

Kohlberger, Timo; Sofka, Michal; Zhang, Jingdan; Birkbeck, Neil; Wetzl, Jens; Kaftan, Jens; Declerck, Jérôme; Zhou, S Kevin

2011-01-01

We present a novel generic segmentation system for the fully automatic multi-organ segmentation from CT medical images. Thereby we combine the advantages of learning-based approaches on point cloud-based shape representation, such a speed, robustness, point correspondences, with those of PDE-optimization-based level set approaches, such as high accuracy and the straightforward prevention of segment overlaps. In a benchmark on 10-100 annotated datasets for the liver, the lungs, and the kidneys we show that the proposed system yields segmentation accuracies of 1.17-2.89 mm average surface errors. Thereby the level set segmentation (which is initialized by the learning-based segmentations) contributes with an 20%-40% increase in accuracy.

Level of recall, retrieval speed, and variability on the Cued-Recall Retrieval Speed Task (CRRST) in individuals with amnestic mild cognitive impairment.

PubMed

Ramratan, Wendy S; Rabin, Laura A; Wang, Cuiling; Zimmerman, Molly E; Katz, Mindy J; Lipton, Richard B; Buschke, Herman

2012-03-01

Individuals with amnestic mild cognitive impairment (aMCI) show deficits on traditional episodic memory tasks and reductions in speed of performance on reaction time tasks. We present results on a novel task, the Cued-Recall Retrieval Speed Task (CRRST), designed to simultaneously measure level and speed of retrieval. A total of 390 older adults (mean age, 80.2 years), learned 16 words based on corresponding categorical cues. In the retrieval phase, we measured accuracy (% correct) and retrieval speed/reaction time (RT; time from cue presentation to voice onset of a correct response) across 6 trials. Compared to healthy elderly adults (HEA, n = 303), those with aMCI (n = 87) exhibited poorer performance in retrieval speed (difference = -0.13; p < .0001) and accuracy on the first trial (difference = -0.19; p < .0001), and their rate of improvement in retrieval speed was slower over subsequent trials. Those with aMCI also had greater within-person variability in processing speed (variance ratio = 1.22; p = .0098) and greater between-person variability in accuracy (variance ratio = 2.08; p = .0001) relative to HEA. Results are discussed in relation to the possibility that computer-based measures of cued-learning and processing speed variability may facilitate early detection of dementia in at-risk older adults.

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

PubMed

Li, Can; Belkin, Daniel; Li, Yunning; Yan, Peng; Hu, Miao; Ge, Ning; Jiang, Hao; Montgomery, Eric; Lin, Peng; Wang, Zhongrui; Song, Wenhao; Strachan, John Paul; Barnell, Mark; Wu, Qing; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei

2018-06-19

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.

Implicit social learning in relation to autistic-like traits.

PubMed

Hudson, Matthew; Nijboer, Tanja C W; Jellema, Tjeerd

2012-12-01

We investigated if variation in autistic traits in the typically-developed population (using the Autism-spectrum Quotient, AQ) influenced implicit learning of social information. In the learning phase, participants repeatedly observed two identities whose gaze and expression conveyed either a pro- or antisocial disposition. These identities were then employed in a gaze-cueing paradigm. Participants made speeded responses to a peripheral target that was spatially pre-cued by a non-predictive gaze direction. The low AQ group (n = 50) showed a smaller gaze-cueing effect for the antisocial than for the prosocial identity. The high AQ group (n = 48) showed equivalent gaze-cueing for both identities. Others' intentions/dispositions can be learned implicitly and affect subsequent responses to their behavior. This ability is impaired with increasing levels of autistic traits.

Best of Both Worlds

ERIC Educational Resources Information Center

Ramaswami, Rama

2009-01-01

Educators know that people learn best by doing. When students are doing something rather than reading or learning about it, they learn better. Immersive environments help students retain more information and speed up their learning. There's an enhancement in the way they learn. Immersive environments--which utilize technologies like simulations,…

Gaussian Processes for Data-Efficient Learning in Robotics and Control.

PubMed

Deisenroth, Marc Peter; Fox, Dieter; Rasmussen, Carl Edward

2015-02-01

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

The Role of Personality in a Regular Cognitive Monitoring Program.

PubMed

Sadeq, Nasreen A; Valdes, Elise G; Harrison Bush, Aryn L; Andel, Ross

2018-02-20

This study examines the role of personality in cognitive performance, adherence, and satisfaction with regular cognitive self-monitoring. One hundred fifty-seven cognitively healthy older adults, age 55+, completed the 44-item Big-Five Inventory and were subsequently engaged in online monthly cognitive monitoring using the Cogstate Brief Battery for up to 35 months (M=14 mo, SD=7 mo). The test measures speed and accuracy in reaction time, visual learning, and working memory tasks. Neuroticism, although not related to cognitive performance overall (P>0.05), was related to a greater increase in accuracy (estimate=0.07, P=0.04) and speed (estimate=-0.09, P=0.03) on One Card Learning. Greater conscientiousness was related to faster overall speed on Detection (estimate=-1.62, P=0.02) and a significant rate of improvement in speed on One Card Learning (estimate=-0.10, P<0.03). No differences in satisfaction or adherence to monthly monitoring as a function of neuroticism or conscientiousness were observed. Participants volunteering for regular cognitive monitoring may be quite uniform in terms of personality traits, with personality traits playing a relatively minor role in adherence and satisfaction. The more neurotic may exhibit better accuracy and improve in speed with time, whereas the more conscientious may perform faster overall and improve in speed on some tasks, but the effects appear small.

Learning-related human brain activations reflecting individual finances.

PubMed

Tobler, Philippe N; Fletcher, Paul C; Bullmore, Edward T; Schultz, Wolfram

2007-04-05

A basic tenet of microeconomics suggests that the subjective value of financial gains decreases with increasing assets of individuals ("marginal utility"). Using concepts from learning theory and microeconomics, we assessed the capacity of financial rewards to elicit behavioral and neuronal changes during reward-predictive learning in participants with different financial backgrounds. Behavioral learning speed during both acquisition and extinction correlated negatively with the assets of the participants, irrespective of education and age. Correspondingly, response changes in midbrain and striatum measured with functional magnetic resonance imaging were slower during both acquisition and extinction with increasing assets and income of the participants. By contrast, asymptotic magnitudes of behavioral and neuronal responses after learning were unrelated to personal finances. The inverse relationship of behavioral and neuronal learning speed with personal finances is compatible with the general concept of decreasing marginal utility with increasing wealth.

Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

PubMed

Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

2015-12-01

We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

Improving Word Learning in Children Using an Errorless Technique

ERIC Educational Resources Information Center

Warmington, Meesha; Hitch, Graham J.; Gathercole, Susan E.

2013-01-01

The current experiment examined the relative advantage of an errorless learning technique over an errorful one in the acquisition of novel names for unfamiliar objects in typically developing children aged between 7 and 9 years. Errorless learning led to significantly better learning than did errorful learning. Processing speed and vocabulary…

Aging and Learning.

ERIC Educational Resources Information Center

Mayor's Office for Senior Citizens, Chicago, IL.

The process of learning with respect to age is discussed. Learning may be defined as the acquisition of information or skills. Three non-cognitive factors varying with age are loss of speed, health, and motivation. Studies on learning in relation to age have not controlled for non-learning factors. Perceptual and psychomotor studies are not…

Engaging Environments Enhance Motor Skill Learning in a Computer Gaming Task.

PubMed

Lohse, Keith R; Boyd, Lara A; Hodges, Nicola J

2016-01-01

Engagement during practice can motivate a learner to practice more, hence having indirect effects on learning through increased practice. However, it is not known whether engagement can also have a direct effect on learning when the amount of practice is held constant. To address this question, 40 participants played a video game that contained an embedded repeated sequence component, under either highly engaging conditions (the game group) or mechanically identical but less engaging conditions (the sterile group). The game environment facilitated retention over a 1-week interval. Specifically, the game group improved in both speed and accuracy for random and repeated trials, suggesting a general motor-related improvement, rather than a specific influence of engagement on implicit sequence learning. These data provide initial evidence that increased engagement during practice has a direct effect on generalized learning, improving retention and transfer of a complex motor skill.

The High-Performance Computing and Communications program, the national information infrastructure and health care.

PubMed Central

Lindberg, D A; Humphreys, B L

1995-01-01

The High-Performance Computing and Communications (HPCC) program is a multiagency federal effort to advance the state of computing and communications and to provide the technologic platform on which the National Information Infrastructure (NII) can be built. The HPCC program supports the development of high-speed computers, high-speed telecommunications, related software and algorithms, education and training, and information infrastructure technology and applications. The vision of the NII is to extend access to high-performance computing and communications to virtually every U.S. citizen so that the technology can be used to improve the civil infrastructure, lifelong learning, energy management, health care, etc. Development of the NII will require resolution of complex economic and social issues, including information privacy. Health-related applications supported under the HPCC program and NII initiatives include connection of health care institutions to the Internet; enhanced access to gene sequence data; the "Visible Human" Project; and test-bed projects in telemedicine, electronic patient records, shared informatics tool development, and image systems. PMID:7614116

The Broadband Imperative: Recommendations to Address K-12 Education Infrastructure Needs. Executive Summary

ERIC Educational Resources Information Center

Fox, C.; Waters, J.; Fletcher, G.; Levin, D.

2012-01-01

It is a simple fact that access to high-speed broadband is now as vital a component of K-12 school infrastructure as electricity, air conditioning, and heating. The same tools and resources that have transformed educators' personal, civic, and professional lives must be part of learning experiences intended to prepare today's students for college…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Y.C.; Doolen, G.; Chen, H.H.

A high-order correlation tensor formalism for neural networks is described. The model can simulate auto associative, heteroassociative, as well as multiassociative memory. For the autoassociative model, simulation results show a drastic increase in the memory capacity and speed over that of the standard Hopfield-like correlation matrix methods. The possibility of using multiassociative memory for a learning universal inference network is also discussed. 9 refs., 5 figs.

Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning

PubMed Central

Schad, Daniel J.; Jünger, Elisabeth; Sebold, Miriam; Garbusow, Maria; Bernhardt, Nadine; Javadi, Amir-Homayoun; Zimmermann, Ulrich S.; Smolka, Michael N.; Heinz, Andreas; Rapp, Michael A.; Huys, Quentin J. M.

2014-01-01

Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed vs. habitual, or, more recently and based on statistical arguments, as model-free vs. model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation. PMID:25566131

The impact of odor–reward memory on chemotaxis in larval Drosophila

PubMed Central

Schleyer, Michael; Reid, Samuel F.; Pamir, Evren; Saumweber, Timo; Paisios, Emmanouil; Davies, Alexander

2015-01-01

How do animals adaptively integrate innate with learned behavioral tendencies? We tackle this question using chemotaxis as a paradigm. Chemotaxis in the Drosophila larva largely results from a sequence of runs and oriented turns. Thus, the larvae minimally need to determine (i) how fast to run, (ii) when to initiate a turn, and (iii) where to direct a turn. We first report how odor-source intensities modulate these decisions to bring about higher levels of chemotactic performance for higher odor-source intensities during innate chemotaxis. We then examine whether the same modulations are responsible for alterations of chemotactic performance by learned odor “valence” (understood throughout as level of attractiveness). We find that run speed (i) is neither modulated by the innate nor by the learned valence of an odor. Turn rate (ii), however, is modulated by both: the higher the innate or learned valence of the odor, the less often larvae turn whenever heading toward the odor source, and the more often they turn when heading away. Likewise, turning direction (iii) is modulated concordantly by innate and learned valence: turning is biased more strongly toward the odor source when either innate or learned valence is high. Using numerical simulations, we show that a modulation of both turn rate and of turning direction is sufficient to account for the empirically found differences in preference scores across experimental conditions. Our results suggest that innate and learned valence organize adaptive olfactory search behavior by their summed effects on turn rate and turning direction, but not on run speed. This work should aid studies into the neural mechanisms by which memory impacts specific aspects of behavior. PMID:25887280

Level of Recall, Retrieval Speed, and Variability on the Cued-Recall Retrieval Speed Task (CRRST) in Individuals with Amnestic Mild Cognitive Impairment

PubMed Central

Ramratan, Wendy S.; Rabin, Laura A.; Wang, Cuiling; Zimmerman, Molly E.; Katz, Mindy J.; Lipton, Richard B.; Buschke, Herman

2013-01-01

Individuals with amnestic mild cognitive impairment (aMCI) show deficits on traditional episodic memory tasks and reductions in speed of performance on reaction time tasks. We present results on a novel task, the Cued-Recall Retrieval Speed Test (CRRST), designed to simultaneously measure level and speed of retrieval. 390 older adults (mean age of 80.2 years), learned 16 words based on corresponding categorical cues. In the retrieval phase, we measured accuracy (% correct) and retrieval speed/reaction time (RT; time from cue presentation to voice onset of a correct response) across 6 trials. Compared to healthy elderly adults (HEA, n = 303), those with aMCI (n = 87) exhibited poorer performance in retrieval speed (difference = −0.13, p<.0001) and accuracy on the first trial (difference = −0.19, p<.0001), and their rate of improvement in retrieval speed was slower over subsequent trials. Those with aMCI also had greater within-person variability in processing speed (variance ratio = 1.22, p = 0.0098) and greater between-person variability in accuracy (variance ratio = 2.08, p = 0.0001) relative to HEA. Results are discussed in relation to the possibility that computer-based measures of cued-learning and processing speed variability may facilitate early detection of dementia in at-risk older adults. PMID:22265423

Online Bayesian Learning with Natural Sequential Prior Distribution Used for Wind Speed Prediction

NASA Astrophysics Data System (ADS)

Cheggaga, Nawal

2017-11-01

Predicting wind speed is one of the most important and critic tasks in a wind farm. All approaches, which directly describe the stochastic dynamics of the meteorological data are facing problems related to the nature of its non-Gaussian statistics and the presence of seasonal effects .In this paper, Online Bayesian learning has been successfully applied to online learning for three-layer perceptron's used for wind speed prediction. First a conventional transition model based on the squared norm of the difference between the current parameter vector and the previous parameter vector has been used. We noticed that the transition model does not adequately consider the difference between the current and the previous wind speed measurement. To adequately consider this difference, we use a natural sequential prior. The proposed transition model uses a Fisher information matrix to consider the difference between the observation models more naturally. The obtained results showed a good agreement between both series, measured and predicted. The mean relative error over the whole data set is not exceeding 5 %.

Knowledge Transfer: What, How, and Why

ERIC Educational Resources Information Center

Chin, Si-Chi

2013-01-01

People learn from prior experiences. We first learn how to use a spoon and then know how to use a different size of spoon. We first learn how to sew and then learn how to embroider. Transferring knowledge from one situation to another related situation often increases the speed of learning. This observation is relevant to human learning, as well…

Bio-Inspired Neural Model for Learning Dynamic Models

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu; Suri, Ronald

2009-01-01

A neural-network mathematical model that, relative to prior such models, places greater emphasis on some of the temporal aspects of real neural physical processes, has been proposed as a basis for massively parallel, distributed algorithms that learn dynamic models of possibly complex external processes by means of learning rules that are local in space and time. The algorithms could be made to perform such functions as recognition and prediction of words in speech and of objects depicted in video images. The approach embodied in this model is said to be "hardware-friendly" in the following sense: The algorithms would be amenable to execution by special-purpose computers implemented as very-large-scale integrated (VLSI) circuits that would operate at relatively high speeds and low power demands.

Speeding up the learning of robot kinematics through function decomposition.

PubMed

Ruiz de Angulo, Vicente; Torras, Carme

2005-11-01

The main drawback of using neural networks or other example-based learning procedures to approximate the inverse kinematics (IK) of robot arms is the high number of training samples (i.e., robot movements) required to attain an acceptable precision. We propose here a trick, valid for most industrial robots, that greatly reduces the number of movements needed to learn or relearn the IK to a given accuracy. This trick consists in expressing the IK as a composition of learnable functions, each having half the dimensionality of the original mapping. Off-line and on-line training schemes to learn these component functions are also proposed. Experimental results obtained by using nearest neighbors and parameterized self-organizing map, with and without the decomposition, show that the time savings granted by the proposed scheme grow polynomially with the precision required.

Adaptive filter design using recurrent cerebellar model articulation controller.

PubMed

Lin, Chih-Min; Chen, Li-Yang; Yeung, Daniel S

2010-07-01

A novel adaptive filter is proposed using a recurrent cerebellar-model-articulation-controller (CMAC). The proposed locally recurrent globally feedforward recurrent CMAC (RCMAC) has favorable properties of small size, good generalization, rapid learning, and dynamic response, thus it is more suitable for high-speed signal processing. To provide fast training, an efficient parameter learning algorithm based on the normalized gradient descent method is presented, in which the learning rates are on-line adapted. Then the Lyapunov function is utilized to derive the conditions of the adaptive learning rates, so the stability of the filtering error can be guaranteed. To demonstrate the performance of the proposed adaptive RCMAC filter, it is applied to a nonlinear channel equalization system and an adaptive noise cancelation system. The advantages of the proposed filter over other adaptive filters are verified through simulations.

Optimizing learning in healthcare: how Island Health is evolving to learn at the speed of change.

PubMed

Gottfredson, Conrad; Stroud, Carol; Jackson, Mary; Stevenson, R Lynn; Archer, Jana

2014-01-01

Healthcare organizations are challenged with constrained resources and increasing service demands by an aging population with complex care needs. Exponential growth in competency requirements also challenges staff's ability to provide quality patient care. How can a healthcare organization support its staff to learn "at or above the speed of change" while continuing to provide the quality patient care? Island Health is addressing this challenge by transforming its traditional education model into an innovative, evidence-based learning and performance support approach. Implementation of the methodology is yielding several lessons learned, both for the internal Learning and Performance Support team, and for what it takes to bring a new way of doing business into an organization. A key result is that this approach is enabling the organization to be more responsive in helping staff gain and maintain competencies.

What we can learn about hereditary dystonia from HSDI of the glottis

NASA Astrophysics Data System (ADS)

Pedersen, Mette; Eeg, Martin

2012-02-01

This study examined efficacy of the innate immune defence via the mannose binding lectin (MBL) in a cohort of 55 dystonic patients prospectively referred to the clinic with laryngeal mucosal complaints, who were placed on local steroids (budesonid inhaler, 400 μg 2 times daily) and antihistamines (fexofenadin 180 mg mostly 3 times daily) with adjuvant lifestyle corrections. Treatment efficacy of the larynx was assessed based on mucosal findings of the vocal folds examined with High speed mucosa studies comprising simultaneous high speed digital imagines (HSDI), kymography, electroglottography (EGG) and voice acoustics combined with a visual score of arytenoids oedema, as these measures are indicative of the magnitude of laryngitis. Lactose and gluten intolerance and immunological analyses of the innate system were made systematically. Results showed that the genetic aspects of immunology did not reveal a role for the innate immune system, represented by the mannose binding lectin (MBL). An unexpected positive effect of the larynx treatment on dystonia symptoms was found evidenced by reduction of dystonic complaints and more normative results of High speed mucosa, and a reduction of oedema of the inter arytenoids region. Symptoms relieve and better quality of life was observed on follow up for the dystonia complaints.

Machine Learning Estimates of Natural Product Conformational Energies

PubMed Central

Rupp, Matthias; Bauer, Matthias R.; Wilcken, Rainer; Lange, Andreas; Reutlinger, Michael; Boeckler, Frank M.; Schneider, Gisbert

2014-01-01

Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures. PMID:24453952

Assessment of processing speed in children with mild TBI: a "first look" at the validity of pediatric ImPACT.

PubMed

Newman, Julie B; Reesman, Jennifer H; Vaughan, Christopher G; Gioia, Gerard A

2013-01-01

Deficit in the speed of cognitive processing is a commonly identified neuropsychological change in children recovering from a mild TBI. However, there are few validated child assessment instruments that allow for serial assessment over the course of recovery in this population. Pediatric ImPACT is a novel measure that purports to assess cognitive speed, learning, and efficiency in this population. The current study sought to validate the use of this new measure by comparing it to traditional paper and pencil measures of processing speed. One hundred and sixty-four children (71% male) age 5-12 with mild TBI evaluated in an outpatient concussion clinic were administered Pediatric ImPACT and other neuropsychological test measures as part of a flexible test battery. Performance on the Response Speed Composite of Pediatric ImPACT was more strongly associated with other measures of cognitive processing speed, than with measures of immediate/working memory and learning/memory in this sample of injured children. There is preliminary support for convergent and discriminant validity of Pediatric ImPACT as a measure for use in post-concussion evaluations of processing speed in children.

Learning from Our Mistakes: Improvements in Spelling Lead to Gains in Reading Speed

ERIC Educational Resources Information Center

Ouellette, Gene; Martin-Chang, Sandra; Rossi, Maya

2017-01-01

The present study tested the hypothesis that underlying orthographic representations vary in completeness within the individual, which is manifested in both spelling accuracy and reading speed. Undergraduate students were trained to improve their spelling of difficult words. Word reading speed was then measured for these same words, allowing for a…

Hardware Neural Network for a Visual Inspection System

NASA Astrophysics Data System (ADS)

Chun, Seungwoo; Hayakawa, Yoshihiro; Nakajima, Koji

The visual inspection of defects in products is heavily dependent on human experience and instinct. In this situation, it is difficult to reduce the production costs and to shorten the inspection time and hence the total process time. Consequently people involved in this area desire an automatic inspection system. In this paper, we propose a hardware neural network, which is expected to provide high-speed operation for automatic inspection of products. Since neural networks can learn, this is a suitable method for self-adjustment of criteria for classification. To achieve high-speed operation, we use parallel and pipelining techniques. Furthermore, we use a piecewise linear function instead of a conventional activation function in order to save hardware resources. Consequently, our proposed hardware neural network achieved 6GCPS and 2GCUPS, which in our test sample proved to be sufficiently fast.

Video Analysis of a Plucked String: An Example of Problem-based Learning

NASA Astrophysics Data System (ADS)

Wentworth, Christopher D.; Buse, Eric

2009-11-01

Problem-based learning is a teaching methodology that grounds learning within the context of solving a real problem. Typically the problem initiates learning of concepts rather than simply being an application of the concept, and students take the lead in identifying what must be developed to solve the problem. Problem-based learning in upper-level physics courses can be challenging, because of the time and financial requirements necessary to generate real data. Here, we present a problem that motivates learning about partial differential equations and their solution in a mathematical methods for physics course. Students study a plucked elastic cord using high speed digital video. After creating video clips of the cord motion under different tensions they are asked to create a mathematical model. Ultimately, students develop and solve a model that includes damping effects that are clearly visible in the videos. The digital video files used in this project are available on the web at http://physics.doane.edu .

A Low-Power High-Speed Smart Sensor Design for Space Exploration Missions

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi

1997-01-01

A low-power high-speed smart sensor system based on a large format active pixel sensor (APS) integrated with a programmable neural processor for space exploration missions is presented. The concept of building an advanced smart sensing system is demonstrated by a system-level microchip design that is composed with an APS sensor, a programmable neural processor, and an embedded microprocessor in a SOI CMOS technology. This ultra-fast smart sensor system-on-a-chip design mimics what is inherent in biological vision systems. Moreover, it is programmable and capable of performing ultra-fast machine vision processing in all levels such as image acquisition, image fusion, image analysis, scene interpretation, and control functions. The system provides about one tera-operation-per-second computing power which is a two order-of-magnitude increase over that of state-of-the-art microcomputers. Its high performance is due to massively parallel computing structures, high data throughput rates, fast learning capabilities, and advanced VLSI system-on-a-chip implementation.

Airdata Measurement and Calibration

NASA Technical Reports Server (NTRS)

Haering, Edward A., Jr.

1995-01-01

This memorandum provides a brief introduction to airdata measurement and calibration. Readers will learn about typical test objectives, quantities to measure, and flight maneuvers and operations for calibration. The memorandum informs readers about tower-flyby, trailing cone, pacer, radar-tracking, and dynamic airdata calibration maneuvers. Readers will also begin to understand how some data analysis considerations and special airdata cases, including high-angle-of-attack flight, high-speed flight, and nonobtrusive sensors are handled. This memorandum is not intended to be all inclusive; this paper contains extensive reference and bibliography sections.

Efficient dynamic optimization of logic programs

NASA Technical Reports Server (NTRS)

Laird, Phil

1992-01-01

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

Initial animal studies of a wireless, batteryless, MEMS implant for cardiovascular applications.

PubMed

Najafi, Nader; Ludomirsky, Achiau

2004-03-01

This paper reports the results of the initial animal studies of a wireless, batteryless, implantable pressure sensor using microelectromechanical systems (MEMS) technology. The animal studies were acute and proved the functional feasibility of using MEMS technology for wireless bio sensing. The results are very encouraging and surpassed the majority of the application's requirements, including high sampling speed and high resolution. Based on the lessons learned, second generation wireless sensors are being developed that will provide total system solution.

Multi-stage learning aids applied to hands-on software training.

PubMed

Rother, Kristian; Rother, Magdalena; Pleus, Alexandra; Upmeier zu Belzen, Annette

2010-11-01

Delivering hands-on tutorials on bioinformatics software and web applications is a challenging didactic scenario. The main reason is that trainees have heterogeneous backgrounds, different previous knowledge and vary in learning speed. In this article, we demonstrate how multi-stage learning aids can be used to allow all trainees to progress at a similar speed. In this technique, the trainees can utilize cards with hints and answers to guide themselves self-dependently through a complex task. We have successfully conducted a tutorial for the molecular viewer PyMOL using two sets of learning aid cards. The trainees responded positively, were able to complete the task, and the trainer had spare time to respond to individual questions. This encourages us to conclude that multi-stage learning aids overcome many disadvantages of established forms of hands-on software training.

Path integration of head direction: updating a packet of neural activity at the correct speed using neuronal time constants.

PubMed

Walters, D M; Stringer, S M

2010-07-01

A key question in understanding the neural basis of path integration is how individual, spatially responsive, neurons may self-organize into networks that can, through learning, integrate velocity signals to update a continuous representation of location within an environment. It is of vital importance that this internal representation of position is updated at the correct speed, and in real time, to accurately reflect the motion of the animal. In this article, we present a biologically plausible model of velocity path integration of head direction that can solve this problem using neuronal time constants to effect natural time delays, over which associations can be learned through associative Hebbian learning rules. The model comprises a linked continuous attractor network and competitive network. In simulation, we show that the same model is able to learn two different speeds of rotation when implemented with two different values for the time constant, and without the need to alter any other model parameters. The proposed model could be extended to path integration of place in the environment, and path integration of spatial view.

Robust sensorimotor representation to physical interaction changes in humanoid motion learning.

PubMed

Shimizu, Toshihiko; Saegusa, Ryo; Ikemoto, Shuhei; Ishiguro, Hiroshi; Metta, Giorgio

2015-05-01

This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply this knowledge during different physical interactions between a robot and its surroundings. The phase transfer sequence represents the temporal order of the changing points in multiple time sequences. It encodes the dynamical aspects of the sequences so as to absorb the gaps in timing and amplitude derived from interaction changes. The phase transfer sequence was evaluated in reinforcement learning of sitting-up and walking motions conducted by a real humanoid robot and compatible simulator. In both tasks, the robotic motions were less dependent on physical interactions when learned by the proposed feature than by conventional similarity measurements. Phase transfer sequence also enhanced the convergence speed of motion learning. Our proposed feature is original primarily because it absorbs the gaps caused by changes of the originally acquired physical interactions, thereby enhancing the learning speed in subsequent interactions.

Rapid e-Learning Tools Selection Process for Cognitive and Psychomotor Learning Objectives

ERIC Educational Resources Information Center

Ku, David Tawei; Huang, Yung-Hsin

2012-01-01

This study developed a decision making process for the selection of rapid e-learning tools that could match different learning domains. With the development of the Internet, the speed of information updates has become faster than ever. E-learning has rapidly become the mainstream for corporate training and academic instruction. In order to reduce…

Virtual Speed Mentoring in the Workplace--Current Approaches to Personal Informal Learning in the Workplace: A Case Study

ERIC Educational Resources Information Center

Hamilton, Chuck; Langlois, Kristen; Watson, Henry

2010-01-01

Informal learning is the biggest undiscovered treasure in today's workplace. Marcia Conner, author and often-cited voice for workplace learning, suggests that "Informal learning accounts for over 75% of the learning taking place in organizations today" (1997). IBM understands the value of the hyper-connected informal workplace and…

Can Executive Functions Help to Understand Children with Mathematical Learning Disorders and to Improve Instruction?

ERIC Educational Resources Information Center

Desoete, Annemie; De Weerdt, Frauke

2013-01-01

Working memory, inhibition and naming speed was assessed in 22 children with mathematical learning disorders (MD), 17 children with a reading learning disorder (RD), and 45 children without any learning problems between 8 and 12 years old. All subjects with learning disorders performed poorly on working memory tasks, providing evidence that they…

Using Log Variables in a Learning Management System to Evaluate Learning Activity Using the Lens of Activity Theory

ERIC Educational Resources Information Center

Park, Yeonjeong; Jo, Il-Hyun

2017-01-01

As the advance of learning technologies and analytics tools continues, learning management systems (LMSs) have been required to fulfil the growing expectations for smart learning. However, the reality regarding the level of technology integration in higher education differs considerably from such expectations or the speed of advances in…

A Fast, Minimalist Search Tool for Remote Sensing Data

NASA Astrophysics Data System (ADS)

Lynnes, C. S.; Macharrie, P. G.; Elkins, M.; Joshi, T.; Fenichel, L. H.

2005-12-01

We present a tool that emphasizes speed and simplicity in searching remotely sensed Earth Science data. The tool, nicknamed "Mirador" (Spanish for a scenic overlook), provides only four freetext search form fields, for Keywords, Location, Data Start and Data Stop. This contrasts with many current Earth Science search tools that offer highly structured interfaces in order to ensure precise, non-zero results. The disadvantages of the structured approach lie in its complexity and resultant learning curve, as well as the time it takes to formulate and execute the search, thus discouraging iterative discovery. On the other hand, the success of the basic Google search interface shows that many users are willing to forgo high search precision if the search process is fast enough to enable rapid iteration. Therefore, we employ several methods to increase the speed of search formulation and execution. Search formulation is expedited by the minimalist search form, with only one required field. Also, a gazetteer enables the use of geographic terms as shorthand for latitude/longitude coordinates. The search execution is accelerated by initially presenting dataset results (returned from a Google Mini appliance) with an estimated number of "hits" for each dataset based on the user's space-time constraints. The more costly file-level search is executed against a PostGres database only when the user "drills down", and then covering only the fraction of the time period needed to return the next page of results. The simplicity of the search form makes the tool easy to learn and use, and the speed of the searches enables an iterative form of data discovery.

Reading speed in the peripheral visual field of older adults: Does it benefit from perceptual learning?

PubMed

Yu, Deyue; Cheung, Sing-Hang; Legge, Gordon E; Chung, Susana T L

2010-04-21

Enhancing reading ability in peripheral vision is important for the rehabilitation of people with central-visual-field loss from age-related macular degeneration (AMD). Previous research has shown that perceptual learning, based on a trigram letter-recognition task, improved peripheral reading speed among normally-sighted young adults (Chung, Legge, & Cheung, 2004). Here we ask whether the same happens in older adults in an age range more typical of the onset of AMD. Eighteen normally-sighted subjects, aged 55-76years, were randomly assigned to training or control groups. Visual-span profiles (plots of letter-recognition accuracy as a function of horizontal letter position) and RSVP reading speeds were measured at 10 degrees above and below fixation during pre- and post-tests for all subjects. Training consisted of repeated measurements of visual-span profiles at 10 degrees below fixation, in four daily sessions. The control subjects did not receive any training. Perceptual learning enlarged the visual spans in both trained (lower) and untrained (upper) visual fields. Reading speed improved in the trained field by 60% when the trained print size was used. The training benefits for these older subjects were weaker than the training benefits for young adults found by Chung et al. Despite the weaker training benefits, perceptual learning remains a potential option for low-vision reading rehabilitation among older adults. Copyright 2010 Elsevier Ltd. All rights reserved.

Learning, Attention/Hyperactivity, and Conduct Problems as Sequelae of Excessive Daytime Sleepiness in a General Population Study of Young Children

PubMed Central

Calhoun, Susan L.; Fernandez-Mendoza, Julio; Vgontzas, Alexandros N.; Mayes, Susan D.; Tsaoussoglou, Marina; Rodriguez-Muñoz, Alfredo; Bixler, Edward O.

2012-01-01

Study Objectives: Although excessive daytime sleepiness (EDS) is a common problem in children, with estimates of 15%; few studies have investigated the sequelae of EDS in young children. We investigated the association of EDS with objective neurocognitive measures and parent reported learning, attention/hyperactivity, and conduct problems in a large general population sample of children. Design: Cross-sectional. Setting: Population based. Participants: 508 children from The Penn State Child Cohort. Interventions: N/A. Measurements and Results: Children underwent a 9-h polysomnogram, comprehensive neurocognitive testing, and parent rating scales. Children were divided into 2 groups: those with and without parent-reported EDS. Structural equation modeling was used to examine whether processing speed and working memory performance would mediate the relationship between EDS and learning, attention/hyperactivity, and conduct problems. Logistic regression models suggest that parent-reported learning, attention/hyperactivity, and conduct problems, as well as objective measurement of processing speed and working memory are significant sequelae of EDS, even when controlling for AHI and objective markers of sleep. Path analysis demonstrates that processing speed and working memory performance are strong mediators of the association of EDS with learning and attention/hyperactivity problems, while to a slightly lesser degree are mediators from EDS to conduct problems. Conclusions: This study suggests that in a large general population sample of young children, parent-reported EDS is associated with neurobehavioral (learning, attention/hyperactivity, conduct) problems and poorer performance in processing speed and working memory. Impairment due to EDS in daytime cognitive and behavioral functioning can have a significant impact on children's development. Citation: Calhoun SL; Fernandez-Mendoza J; Vgontzas AN; Mayes SD; Tsaoussoglou M; Rodriguez-Muñoz A; Bixler EO. Learning, attention/hyperactivity, and conduct problems as sequelae of excessive daytime sleepiness in a general population study of young children. SLEEP 2012;35(5):627-632. PMID:22547888

Interference due to shared features between action plans is influenced by working memory span.

PubMed

Fournier, Lisa R; Behmer, Lawrence P; Stubblefield, Alexandra M

2014-12-01

In this study, we examined the interactions between the action plans that we hold in memory and the actions that we carry out, asking whether the interference due to shared features between action plans is due to selection demands imposed on working memory. Individuals with low and high working memory spans learned arbitrary motor actions in response to two different visual events (A and B), presented in a serial order. They planned a response to the first event (A) and while maintaining this action plan in memory they then executed a speeded response to the second event (B). Afterward, they executed the action plan for the first event (A) maintained in memory. Speeded responses to the second event (B) were delayed when it shared an action feature (feature overlap) with the first event (A), relative to when it did not (no feature overlap). The size of the feature-overlap delay was greater for low-span than for high-span participants. This indicates that interference due to overlapping action plans is greater when fewer working memory resources are available, suggesting that this interference is due to selection demands imposed on working memory. Thus, working memory plays an important role in managing current and upcoming action plans, at least for newly learned tasks. Also, managing multiple action plans is compromised in individuals who have low versus high working memory spans.

A discussion on velocity-speed and their instruction

NASA Astrophysics Data System (ADS)

Yıldız, Ali

2016-04-01

This study was conducted to investigate how to teach velocity and speed effectively, with which activities and examples. Although they are different quantities, they are generally used in the same meaning. Study data and the quantities discussed were obtained from the examination of documents such as scientific articles and books about the instruction and they were examined by descriptive analysis approach. Expository instruction was supported with writing to learn activities and an approach actualized in seven stages was suggested so that velocity and speed could be understood at an anticipated level. At each stage, possible practices were explained; and especially at the fifth stage of the study, a detailed example on distance, displacement, velocity and speed promoted the understanding of presented quantities much more easily and correctly with their critical properties, thus students would be able to associate it with their prior knowledge. Moreover, it was anticipated based on these reasons that the example could be used as a tool to actualize permanent learning. At the last stage of the study, it was considered that having students write a letter and a summary to young respondents could support the practices in the previous stages, and also it would help promoting long-term retention of the learned concepts.

FTA low-speed urban Maglev research program : updated lessons learned.

DOT National Transportation Integrated Search

2012-11-01

In 1999, the Federal Transit Administration (FTA) initiated the Low-Speed Urban Magnetic Levitation (Urban Maglev) Program to develop magnetic levitation technology that offers a cost-effective, reliable, and environmentally-sound transit option for ...

CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing- learning-actuating system for high-speed visual object recognition and tracking.

PubMed

Serrano-Gotarredona, Rafael; Oster, Matthias; Lichtsteiner, Patrick; Linares-Barranco, Alejandro; Paz-Vicente, Rafael; Gomez-Rodriguez, Francisco; Camunas-Mesa, Luis; Berner, Raphael; Rivas-Perez, Manuel; Delbruck, Tobi; Liu, Shih-Chii; Douglas, Rodney; Hafliger, Philipp; Jimenez-Moreno, Gabriel; Civit Ballcels, Anton; Serrano-Gotarredona, Teresa; Acosta-Jimenez, Antonio J; Linares-Barranco, Bernabé

2009-09-01

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies.

Smart Aquarium as Physics Learning Media for Renewable Energy

NASA Astrophysics Data System (ADS)

Desnita, D.; Raihanati, R.; Susanti, D.

2018-04-01

Smart aquarium has been developed as a learning media to visualize Micro Hydro Power Generator (MHPG). Its used aquarium water circulation system and Wind Power Generation (WPG) which generated through a wheel as a source. Its also used to teach about energy changes, circular motion and wheel connection, electromagnetic impact, and AC power circuit. The output power and system efficiency was adjusted through the adjustment of water level and wind speed. Specific targets in this research are: to achieved: (i) develop green aquarium technology that’s suitable to used as a medium of physics learning, (ii) improving quality of process and learning result at a senior high school student. Research method used development research by Borg and Gall, which includes preliminary studies, design, product development, expert validation, and product feasibility test, and vinalisation. The validation test by the expert states that props feasible to use. Limited trials conducted prove that this tool can improve students science process skills.

Staff Perceptions of E-Learning in a Community Health Care Organization

ERIC Educational Resources Information Center

Gabriel, Monica; Longman, Sandra

2004-01-01

How do organizations cope with the increased speed of technological change? How do leaders optimize resources with tightened budgets? How do staff and students acquire the necessary knowledge and skills in the midst of constant change? Electronic learning (e-learning) is one form of learning that utilizes technology to deliver, interact or…

Learning in Context: Linguistic and Attentional Constraints on Children's Color Term Learning

ERIC Educational Resources Information Center

O'Hanlon, Catherine G.; Roberson, Debi

2006-01-01

Three experiments investigated whether linguistic and/or attentional constraints might account for preschoolers' difficulties when learning color terms. Task structure and demands were equated across experiments, and both speed and degree of learning were compared. In Experiment 1, 3-year-olds who were matched on vocabulary score were taught new…

The Role of Cognitive Abilities in Laparoscopic Simulator Training

ERIC Educational Resources Information Center

Groenier, M.; Schraagen, J. M. C.; Miedema, H. A. T.; Broeders, I. A. J. M.

2014-01-01

Learning minimally invasive surgery (MIS) differs substantially from learning open surgery and trainees differ in their ability to learn MIS. Previous studies mainly focused on the role of visuo-spatial ability (VSA) on the learning curve for MIS. In the current study, the relationship between spatial memory, perceptual speed, and general…

Predicting ICME properties at 1AU

NASA Astrophysics Data System (ADS)

Lago, A.; Braga, C. R.; Mesquita, A. L.; De Mendonça, R. R. S.

2017-12-01

Coronal mass ejections (CMEs) are among the main origins of geomagnetic disturbances. They change the properties of the near-earth interplanetary medium, enhancing some key parameters, such as the southward interplanetary magnetic field and the solar wind speed. Both quantities are known to be related to the energy transfer from the solar wind to the Earth's magnetosphere via the magnetic reconnection process. Many attempts have been made to predict the magnetic filed and the solar wind speed from coronagraph observations. However, we still have much to learn about the dynamic evolution of ICMEs as they propagate through the interplanetary space. Increased observation capability is probably needed. Among the several attempts to establish correlations between CME and ICME properties, it was found that the average CME propagation speed to 1AU is highly correlated to the ICME peak speed (Dal Lago et al, 2004). In this work, we present an extended study of such correlation, which confirms the results found in our previous study. Some suggestions on how to use this kind of results for space weather estimates are explored.

Monetary reward speeds up voluntary saccades.

PubMed

Chen, Lewis L; Chen, Y Mark; Zhou, Wu; Mustain, William D

2014-01-01

Past studies have shown that reward contingency is critical for sensorimotor learning, and reward expectation speeds up saccades in animals. Whether monetary reward speeds up saccades in human remains unknown. Here we addressed this issue by employing a conditional saccade task, in which human subjects performed a series of non-reflexive, visually-guided horizontal saccades. The subjects were (or were not) financially compensated for making a saccade in response to a centrally-displayed visual congruent (or incongruent) stimulus. Reward modulation of saccadic velocities was quantified independently of the amplitude-velocity coupling. We found that reward expectation significantly sped up voluntary saccades up to 30°/s, and the reward modulation was consistent across tests. These findings suggest that monetary reward speeds up saccades in human in a fashion analogous to how juice reward sped up saccades in monkeys. We further noticed that the idiosyncratic nasal-temporal velocity asymmetry was highly consistent regardless of test order, and its magnitude was not correlated with the magnitude of reward modulation. This suggests that reward modulation and the intrinsic velocity asymmetry may be governed by separate mechanisms that regulate saccade generation.

Relationship between accuracy and complexity when learning underarm precision throwing.

PubMed

Valle, Maria Stella; Lombardo, Luciano; Cioni, Matteo; Casabona, Antonino

2018-06-12

Learning precision ball throwing was mostly studied to explore the early rapid improvement of accuracy, with poor attention on possible adaptive processes occurring later when the rate of improvement is reduced. Here, we tried to demonstrate that the strategy to select angle, speed and height at ball release can be managed during the learning periods following the performance stabilization. To this aim, we used a multivariate linear model with angle, speed and height as predictors of changes in accuracy. Participants performed underarm throws of a tennis ball to hit a target on the floor, 3.42 m away. Two training sessions (S1, S2) and one retention test were executed. Performance accuracy increased over the S1 and stabilized during the S2, with a rate of changes along the throwing axis slower than along the orthogonal axis. However, both the axes contributed to the performance changes over the learning and consolidation time. A stable relationship between the accuracy and the release parameters was observed only during S2, with a good fraction of the performance variance explained by the combination of speed and height. All the variations were maintained during the retention test. Overall, accuracy improvements and reduction in throwing complexity at the ball release followed separate timing over the course of learning and consolidation.

Learning moment-based fast local binary descriptor

NASA Astrophysics Data System (ADS)

Bellarbi, Abdelkader; Zenati, Nadia; Otmane, Samir; Belghit, Hayet

2017-03-01

Recently, binary descriptors have attracted significant attention due to their speed and low memory consumption; however, using intensity differences to calculate the binary descriptive vector is not efficient enough. We propose an approach to binary description called POLAR_MOBIL, in which we perform binary tests between geometrical and statistical information using moments in the patch instead of the classical intensity binary test. In addition, we introduce a learning technique used to select an optimized set of binary tests with low correlation and high variance. This approach offers high distinctiveness against affine transformations and appearance changes. An extensive evaluation on well-known benchmark datasets reveals the robustness and the effectiveness of the proposed descriptor, as well as its good performance in terms of low computation complexity when compared with state-of-the-art real-time local descriptors.

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…

Progesterone After Estradiol Modulates Shuttle-Cage Escape by Facilitating Volition

PubMed Central

Mayeaux, Darryl J.; Tandle, Sarah M.; Cilano, Sean M.; Fitzharris, Matthew J.

2015-01-01

In animal models of depression, depression is defined as performance on a learning task. That task is typically escaping a mild electric shock in a shuttle cage by moving from one side of the cage to the other. Ovarian hormones influence learning in other kinds of tasks, and these hormones are associated with depressive symptoms in humans. The role of these hormones in shuttle-cage escape learning, however, is less clear. This study manipulated estradiol and progesterone in ovariectomized female rats to examine their performance in shuttle-cage escape learning without intentionally inducing a depressive-like state. Progesterone, not estradiol, within four hours of testing affected latencies to escape. The improvement produced by progesterone was in the decision to act, not in the speed of learning or speed of escaping. This parallels depression in humans in that depressed people are slower in volition, in their decisions to take action. PMID:26823653

The effect of red grape juice on Alzheimer's disease in rats

PubMed Central

Siahmard, Zahra; Alaei, Hojjatollah; Reisi, Parham; Pilehvarian, Ali Asghar

2012-01-01

Background: Alzheimer's disease is a neurodegenerative disease appearing as a result of free radicals and oxidative stress. Antioxidants agents boost memory and control Alzheimer's disease. Since red grape juice contains antioxidant agents, its effects on speed of learning and improvement of memory was studied in Alzheimer's rats. Materials and Methods: Alzheimer's model was induced by bilateral infusion of streptozocine into lateral ventricles of brain of male rats. Rats drank 10% red grape juice for 21 days. Passive avoidance learning test was used for measuring memory and learning in rats. Results: Our results showed that learning and memory in STZ-group decreased significantly compared to Sham group. However, intake of red grape juice increased speed of learning and improvement of memory in Alzheimer's rats. Conclusions: Our results suggest that there are active ingredients in red grape juice, which probably have therapeutic and preventive effects on cognitive impairments in Alzheimer's disease. PMID:23326794

The effect of red grape juice on Alzheimer's disease in rats.

PubMed

Siahmard, Zahra; Alaei, Hojjatollah; Reisi, Parham; Pilehvarian, Ali Asghar

2012-01-01

Alzheimer's disease is a neurodegenerative disease appearing as a result of free radicals and oxidative stress. Antioxidants agents boost memory and control Alzheimer's disease. Since red grape juice contains antioxidant agents, its effects on speed of learning and improvement of memory was studied in Alzheimer's rats. Alzheimer's model was induced by bilateral infusion of streptozocine into lateral ventricles of brain of male rats. Rats drank 10% red grape juice for 21 days. Passive avoidance learning test was used for measuring memory and learning in rats. Our results showed that learning and memory in STZ-group decreased significantly compared to Sham group. However, intake of red grape juice increased speed of learning and improvement of memory in Alzheimer's rats. Our results suggest that there are active ingredients in red grape juice, which probably have therapeutic and preventive effects on cognitive impairments in Alzheimer's disease.

Gains following perceptual learning are closely linked to the initial visual acuity.

PubMed

Yehezkel, Oren; Sterkin, Anna; Lev, Maria; Levi, Dennis M; Polat, Uri

2016-04-28

The goal of the present study was to evaluate the dependence of perceptual learning gains on initial visual acuity (VA), in a large sample of subjects with a wide range of VAs. A large sample of normally sighted and presbyopic subjects (N = 119; aged 40 to 63) with a wide range of uncorrected near visual acuities (VA, -0.12 to 0.8 LogMAR), underwent perceptual learning. Training consisted of detecting briefly presented Gabor stimuli under spatial and temporal masking conditions. Consistent with previous findings, perceptual learning induced a significant improvement in near VA and reading speed under conditions of limited exposure duration. Our results show that the improvements in VA and reading speed observed following perceptual learning are closely linked to the initial VA, with only a minor fraction of the observed improvement that may be attributed to the additional sessions performed by those with the worse VA.

Using Deep Learning to Analyze the Voices of Stars.

NASA Astrophysics Data System (ADS)

Boudreaux, Thomas Macaulay

2018-01-01

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and compare the performance of different deep learning algorithms, including Artifical Neural Netoworks, and Convolutional Neural Networks, in classifing these synthetic data sets as either pulsators, or not observed to vary stars.

Word learning emerges from the interaction of online referent selection and slow associative learning

PubMed Central

McMurray, Bob; Horst, Jessica S.; Samuelson, Larissa K.

2013-01-01

Classic approaches to word learning emphasize the problem of referential ambiguity: in any naming situation the referent of a novel word must be selected from many possible objects, properties, actions, etc. To solve this problem, researchers have posited numerous constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative model in which referent selection is an online process that is independent of long-term learning. This two timescale approach creates significant power in the developing system. We illustrate this with a dynamic associative model in which referent selection is simulated as dynamic competition between competing referents, and learning is simulated using associative (Hebbian) learning. This model can account for a range of findings including the delay in expressive vocabulary relative to receptive vocabulary, learning under high degrees of referential ambiguity using cross-situational statistics, accelerating (vocabulary explosion) and decelerating (power-law) learning rates, fast-mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between individual differences in speed of processing and learning. Five theoretical points are illustrated. 1) Word learning does not require specialized processes – general association learning buttressed by dynamic competition can account for much of the literature. 2) The processes of recognizing familiar words are not different than those that support novel words (e.g., fast-mapping). 3) Online competition may allow the network (or child) to leverage information available in the task to augment performance or behavior despite what might be relatively slow learning or poor representations. 4) Even associative learning is more complex than previously thought – a major contributor to performance is the pruning of incorrect associations between words and referents. 5) Finally, the model illustrates that learning and referent selection/word recognition, though logically distinct, can be deeply and subtly related as phenomena like speed of processing and mutual exclusivity may derive in part from the way learning shapes the system. As a whole, this suggests more sophisticated ways of describing the interaction between situation- and developmental-time processes and points to the need for considering such interactions as a primary determinant of development and processing in children. PMID:23088341

FTA Low-speed urban maglev research program lessons learned : March 2009.

DOT National Transportation Integrated Search

2009-03-01

In 1999, the Federal Transit Administration initiated the Low-Speed Urban Magnetic Levitation (UML) Program to develop magnetic levitation technology that offers a cost effective, reliable, and environmentally sound transit option for urban mass tran...

Integrating Machine Learning into a Crowdsourced Model for Earthquake-Induced Damage Assessment

NASA Technical Reports Server (NTRS)

Rebbapragada, Umaa; Oommen, Thomas

2011-01-01

On January 12th, 2010, a catastrophic 7.0M earthquake devastated the country of Haiti. In the aftermath of an earthquake, it is important to rapidly assess damaged areas in order to mobilize the appropriate resources. The Haiti damage assessment effort introduced a promising model that uses crowdsourcing to map damaged areas in freely available remotely-sensed data. This paper proposes the application of machine learning methods to improve this model. Specifically, we apply work on learning from multiple, imperfect experts to the assessment of volunteer reliability, and propose the use of image segmentation to automate the detection of damaged areas. We wrap both tasks in an active learning framework in order to shift volunteer effort from mapping a full catalog of images to the generation of high-quality training data. We hypothesize that the integration of machine learning into this model improves its reliability, maintains the speed of damage assessment, and allows the model to scale to higher data volumes.

Exercise effects on cognitive functioning in young adults with first-episode psychosis: FitForLife.

PubMed

Hallgren, Mats; Skott, Maria; Ekblom, Örjan; Firth, Joseph; Schembri, Adrian; Forsell, Yvonne

2018-05-06

Exercise has mood-enhancing effects and can improve cognitive functioning, but the effects in first-episode psychosis (FEP) remain understudied. We examined the feasibility and cognitive effects of exercise in FEP. Multi-center, open-label intervention study. Ninety-one outpatients with FEP (mean age = 30 years, 65% male) received usual care plus a 12-week supervised circuit-training program, consisting of high-volume resistance exercises, aerobic training, and stretching. Primary study outcome was cognitive functioning assessed by Cogstate Brief Battery (processing speed, attention, visual learning, working memory) and Trailmaking A and B tasks (visual attention and task shifting). Within-group changes in cognition were assessed using paired sample t tests with effect sizes (Hedges' g) reported for significant values. Relationships between exercise frequency and cognitive improvement were assessed using analysis of covariance. Moderating effects of gender were explored with stratified analyses. Participants exercised on average 13.5 (s.d. = 11.7) times. Forty-eight percent completed 12 or more sessions. Significant post-intervention improvements were seen for processing speed, visual learning, and visual attention; all with moderate effect sizes (g = 0.47-0.49, p < 0.05). Exercise participation was also associated with a positive non-significant trend for working memory (p < 0.07). Stratified analyses indicated a moderating effect of gender. Positive changes were seen among females only for processing speed, visual learning, working memory, and visual attention (g = 0.43-0.69). A significant bivariate correlation was found between total training frequency and improvements in visual attention among males (r = 0.40, p < 0.05). Supported physical exercise is a feasible and safe adjunct treatment for FEP with potential cognitive benefits, especially among females.

Influence of shift work on cognitive performance in male business process outsourcing employees

PubMed Central

Shwetha, Bijavara; Sudhakar, Honnamachanahalli

2012-01-01

Background: India is a front runner in IT industry. Business process outsourcing (BPO) sector is a major part of IT industry with around 4.5 million employees. These employees are subjected to high work stress, odd working hours, and frequent shift changes leading to increased physical and mental health problems. Aim: To study the cognitive functions in male BPO employees exposed to regular shifts. Settings and Design: Young BPO employees from various BPO companies of Bangalore were tested for cognitive functions. Materials and Methods: Fifty male BPO employees exposed to regular shifts were assessed for various cognitive functions including tests for speed, attention, learning and memory, and executive function. They were compared with 50 non-BPO employees not working in shifts. Statistical analysis - Data was analysed by t-test and Mann-Whitney test using SPSS V.13.0. Results: BPO employees performed poorly compared to their controls in tests for mental speed, learning and memory, and response inhibition. No changes were seen between groups in tests for attention and working memory. Conclusion: Cognitive functions are impaired in BPO employees exposed to regular shift changes. PMID:23776319

Influence of shift work on cognitive performance in male business process outsourcing employees.

PubMed

Shwetha, Bijavara; Sudhakar, Honnamachanahalli

2012-09-01

India is a front runner in IT industry. Business process outsourcing (BPO) sector is a major part of IT industry with around 4.5 million employees. These employees are subjected to high work stress, odd working hours, and frequent shift changes leading to increased physical and mental health problems. To study the cognitive functions in male BPO employees exposed to regular shifts. Young BPO employees from various BPO companies of Bangalore were tested for cognitive functions. Fifty male BPO employees exposed to regular shifts were assessed for various cognitive functions including tests for speed, attention, learning and memory, and executive function. They were compared with 50 non-BPO employees not working in shifts. Statistical analysis - Data was analysed by t-test and Mann-Whitney test using SPSS V.13.0. BPO employees performed poorly compared to their controls in tests for mental speed, learning and memory, and response inhibition. No changes were seen between groups in tests for attention and working memory. Cognitive functions are impaired in BPO employees exposed to regular shift changes.

Optical implementation of neural learning algorithms based on cross-gain modulation in a semiconductor optical amplifier

NASA Astrophysics Data System (ADS)

Li, Qiang; Wang, Zhi; Le, Yansi; Sun, Chonghui; Song, Xiaojia; Wu, Chongqing

2016-10-01

Neuromorphic engineering has a wide range of applications in the fields of machine learning, pattern recognition, adaptive control, etc. Photonics, characterized by its high speed, wide bandwidth, low power consumption and massive parallelism, is an ideal way to realize ultrafast spiking neural networks (SNNs). Synaptic plasticity is believed to be critical for learning, memory and development in neural circuits. Experimental results have shown that changes of synapse are highly dependent on the relative timing of pre- and postsynaptic spikes. Synaptic plasticity in which presynaptic spikes preceding postsynaptic spikes results in strengthening, while the opposite timing results in weakening is called antisymmetric spike-timing-dependent plasticity (STDP) learning rule. And synaptic plasticity has the opposite effect under the same conditions is called antisymmetric anti-STDP learning rule. We proposed and experimentally demonstrated an optical implementation of neural learning algorithms, which can achieve both of antisymmetric STDP and anti-STDP learning rule, based on the cross-gain modulation (XGM) within a single semiconductor optical amplifier (SOA). The weight and height of the potentitation and depression window can be controlled by adjusting the injection current of the SOA, to mimic the biological antisymmetric STDP and anti-STDP learning rule more realistically. As the injection current increases, the width of depression and potentitation window decreases and height increases, due to the decreasing of recovery time and increasing of gain under a stronger injection current. Based on the demonstrated optical STDP circuit, ultrafast learning in optical SNNs can be realized.

A teaching-learning sequence about weather map reading

NASA Astrophysics Data System (ADS)

Mandrikas, Achilleas; Stavrou, Dimitrios; Skordoulis, Constantine

2017-07-01

In this paper a teaching-learning sequence (TLS) introducing pre-service elementary teachers (PET) to weather map reading, with emphasis on wind assignment, is presented. The TLS includes activities about recognition of wind symbols, assignment of wind direction and wind speed on a weather map and identification of wind characteristics in a weather forecast. Sixty PET capabilities and difficulties in understanding weather maps were investigated, using inquiry-based learning activities. The results show that most PET became more capable of reading weather maps and assigning wind direction and speed on them. Our results also show that PET could be guided to understand meteorology concepts useful in everyday life and in teaching their future students.

Do Performers' Experience and Sex Affect Their Performance?

PubMed

Emmanuel, Jacobs; Nathalie, Roussel; Van Caekenberghe, Ine; Cassiers, Edith; Van den Dries, Luc; Rutgeerts, Jonas; Gielen, Jan; Hallemans, Ann

2017-04-01

This cross-sectional study aimed at developing a biomechanical method to objectify voluntary and unpredictable movements, using an automated three-dimensional motion capture system and surface electromyography. Fourteen experienced theater performers were tested while executing the old man exercise, wherein they have to walk like an old man, building up a sustained high intensive muscular activity and tremor. Less experienced performed showed a different kinematics of movement, a slower speed of progression and more variable EMG signals at higher intensity. Female performers also differed from males in movement kinematics and muscular activity. The number of the trial only influenced the speed of progression. The performers showed results which could be well placed within the stages of learning and the degrees of freedom problem.

The Relative Merits of Transparency: Investigating Situations that Support the Use of Robotics in Developing Student Learning Adaptability across Virtual and Physical Computing Platforms

ERIC Educational Resources Information Center

Okita, Sandra Y.

2014-01-01

This study examined whether developing earlier forms of knowledge in specific learning environments prepares students better for future learning when they are placed in an unfamiliar learning environment. Forty-one students in the fifth and sixth grades learned to program robot movements using abstract concepts of speed, distance and direction.…

On-chip learning of hyper-spectral data for real time target recognition

NASA Technical Reports Server (NTRS)

Duong, T. A.; Daud, T.; Thakoor, A.

2000-01-01

As the focus of our present paper, we have used the cascade error projection (CEP) learning algorithm (shown to be hardware-implementable) with on-chip learning (OCL) scheme to obtain three orders of magnitude speed-up in target recognition compared to software-based learning schemes. Thus, it is shown, real time learning as well as data processing for target recognition can be achieved.

Robust Fault Diagnosis in Electric Drives Using Machine Learning

DTIC Science & Technology

2004-09-08

detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points...were used to generate various fault condition data for machine learning . The technique is viable for accurate, reliable and fast fault detection in electric drives.

Dynamic Sensorimotor Planning during Long-Term Sequence Learning: The Role of Variability, Response Chunking and Planning Errors

PubMed Central

Verstynen, Timothy; Phillips, Jeff; Braun, Emily; Workman, Brett; Schunn, Christian; Schneider, Walter

2012-01-01

Many everyday skills are learned by binding otherwise independent actions into a unified sequence of responses across days or weeks of practice. Here we looked at how the dynamics of action planning and response binding change across such long timescales. Subjects (N = 23) were trained on a bimanual version of the serial reaction time task (32-item sequence) for two weeks (10 days total). Response times and accuracy both showed improvement with time, but appeared to be learned at different rates. Changes in response speed across training were associated with dynamic changes in response time variability, with faster learners expanding their variability during the early training days and then contracting response variability late in training. Using a novel measure of response chunking, we found that individual responses became temporally correlated across trials and asymptoted to set sizes of approximately 7 bound responses at the end of the first week of training. Finally, we used a state-space model of the response planning process to look at how predictive (i.e., response anticipation) and error-corrective (i.e., post-error slowing) processes correlated with learning rates for speed, accuracy and chunking. This analysis yielded non-monotonic association patterns between the state-space model parameters and learning rates, suggesting that different parts of the response planning process are relevant at different stages of long-term learning. These findings highlight the dynamic modulation of response speed, variability, accuracy and chunking as multiple movements become bound together into a larger set of responses during sequence learning. PMID:23056630

The effect of normal aging and age-related macular degeneration on perceptual learning.

PubMed

Astle, Andrew T; Blighe, Alan J; Webb, Ben S; McGraw, Paul V

2015-01-01

We investigated whether perceptual learning could be used to improve peripheral word identification speed. The relationship between the magnitude of learning and age was established in normal participants to determine whether perceptual learning effects are age invariant. We then investigated whether training could lead to improvements in patients with age-related macular degeneration (AMD). Twenty-eight participants with normal vision and five participants with AMD trained on a word identification task. They were required to identify three-letter words, presented 10° from fixation. To standardize crowding across each of the letters that made up the word, words were flanked laterally by randomly chosen letters. Word identification performance was measured psychophysically using a staircase procedure. Significant improvements in peripheral word identification speed were demonstrated following training (71% ± 18%). Initial task performance was correlated with age, with older participants having poorer performance. However, older adults learned more rapidly such that, following training, they reached the same level of performance as their younger counterparts. As a function of number of trials completed, patients with AMD learned at an equivalent rate as age-matched participants with normal vision. Improvements in word identification speed were maintained at least 6 months after training. We have demonstrated that temporal aspects of word recognition can be improved in peripheral vision with training across a range of ages and these learned improvements are relatively enduring. However, training targeted at other bottlenecks to peripheral reading ability, such as visual crowding, may need to be incorporated to optimize this approach.

The effect of normal aging and age-related macular degeneration on perceptual learning

PubMed Central

Astle, Andrew T.; Blighe, Alan J.; Webb, Ben S.; McGraw, Paul V.

2015-01-01

We investigated whether perceptual learning could be used to improve peripheral word identification speed. The relationship between the magnitude of learning and age was established in normal participants to determine whether perceptual learning effects are age invariant. We then investigated whether training could lead to improvements in patients with age-related macular degeneration (AMD). Twenty-eight participants with normal vision and five participants with AMD trained on a word identification task. They were required to identify three-letter words, presented 10° from fixation. To standardize crowding across each of the letters that made up the word, words were flanked laterally by randomly chosen letters. Word identification performance was measured psychophysically using a staircase procedure. Significant improvements in peripheral word identification speed were demonstrated following training (71% ± 18%). Initial task performance was correlated with age, with older participants having poorer performance. However, older adults learned more rapidly such that, following training, they reached the same level of performance as their younger counterparts. As a function of number of trials completed, patients with AMD learned at an equivalent rate as age-matched participants with normal vision. Improvements in word identification speed were maintained at least 6 months after training. We have demonstrated that temporal aspects of word recognition can be improved in peripheral vision with training across a range of ages and these learned improvements are relatively enduring. However, training targeted at other bottlenecks to peripheral reading ability, such as visual crowding, may need to be incorporated to optimize this approach. PMID:26605694

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

PubMed Central

Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods. PMID:27610128

Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

PubMed

Pang, Shan; Yang, Xinyi

2016-01-01

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

Using Computers To Accommodate Learning Disabled Students in Mathematics Classes.

ERIC Educational Resources Information Center

Rapp, Rhonda H.; Gittinger, Dennis J.

A person with a learning disability usually has average or above average intelligence, but has difficulty taking in, remembering, or expressing information. Learning disabilities can involve visual processing speed, short-term memory processing, fluid reasoning, and long-term memory retrieval. These disorders are intrinsic to the individual and…

Split-belt walking adaptation recalibrates sensorimotor estimates of leg speed but not position or force

PubMed Central

Vazquez, Alejandro; Statton, Matthew A.; Busgang, Stefanie A.

2015-01-01

Motor learning during reaching not only recalibrates movement but can also lead to small but consistent changes in the sense of arm position. Studies have suggested that this sensory effect may be the result of recalibration of a forward model that associates motor commands with their sensory consequences. Here we investigated whether similar perceptual changes occur in the lower limbs after learning a new walking pattern on a split-belt treadmill—a task that critically involves proprioception. Specifically, we studied how this motor learning task affects perception of leg speed during walking, perception of leg position during standing or walking, and perception of contact force during stepping. Our results show that split-belt adaptation leads to robust motor aftereffects and alters the perception of leg speed during walking. This is specific to the direction of walking that was trained during adaptation (i.e., backward or forward). The change in leg speed perception accounts for roughly half of the observed motor aftereffect. In contrast, split-belt adaptation does not alter the perception of leg position during standing or walking and does not change the perception of stepping force. Our results demonstrate that there is a recalibration of a sensory percept specific to the domain of the perturbation that was applied during walking (i.e., speed but not position or force). Furthermore, the motor and sensory consequences of locomotor adaptation may be linked, suggesting overlapping mechanisms driving changes in the motor and sensory domains. PMID:26424576

Hemispheric preference and progressive-part or whole practice in beginning typewriting.

PubMed

Johns, L B

1989-04-01

This investigation explored the interaction of progressive-part versus whole methods of practice with hemispheric preference for processing information and the impact of each upon high school students' speed and accuracy in beginning typewriting. Zenhausern's Differential Hemispheric Activation Test was scored in such a way that it was possible to plot the scores along a continuum. Analysis of variance gave significant F ratios on 3 of the 4 testing days. The continuous scores were divided into five categories: middle, left moderates, right moderates, extreme rights, and extreme lefts. The moderate-left group speed was consistently the fastest group, and the extreme rights were consistently the slowest group. This difference was significant for all four testing days with the moderate-left mean speed varying between 4 to 6 words per minute faster each testing day. The extreme rights were consistently the most accurate, even though not statistically significantly so. There was no significant difference between method of practice and typewriting speed or between method of practice and typewriting accuracy; however, on all four testing days the mean gross speed of the whole practice learning group was 0.73 to 0.99 words per minute faster than the progressive-part group. A two-way analysis of variance indicated no interaction between method or practice and hemispheric preference.

Electrical Stimulation of the Midbrain to Promote Recovery from Traumatic Forebrain Injury

DTIC Science & Technology

2009-04-01

the beneficial trophic effects . The cylinder test, taken to indicate somatosensory function, gave highly variable results. We were unable to see a...learning in a hidden-platform water maze test was speeded by both dorsal and median raphe stimulation. Rearing movements in a transparent cylinder ...sensorimotor performance) were normalized by the median but not the dorsal raphe. One adverse effect was seen: the dorsal but not the median raphe reduced

Silicon photonics for neuromorphic information processing

NASA Astrophysics Data System (ADS)

Bienstman, Peter; Dambre, Joni; Katumba, Andrew; Freiberger, Matthias; Laporte, Floris; Lugnan, Alessio

2018-02-01

We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. We will discuss aspects like scalability, novel architectures for enhanced power efficiency, as well as all-optical readout. Additionally, we will touch upon new machine learning techniques to operate these integrated readouts. Finally, we will show how these systems can be used for high-speed low-power information processing for applications like recognition of biological cells.

Word learning emerges from the interaction of online referent selection and slow associative learning.

PubMed

McMurray, Bob; Horst, Jessica S; Samuelson, Larissa K

2012-10-01

Classic approaches to word learning emphasize referential ambiguity: In naming situations, a novel word could refer to many possible objects, properties, actions, and so forth. To solve this, researchers have posited constraints, and inference strategies, but assume that determining the referent of a novel word is isomorphic to learning. We present an alternative in which referent selection is an online process and independent of long-term learning. We illustrate this theoretical approach with a dynamic associative model in which referent selection emerges from real-time competition between referents and learning is associative (Hebbian). This model accounts for a range of findings including the differences in expressive and receptive vocabulary, cross-situational learning under high degrees of ambiguity, accelerating (vocabulary explosion) and decelerating (power law) learning, fast mapping by mutual exclusivity (and differences in bilinguals), improvements in familiar word recognition with development, and correlations between speed of processing and learning. Together it suggests that (a) association learning buttressed by dynamic competition can account for much of the literature; (b) familiar word recognition is subserved by the same processes that identify the referents of novel words (fast mapping); (c) online competition may allow the children to leverage information available in the task to augment performance despite slow learning; (d) in complex systems, associative learning is highly multifaceted; and (e) learning and referent selection, though logically distinct, can be subtly related. It suggests more sophisticated ways of describing the interaction between situation- and developmental-time processes and points to the need for considering such interactions as a primary determinant of development. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Visual object tracking by correlation filters and online learning

NASA Astrophysics Data System (ADS)

Zhang, Xin; Xia, Gui-Song; Lu, Qikai; Shen, Weiming; Zhang, Liangpei

2018-06-01

Due to the complexity of background scenarios and the variation of target appearance, it is difficult to achieve high accuracy and fast speed for object tracking. Currently, correlation filters based trackers (CFTs) show promising performance in object tracking. The CFTs estimate the target's position by correlation filters with different kinds of features. However, most of CFTs can hardly re-detect the target in the case of long-term tracking drifts. In this paper, a feature integration object tracker named correlation filters and online learning (CFOL) is proposed. CFOL estimates the target's position and its corresponding correlation score using the same discriminative correlation filter with multi-features. To reduce tracking drifts, a new sampling and updating strategy for online learning is proposed. Experiments conducted on 51 image sequences demonstrate that the proposed algorithm is superior to the state-of-the-art approaches.

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.

Working memory supports inference learning just like classification learning.

PubMed

Craig, Stewart; Lewandowsky, Stephan

2013-08-01

Recent research has found a positive relationship between people's working memory capacity (WMC) and their speed of category learning. To date, only classification-learning tasks have been considered, in which people learn to assign category labels to objects. It is unknown whether learning to make inferences about category features might also be related to WMC. We report data from a study in which 119 participants undertook classification learning and inference learning, and completed a series of WMC tasks. Working memory capacity was positively related to people's classification and inference learning performance.

An assessment of support vector machines for land cover classification

USGS Publications Warehouse

Huang, C.; Davis, L.S.; Townshend, J.R.G.

2002-01-01

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

Learning fast accurate movements requires intact frontostriatal circuits

PubMed Central

Shabbott, Britne; Ravindran, Roshni; Schumacher, Joseph W.; Wasserman, Paula B.; Marder, Karen S.; Mazzoni, Pietro

2013-01-01

The basal ganglia are known to play a crucial role in movement execution, but their importance for motor skill learning remains unclear. Obstacles to our understanding include the lack of a universally accepted definition of motor skill learning (definition confound), and difficulties in distinguishing learning deficits from execution impairments (performance confound). We studied how healthy subjects and subjects with a basal ganglia disorder learn fast accurate reaching movements. We addressed the definition and performance confounds by: (1) focusing on an operationally defined core element of motor skill learning (speed-accuracy learning), and (2) using normal variation in initial performance to separate movement execution impairment from motor learning abnormalities. We measured motor skill learning as performance improvement in a reaching task with a speed-accuracy trade-off. We compared the performance of subjects with Huntington's disease (HD), a neurodegenerative basal ganglia disorder, to that of premanifest carriers of the HD mutation and of control subjects. The initial movements of HD subjects were less skilled (slower and/or less accurate) than those of control subjects. To factor out these differences in initial execution, we modeled the relationship between learning and baseline performance in control subjects. Subjects with HD exhibited a clear learning impairment that was not explained by differences in initial performance. These results support a role for the basal ganglia in both movement execution and motor skill learning. PMID:24312037

Can reading-specific training stimuli improve the effect of perceptual learning on peripheral reading speed?

PubMed

Bernard, Jean-Baptiste; Arunkumar, Amit; Chung, Susana T L

2012-08-01

In a previous study, Chung, Legge, and Cheung (2004) showed that training using repeated presentation of trigrams (sequences of three random letters) resulted in an increase in the size of the visual span (number of letters recognized in a glance) and reading speed in the normal periphery. In this study, we asked whether we could optimize the benefit of trigram training on reading speed by using trigrams more specific to the reading task (i.e., trigrams frequently used in the English language) and presenting them according to their frequencies of occurrence in normal English usage and observers' performance. Averaged across seven observers, our training paradigm (4 days of training) increased the size of the visual span by 6.44 bits, with an accompanied 63.6% increase in the maximum reading speed, compared with the values before training. However, these benefits were not statistically different from those of Chung, Legge, and Cheung (2004) using a random-trigram training paradigm. Our findings confirm the possibility of increasing the size of the visual span and reading speed in the normal periphery with perceptual learning, and suggest that the benefits of training on letter recognition and maximum reading speed may not be linked to the types of letter strings presented during training. Copyright © 2012 Elsevier Ltd. All rights reserved.

Two-year Test-Retest Reliability in High School Athletes Using the Four- and Two-Factor ImPACT Composite Structures: The Effects of Learning Disorders and Headache/Migraine Treatment History.

PubMed

Brett, Benjamin L; Solomon, Gary S; Hill, Jennifer; Schatz, Philip

2018-03-01

This study examined the test-retest reliability of the four- and two-factor structures (i.e., Memory and Speed) of ImPACT over a 2-year interval across multiple groups with premorbid conditions, including those with a history of special education or learning disorders (LD; n = 114), treatment history for headache/migraine (n = 81), and a control group (n = 792). Nine hundred and eighty seven high school athletes completed baseline testing using online ImPACT across a 2-year interval. Paired-samples t-tests documented improvement from initial to follow-up assessments. Test stability was examined using Regression-based measures (RBM) and Reliable change indices (RCI). Reliability was examined using intraclass correlation coefficients (ICC). Significant improvement on all four composites were observed for the control group over a 2-year interval; whereas significant differences were observed only on Visual Motor Speed for the LD and headache/migraine treatment history groups. ICCs ranges were similar across groups and greater or comparable reliability was observed for the two-factor structure on Memory (0.67-0.73) and Speed (0.76-0.78) composites. RCIs and RBMs demonstrated stability for the four- and two-factor structures, with few cases falling outside the range of expected change within a healthy sample at the 90% and 95% CIs. Typical practices of obtaining new baselines every 2 years in the high school population can be applied to athletes with a history of special education or LD and headache/migraine treatment. The two-factor structure has potential to increase test-retest reliability. Further research regarding clinical utility is needed. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata.

PubMed

Zhang, JunQi; Wang, Cheng; Zhou, MengChu

2015-10-01

Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

A linear recurrent kernel online learning algorithm with sparse updates.

PubMed

Fan, Haijin; Song, Qing

2014-02-01

In this paper, we propose a recurrent kernel algorithm with selectively sparse updates for online learning. The algorithm introduces a linear recurrent term in the estimation of the current output. This makes the past information reusable for updating of the algorithm in the form of a recurrent gradient term. To ensure that the reuse of this recurrent gradient indeed accelerates the convergence speed, a novel hybrid recurrent training is proposed to switch on or off learning the recurrent information according to the magnitude of the current training error. Furthermore, the algorithm includes a data-dependent adaptive learning rate which can provide guaranteed system weight convergence at each training iteration. The learning rate is set as zero when the training violates the derived convergence conditions, which makes the algorithm updating process sparse. Theoretical analyses of the weight convergence are presented and experimental results show the good performance of the proposed algorithm in terms of convergence speed and estimation accuracy. Copyright © 2013 Elsevier Ltd. All rights reserved.

Drift diffusion model of reward and punishment learning in schizophrenia: Modeling and experimental data.

PubMed

Moustafa, Ahmed A; Kéri, Szabolcs; Somlai, Zsuzsanna; Balsdon, Tarryn; Frydecka, Dorota; Misiak, Blazej; White, Corey

2015-09-15

In this study, we tested reward- and punishment learning performance using a probabilistic classification learning task in patients with schizophrenia (n=37) and healthy controls (n=48). We also fit subjects' data using a Drift Diffusion Model (DDM) of simple decisions to investigate which components of the decision process differ between patients and controls. Modeling results show between-group differences in multiple components of the decision process. Specifically, patients had slower motor/encoding time, higher response caution (favoring accuracy over speed), and a deficit in classification learning for punishment, but not reward, trials. The results suggest that patients with schizophrenia adopt a compensatory strategy of favoring accuracy over speed to improve performance, yet still show signs of a deficit in learning based on negative feedback. Our data highlights the importance of applying fitting models (particularly drift diffusion models) to behavioral data. The implications of these findings are discussed relative to theories of schizophrenia and cognitive processing. Copyright © 2015 Elsevier B.V. All rights reserved.

Get Up to Speed on the Latest Product Releases in the Education Market

ERIC Educational Resources Information Center

Technology & Learning, 2007

2007-01-01

This article provides brief descriptions of the latest product releases in the education market. These products include hardware, software, and resources. The following products are presented in this article, but not limited to: (1) Radius Audio Learning System(www.learningresources.com); (2) The Indigo Learning System from LearningSoft, LLC…

Speech and Nonspeech Sequence Skill Learning in Adults Who Stutter

ERIC Educational Resources Information Center

Smits-Bandstra, Sarah; De Nil, Luc; Saint-Cyr, Jean A.

2006-01-01

Two studies compared the speech and nonspeech sequence skill learning of nine persons who stutter (PWS) and nine matched fluent speakers (PNS). Sequence skill learning was defined as a continuing process of stable improvement in speed and/or accuracy of sequencing performance over practice and was measured by comparing PWS's and PNS's performance…

Contingency Learning and Reactivity in Preterm and Full-Term Infants at 3 Months

ERIC Educational Resources Information Center

Haley, David W.; Grunau, Ruth E.; Oberlander, Tim F.; Weinberg, Joanne

2008-01-01

Learning difficulties in preterm infants are thought to reflect impairment in arousal regulation. We examined relationships among gestational age, learning speed, and behavioral and physiological reactivity in 55 preterm and 49 full-term infants during baseline, contingency, and nonreinforcement phases of a conjugate mobile paradigm at 3 months…

The Student Experience in Speed Teaming: A New Approach to Team Formation

ERIC Educational Resources Information Center

Hansen, Randall S.; Hansen, Katharine

2007-01-01

Many benefits accrue for students when they work in teams. Researchers have shown that the learning-by-doing approach of group projects results in active learning and far greater comprehension and retention, higher levels of student achievement, accomplishment of sophisticated learning objectives, the development of critical reasoning skills, and…

Lifelong Learning: Foundational Models, Underlying Assumptions and Critiques

ERIC Educational Resources Information Center

Regmi, Kapil Dev

2015-01-01

Lifelong learning has become a catchword in almost all countries because of its growing influence on education policies in the globalised world. In the Organisation for Economic Cooperation and Development (OECD) and the European Union (EU), the promotion of lifelong learning has been a strategy to speed up economic growth and become competitive.…

Updating and Not Shifting Predicts Learning Performance in Young and Middle-Aged Adults

ERIC Educational Resources Information Center

Gijselaers, Hieronymus J. M.; Meijs, Celeste; Neroni, Joyce; Kirschner, Paul A.; de Groot, Renate H. M.

2017-01-01

The goal of this study was to investigate whether single executive function (EF) tests were predictive for learning performance in mainly young and middle-aged adults. The tests measured shifting and updating. Processing speed was also measured. In an observational study, cognitive performance and learning performance were measured objectively in…

Learning at the Speed of Light: Deep Learning and Accelerated Online Graduate Courses

ERIC Educational Resources Information Center

Trekles, Anastasia M.

2013-01-01

An increasing number of university programs, particularly at the graduate level, are moving to an accelerated, time-compressed model for online degree offerings. However, the literature revealed that research in distance education effectiveness is still confounded by many variables, including course design and student approach to learning.…

Alumina Concentration Detection Based on the Kernel Extreme Learning Machine.

PubMed

Zhang, Sen; Zhang, Tao; Yin, Yixin; Xiao, Wendong

2017-09-01

The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

Motor Learning Versus StandardWalking Exercise in Older Adults with Subclinical Gait Dysfunction: A Randomized Clinical Trial

PubMed Central

Brach, Jennifer S.; Van Swearingen, Jessie M.; Perera, Subashan; Wert, David M.; Studenski, Stephanie

2013-01-01

Background Current exercise recommendationsfocus on endurance and strength, but rarely incorporate principles of motor learning. Motor learning exerciseis designed to address neurological aspects of movement. Motor learning exercise has not been evaluated in older adults with subclinical gait dysfunction. Objectives Tocompare motor learning versus standard exercise on measures of mobility and perceived function and disability. Design Single-blind randomized trial. Setting University research center. Participants Olderadults (n=40), mean age 77.1±6.0 years), who had normal walking speed (≥1.0 m/s) and impaired motor skill (Figure of 8 walk time > 8 s). Interventions The motor learning program (ML) incorporated goal-oriented stepping and walking to promote timing and coordination within the phases of the gait cycle. The standard program (S) employed endurance training by treadmill walking.Both included strength training and were offered twice weekly for one hour for 12 weeks. Measurements Primary outcomes included mobility performance (gait efficiency, motor skill in walking, gait speed, and walking endurance)and secondary outcomes included perceived function and disability (Late Life Function and Disability Instrument). Results 38 of 40 participants completed the trial (ML, n=18; S, n=20). ML improved more than Sin gait speed (0.13 vs. 0.05 m/s, p=0.008) and motor skill (−2.2 vs. −0.89 s, p<0.0001). Both groups improved in walking endurance (28.3 and 22.9m, but did not differ significantly p=0.14). Changes in gait efficiency and perceived function and disability were not different between the groups (p>0.10). Conclusion In older adults with subclinical gait dysfunction, motor learning exercise improved some parameters of mobility performance more than standard exercise. PMID:24219189

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

PubMed

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Behavioral training to improve collision detection

PubMed Central

DeLoss, Denton J.; Bian, Zheng; Watanabe, Takeo; Andersen, George J.

2015-01-01

Young drivers are a high-risk group for vehicle crashes due to inexperience in detecting an impending collision and are one group that may benefit from perceptual learning (PL) training. The present study assessed whether PL could be used to improve performance in collision detection. Ten college-aged subjects participated in the first experiment, which consisted of seven 1-hr sessions conducted on separate days. Thresholds at three observer/object speeds were measured prior to training using a two-alternative forced choice procedure during which they indicated whether an approaching object would result in a collision or noncollision event. Participants were then trained near threshold at one of these speeds for 5 days. After training, participants showed a significant reduction in the time needed to detect a collision at the trained speed. This improvement was also found to transfer to the higher observer speed condition. A second experiment was conducted to determine whether this improvement was due to training near threshold or whether this improvement was merely due to practice with the task. Training with stimuli well above threshold showed no significant improvement in performance, indicating that the improvement seen in the first experiment was not solely due to task practice. PMID:26230917

Learning to explore the structure of kinematic objects in a virtual environment

PubMed Central

Buckmann, Marcus; Gaschler, Robert; Höfer, Sebastian; Loeben, Dennis; Frensch, Peter A.; Brock, Oliver

2015-01-01

The current study tested the quantity and quality of human exploration learning in a virtual environment. Given the everyday experience of humans with physical object exploration, we document substantial practice gains in the time, force, and number of actions needed to classify the structure of virtual chains, marking the joints as revolute, prismatic, or rigid. In line with current work on skill acquisition, participants could generalize the new and efficient psychomotor patterns of object exploration to novel objects. On the one hand, practice gains in exploration performance could be captured by a negative exponential practice function. On the other hand, they could be linked to strategies and strategy change. After quantifying how much was learned in object exploration and identifying the time course of practice-related gains in exploration efficiency (speed), we identified what was learned. First, we identified strategy components that were associated with efficient (fast) exploration performance: sequential processing, simultaneous use of both hands, low use of pulling rather than pushing, and low use of force. Only the latter was beneficial irrespective of the characteristics of the other strategy components. Second, we therefore characterized efficient exploration behavior by strategies that simultaneously take into account the abovementioned strategy components. We observed that participants maintained a high level of flexibility, sampling from a pool of exploration strategies trading the level of psycho-motoric challenges with exploration speed. We discuss the findings pursuing the aim of advancing intelligent object exploration by combining analytic (object exploration in humans) and synthetic work (object exploration in robots) in the same virtual environment. PMID:25904878

Cognitive learning: a machine learning approach for automatic process characterization from design

NASA Astrophysics Data System (ADS)

Foucher, J.; Baderot, J.; Martinez, S.; Dervilllé, A.; Bernard, G.

2018-03-01

Cutting edge innovation requires accurate and fast process-control to obtain fast learning rate and industry adoption. Current tools available for such task are mainly manual and user dependent. We present in this paper cognitive learning, which is a new machine learning based technique to facilitate and to speed up complex characterization by using the design as input, providing fast training and detection time. We will focus on the machine learning framework that allows object detection, defect traceability and automatic measurement tools.

Constructing a Plastic Bottle Wind Turbine as a Practical Aid for Learning about Using Wind Energy to Generate Electricity

ERIC Educational Resources Information Center

Appleyard, S. J.

2009-01-01

A simple horizontal axis wind turbine can be easily constructed using a 1.5 l PET plastic bottle, a compact disc and a small dynamo. The turbine operates effectively at low wind speeds and has a rotational speed of 500 rpm at a wind speed of about 14 km h[superscript -1]. The wind turbine can be used to demonstrate the relationship between open…

Acquisition of Programming Skills

DTIC Science & Technology

1990-04-01

skills (e.g., arithmetic reasoning, work knowledge, information processing speed); and c) passive versus active learning style. Ability measures...concurrent storage and processing an individual was capable of doing), and an active learning style. Implications of the findings for the development of

The California Verbal Learning Test-Children's Version: relation to factor indices of the Wechsler Intelligence Scale for Children-Third Edition.

PubMed

O'Jile, Judith R; Schrimsher, Gregory W; O'Bryant, Sid E

2005-10-01

The California Verbal Learning Test-Children's Version (CVLT-C) provides clinicians with a method of assessing various aspects of children's verbal memory and has been found to be sensitive to memory deficits resulting from a variety of neurological conditions. Intuitively, the CVLT-C would be expected to be highly related to a child's verbal cognitive abilities; however, with only a few exceptions, the relationship of this test to various domains of cognitive function has not been broadly studied empirically. To examine this issue, we evaluated the amount of unique variance in CVLT-C scores that could be predicted by the Verbal Comprehension, Perceptual Organization, Freedom from Distractibility, and Processing Speed indices of the Wechsler Intelligence Scale for Children, Third Edition (WISC-III) beyond that accounted for by age and gender in a sample of 62 children referred to an outpatient psychiatry clinic for neuropsychological evaluation. While the Processing Speed Index predicted a significant amount of variance for both short and long delay free and cued recall, the Verbal Comprehension Index was a poor predictor of CVLT-C performance on all outcome variables, accounting for only 1.5 to 4.5% additional variance above age and gender. These findings indicate that while the CVLT-C may be relatively independent of influences of verbal intelligence and abstract verbal reasoning, general speed and efficiency of processing play an important role in successful encoding for later retrieval on the CVLT-C.

Fuzzy Q-Learning for Generalization of Reinforcement Learning

NASA Technical Reports Server (NTRS)

Berenji, Hamid R.

1996-01-01

Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

Acquisition and extinction in autoshaping.

PubMed

Kakade, Sham; Dayan, Peter

2002-07-01

C. R. Gallistel and J. Gibbon (2000) presented quantitative data on the speed with which animals acquire behavioral responses during autoshaping, together with a statistical model of learning intended to account for them. Although this model captures the form of the dependencies among critical variables, its detailed predictions are substantially at variance with the data. In the present article, further key data on the speed of acquisition are used to motivate an alternative model of learning, in which animals can be interpreted as paying different amounts of attention to stimuli according to estimates of their differential reliabilities as predictors.

MRI and Neuropsychological Correlates in African Americans with Hypertension and left vEntricular Hypertrophy.

PubMed

Waldron-Perrine, B; Kisser, J E; Brody, A; Haacke, E M; Dawood, R; Millis, S; Levy, P

2018-04-17

African Americans (AA) are at high risk for hypertension (HTN) and poor blood pressure (BP) control. Persistently elevated BP contributes to cardiovascular morbidity. White matter hyperintensities (WMH) are a definable magnetic resonance imaging (MRI) marker of cerebrovascular injury linked to impairments in higher level thinking (i.e., executive functions), memory formation and speed of perceptual-motor processing. This sub-investigation evaluated neuropsychological functioning in association with WMH on brain MRIs in 23 otherwise healthy hypertensive AAs participating in an NIH-funded study of the effects of Vitamin D on BP and cardiac remodeling in AA patients 30-74 years of age with HTN and left ventricular hypertrophy. Neuropsychological assessment included psychomotor processing speed [(Symbol Digit Modality Test (SDMT) and Trail Making Test], executive functioning (Controlled Oral Word Association Test and Trail Making Test Part B), memory (Rey Auditory Verbal Learning Test), and fine motor functioning (Finger Tapping). Significant correlations (p< .05) were found between volume of periventricular lesions and Trails A (r = .51) and dominant hand finger tapping speed (r = -.69) and between subcortical lesion volume and Trails A (r = .60), both dominant (r = -.62) and non-dominant hand finger tapping speed (r = -.76) and oral SDMT (r = -.60); higher lesion volumes correlated to worse neuropsychological performance. Psychomotor tests including the Trail Making Test and finger tapping speed are sensitive indicators of subclinical deficits in mental processing speed and could serve as early markers of deep subcortical cerebrovascular injury in otherwise-healthy individuals with uncontrolled chronic HTN.

COMPOSER: A Probabilistic Solution to the Utility Problem in Speed-up Learning.

ERIC Educational Resources Information Center

Gratch, Jonathan; DeJong, Gerald

In machine learning there is considerable interest in techniques which improve planning ability. Initial investigations have identified a wide variety of techniques to address this issue. Progress has been hampered by the utility problem, a basic tradeoff between the benefit of learned knowledge and the cost to locate and apply relevant knowledge.…

Impact of Conscious Intent on Chunking during Motor Learning

ERIC Educational Resources Information Center

Song, Sunbin; Cohen, Leonardo

2014-01-01

Humans and other mammals learn sequences of movements by splitting them into smaller "chunks." Such chunks are defined by the faster speed of performance of groups of movements. The purpose of this report is to determine how conscious intent to learn impacts chunking, an issue that remains unknown. Here, we studied 80 subjects who either…

Incidental Learning Speeds Visual Search by Lowering Response Thresholds, Not by Improving Efficiency: Evidence from Eye Movements

ERIC Educational Resources Information Center

Hout, Michael C.; Goldinger, Stephen D.

2012-01-01

When observers search for a target object, they incidentally learn the identities and locations of "background" objects in the same display. This learning can facilitate search performance, eliciting faster reaction times for repeated displays. Despite these findings, visual search has been successfully modeled using architectures that maintain no…

Quickprop method to speed up learning process of Artificial Neural Network in money's nominal value recognition case

NASA Astrophysics Data System (ADS)

Swastika, Windra

2017-03-01

A money's nominal value recognition system has been developed using Artificial Neural Network (ANN). ANN with Back Propagation has one disadvantage. The learning process is very slow (or never reach the target) in the case of large number of iteration, weight and samples. One way to speed up the learning process is using Quickprop method. Quickprop method is based on Newton's method and able to speed up the learning process by assuming that the weight adjustment (E) is a parabolic function. The goal is to minimize the error gradient (E'). In our system, we use 5 types of money's nominal value, i.e. 1,000 IDR, 2,000 IDR, 5,000 IDR, 10,000 IDR and 50,000 IDR. One of the surface of each nominal were scanned and digitally processed. There are 40 patterns to be used as training set in ANN system. The effectiveness of Quickprop method in the ANN system was validated by 2 factors, (1) number of iterations required to reach error below 0.1; and (2) the accuracy to predict nominal values based on the input. Our results shows that the use of Quickprop method is successfully reduce the learning process compared to Back Propagation method. For 40 input patterns, Quickprop method successfully reached error below 0.1 for only 20 iterations, while Back Propagation method required 2000 iterations. The prediction accuracy for both method is higher than 90%.

Reward speeds up and increases consistency of visual selective attention: a lifespan comparison.

PubMed

Störmer, Viola; Eppinger, Ben; Li, Shu-Chen

2014-06-01

Children and older adults often show less favorable reward-based learning and decision making, relative to younger adults. It is unknown, however, whether reward-based processes that influence relatively early perceptual and attentional processes show similar lifespan differences. In this study, we investigated whether stimulus-reward associations affect selective visual attention differently across the human lifespan. Children, adolescents, younger adults, and older adults performed a visual search task in which the target colors were associated with either high or low monetary rewards. We discovered that high reward value speeded up response times across all four age groups, indicating that reward modulates attentional selection across the lifespan. This speed-up in response time was largest in younger adults, relative to the other three age groups. Furthermore, only younger adults benefited from high reward value in increasing response consistency (i.e., reduction of trial-by-trial reaction time variability). Our findings suggest that reward-based modulations of relatively early and implicit perceptual and attentional processes are operative across the lifespan, and the effects appear to be greater in adulthood. The age-specific effect of reward on reducing intraindividual response variability in younger adults likely reflects mechanisms underlying the development and aging of reward processing, such as lifespan age differences in the efficacy of dopaminergic modulation. Overall, the present results indicate that reward shapes visual perception across different age groups by biasing attention to motivationally salient events.

Graph Representations of Flow and Transport in Fracture Networks using Machine Learning

NASA Astrophysics Data System (ADS)

Srinivasan, G.; Viswanathan, H. S.; Karra, S.; O'Malley, D.; Godinez, H. C.; Hagberg, A.; Osthus, D.; Mohd-Yusof, J.

2017-12-01

Flow and transport of fluids through fractured systems is governed by the properties and interactions at the micro-scale. Retaining information about the micro-structure such as fracture length, orientation, aperture and connectivity in mesh-based computational models results in solving for millions to billions of degrees of freedom and quickly renders the problem computationally intractable. Our approach depicts fracture networks graphically, by mapping fractures to nodes and intersections to edges, thereby greatly reducing computational burden. Additionally, we use machine learning techniques to build simulators on the graph representation, trained on data from the mesh-based high fidelity simulations to speed up computation by orders of magnitude. We demonstrate our methodology on ensembles of discrete fracture networks, dividing up the data into training and validation sets. Our machine learned graph-based solvers result in over 3 orders of magnitude speedup without any significant sacrifice in accuracy.

Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

PubMed

Ak, Ronay; Fink, Olga; Zio, Enrico

2016-08-01

The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

The applications of deep neural networks to sdBV classification

NASA Astrophysics Data System (ADS)

Boudreaux, Thomas M.

2017-12-01

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and we show that two separate paradigms of deep learning - the Artificial Neural Network And the Convolutional Neural Network - can both be used to classify this synthetic data effectively. And that additionally this classification can be performed at relatively high levels of accuracy with minimal time spent adjusting network hyperparameters.

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models.

PubMed

AlDahoul, Nouar; Md Sabri, Aznul Qalid; Mansoor, Ali Mohammed

2018-01-01

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM's training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).

Forming impressions of facial attractiveness is mandatory.

PubMed

Ritchie, Kay L; Palermo, Romina; Rhodes, Gillian

2017-03-28

First impressions of social traits, such as attractiveness, from faces are often claimed to be made automatically, given their speed and reliability. However, speed of processing is only one aspect of automaticity. Here we address a further aspect, asking whether impression formation is mandatory. Mandatory formation requires that impressions are formed about social traits even when this is task-irrelevant, and that once formed, these impressions are difficult to inhibit. In two experiments, participants learned what new people looked like for the purpose of future identification, from sets of images high or low in attractiveness. They then rated middle-attractiveness images of each person, for attractiveness. Even though instructed to rate the specific images, not the people, their ratings were biased by the attractiveness of the learned images. A third control experiment, with participants rating names, demonstrated that participants in Experiments 1 and 2 were not simply rating the people, rather than the specific images as instructed. These results show that the formation of attractiveness impressions from faces is mandatory, thus broadening the evidence for automaticity of facial impressions. The mandatory formation of impressions is likely to have an important impact in real-world situations such as online dating sites.

Applications of fuzzy logic to control and decision making

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Jani, Yashvant

1991-01-01

Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.

Status of NASA High-Speed Research Program

NASA Technical Reports Server (NTRS)

Whitehead, Allen H., Jr.

1998-01-01

This paper provides an overview of the NASA High-Speed Research (HSR) Program dedicated to establishing the technology foundation to support the US transport industry's decision for an environmentally acceptable, economically viable 300 passenger, 5000 n.mi., Mach 2.4 aircraft. The HSR program, begun in 1990, is supported by a team of US aerospace companies. The international economic stakes are high. The projected market for more than 500 High-Speed Civil Transport (HSCT) airplanes introduced between the years 2000 and 2015 translates to more than $200 billion in aircraft sales, and the potential of 140,000 new jobs. The paper addresses the history of supersonic commercial air transportation beginning with the Concorde and TU-144 developments in the early 1960 time period. The technology goals for the HSR program are derived from market study results, projections on environmental requirements, and technical goals for each discipline area referenced to the design and operational features of the Concorde. Progress since the inception of the program is reviewed and a summary of some of the lessons learned will be highlighted. An outline is presented of the remaining technological challenges. Emphasis in this paper will be on the traditional aeronautical technologies that lead to higher performance to ensure economic viability. Specific discussion will center around aerodynamic performance, flight deck research, materials and structures development and propulsion systems. The environmental barriers to the HSCT and that part of the HSR program that addresses those technologies are reviewed and assessed in a companion paper.

What Do High-Resolution EIT Waves Tell Us About CMEs?

NASA Technical Reports Server (NTRS)

Thompson, Barbara

2010-01-01

Although many studies have demonstrated that some coronal waves are not generated by corona) mass ejections, we have learned a great deal about the ability of corona) mass ejections to drive large-scale corona) waves, also called "EIT waves." We present new results based on EIT wave amplitude, timing, speed, and direction of propagation, with respect to their correlation with CME-related dimmings, speeds, locations and widths. Furthermore, we demonstrate the ability to correlate different aspects of EIT waves with some of the observed structure of CMEs observed in coronagraph data. Finally, we expand on the discussion of the types of wave modes that can be generated by a corona) mass ejection, and how these observations can serve as a diagnostic of the type of impulse a CME can deliver to the surrounding corona. These diagnostics are obtained by examining the motion of individual field lines, requiring high-resolution observations like those provided by TRACE and SDO/AIA.

Male bumblebees, Bombus terrestris, perform equally well as workers in a serial colour-learning task

PubMed Central

Wolf, Stephan; Chittka, Lars

2016-01-01

The learning capacities of males and females may differ with sex-specific behavioural requirements. Bumblebees provide a useful model system to explore how different lifestyles are reflected in learning abilities, because their (female but sterile) workers and males engage in fundamentally different behaviour routines. Bumblebee males, like workers, embark on active flower foraging but in contrast to workers they have to trade off their feeding with mate search, potentially affecting their abilities to learn and utilize floral cues efficiently during foraging. We used a serial colour-learning task with freely flying males and workers to compare their ability to flexibly learn visual floral cues with reward in a foraging scenario that changed over time. Male bumblebees did not differ from workers in both their learning speed and their ability to overcome previously acquired associations, when these ceased to predict reward. In all foraging tasks we found a significant improvement in choice accuracy in both sexes over the course of the training. In both sexes, the characteristics of the foraging performance depended largely on the colour difference of the two presented feeder types. Large colour distances entailed fast and reliable learning of the rewarding feeders whereas choice accuracy on highly similar colours improved significantly more slowly. Conversely, switching from a learned feeder type to a novel one was fastest for similar feeder colours and slow for highly different ones. Overall, we show that behavioural sex dimorphism in bumblebees did not affect their learning abilities beyond the mating context. We discuss the possible drivers and limitations shaping the foraging abilities of males and workers and implications for pollination ecology. We also suggest stingless male bumblebees as an advantageous alternative model system for the study of pollinator cognition. PMID:26877542

Blended-Wing-Body Low-Speed Flight Dynamics: Summary of Ground Tests and Sample Results

NASA Technical Reports Server (NTRS)

Vicroy, Dan D.

2009-01-01

A series of low-speed wind tunnel tests of a Blended-Wing-Body tri-jet configuration to evaluate the low-speed static and dynamic stability and control characteristics over the full envelope of angle of attack and sideslip are summarized. These data were collected for use in simulation studies of the edge-of-the-envelope and potential out-of-control flight characteristics. Some selected results with lessons learned are presented.

Reading Aloud and Solving Simple Arithmetic Calculation Intervention (Learning Therapy) Improves Inhibition, Verbal Episodic Memory, Focus Attention and Processing Speed in Healthy Elderly People: Evidence from a Randomized Controlled Trial

PubMed Central

Nouchi, Rui; Taki, Yasuyuki; Takeuchi, Hikaru; Nozawa, Takayuki; Sekiguchi, Atsushi; Kawashima, Ryuta

2016-01-01

Background: Previous reports have described that simple cognitive training using reading aloud and solving simple arithmetic calculations, so-called “learning therapy”, can improve executive functions and processing speed in the older adults. Nevertheless, it is not well-known whether learning therapy improve a wide range of cognitive functions or not. We investigated the beneficial effects of learning therapy on various cognitive functions in healthy older adults. Methods: We used a single-blinded intervention with two groups (learning therapy group: LT and waiting list control group: WL). Sixty-four elderly were randomly assigned to LT or WL. In LT, participants performed reading Japanese aloud and solving simple calculations training tasks for 6 months. WL did not participate in the intervention. We measured several cognitive functions before and after 6 months intervention periods. Results: Compared to WL, results revealed that LT improved inhibition performance in executive functions (Stroop: LT (Mean = 3.88) vs. WL (Mean = 1.22), adjusted p = 0.013 and reverse Stroop LT (Mean = 3.22) vs. WL (Mean = 1.59), adjusted p = 0.015), verbal episodic memory (Logical Memory (LM): LT (Mean = 4.59) vs. WL (Mean = 2.47), adjusted p = 0.015), focus attention (D-CAT: LT (Mean = 2.09) vs. WL (Mean = −0.59), adjusted p = 0.010) and processing speed compared to the WL control group (digit symbol coding: LT (Mean = 5.00) vs. WL (Mean = 1.13), adjusted p = 0.015 and Symbol Search (SS): LT (Mean = 3.47) vs. WL (Mean = 1.81), adjusted p = 0.014). Discussion: This randomized controlled trial (RCT) can be showed the benefit of LT on inhibition of executive functions, verbal episodic memory, focus attention and processing speed in healthy elderly people. Our results were discussed under overlapping hypothesis. PMID:27242481

Reading Aloud and Solving Simple Arithmetic Calculation Intervention (Learning Therapy) Improves Inhibition, Verbal Episodic Memory, Focus Attention and Processing Speed in Healthy Elderly People: Evidence from a Randomized Controlled Trial.

PubMed

Nouchi, Rui; Taki, Yasuyuki; Takeuchi, Hikaru; Nozawa, Takayuki; Sekiguchi, Atsushi; Kawashima, Ryuta

2016-01-01

Previous reports have described that simple cognitive training using reading aloud and solving simple arithmetic calculations, so-called "learning therapy", can improve executive functions and processing speed in the older adults. Nevertheless, it is not well-known whether learning therapy improve a wide range of cognitive functions or not. We investigated the beneficial effects of learning therapy on various cognitive functions in healthy older adults. We used a single-blinded intervention with two groups (learning therapy group: LT and waiting list control group: WL). Sixty-four elderly were randomly assigned to LT or WL. In LT, participants performed reading Japanese aloud and solving simple calculations training tasks for 6 months. WL did not participate in the intervention. We measured several cognitive functions before and after 6 months intervention periods. Compared to WL, results revealed that LT improved inhibition performance in executive functions (Stroop: LT (Mean = 3.88) vs. WL (Mean = 1.22), adjusted p = 0.013 and reverse Stroop LT (Mean = 3.22) vs. WL (Mean = 1.59), adjusted p = 0.015), verbal episodic memory (Logical Memory (LM): LT (Mean = 4.59) vs. WL (Mean = 2.47), adjusted p = 0.015), focus attention (D-CAT: LT (Mean = 2.09) vs. WL (Mean = -0.59), adjusted p = 0.010) and processing speed compared to the WL control group (digit symbol coding: LT (Mean = 5.00) vs. WL (Mean = 1.13), adjusted p = 0.015 and Symbol Search (SS): LT (Mean = 3.47) vs. WL (Mean = 1.81), adjusted p = 0.014). This randomized controlled trial (RCT) can be showed the benefit of LT on inhibition of executive functions, verbal episodic memory, focus attention and processing speed in healthy elderly people. Our results were discussed under overlapping hypothesis.

A Single Bout of Moderate Aerobic Exercise Improves Motor Skill Acquisition.

PubMed

Statton, Matthew A; Encarnacion, Marysol; Celnik, Pablo; Bastian, Amy J

2015-01-01

Long-term exercise is associated with improved performance on a variety of cognitive tasks including attention, executive function, and long-term memory. Remarkably, recent studies have shown that even a single bout of aerobic exercise can lead to immediate improvements in declarative learning and memory, but less is known about the effect of exercise on motor learning. Here we sought to determine the effect of a single bout of moderate intensity aerobic exercise on motor skill learning. In experiment 1, we investigated the effect of moderate aerobic exercise on motor acquisition. 24 young, healthy adults performed a motor learning task either immediately after 30 minutes of moderate intensity running, after running followed by a long rest period, or after slow walking. Motor skill was assessed via a speed-accuracy tradeoff function to determine how exercise might differentially affect two distinct components of motor learning performance: movement speed and accuracy. In experiment 2, we investigated both acquisition and retention of motor skill across multiple days of training. 20 additional participants performed either a bout of running or slow walking immediately before motor learning on three consecutive days, and only motor learning (no exercise) on a fourth day. We found that moderate intensity running led to an immediate improvement in motor acquisition for both a single session and on multiple sessions across subsequent days, but had no effect on between-day retention. This effect was driven by improved movement accuracy, as opposed to speed. However, the benefit of exercise was dependent upon motor learning occurring immediately after exercise-resting for a period of one hour after exercise diminished the effect. These results demonstrate that moderate intensity exercise can prime the nervous system for the acquisition of new motor skills, and suggest that similar exercise protocols may be effective in improving the outcomes of movement rehabilitation programs.

A Single Bout of Moderate Aerobic Exercise Improves Motor Skill Acquisition

PubMed Central

Statton, Matthew A.; Encarnacion, Marysol; Celnik, Pablo; Bastian, Amy J.

2015-01-01

Long-term exercise is associated with improved performance on a variety of cognitive tasks including attention, executive function, and long-term memory. Remarkably, recent studies have shown that even a single bout of aerobic exercise can lead to immediate improvements in declarative learning and memory, but less is known about the effect of exercise on motor learning. Here we sought to determine the effect of a single bout of moderate intensity aerobic exercise on motor skill learning. In experiment 1, we investigated the effect of moderate aerobic exercise on motor acquisition. 24 young, healthy adults performed a motor learning task either immediately after 30 minutes of moderate intensity running, after running followed by a long rest period, or after slow walking. Motor skill was assessed via a speed-accuracy tradeoff function to determine how exercise might differentially affect two distinct components of motor learning performance: movement speed and accuracy. In experiment 2, we investigated both acquisition and retention of motor skill across multiple days of training. 20 additional participants performed either a bout of running or slow walking immediately before motor learning on three consecutive days, and only motor learning (no exercise) on a fourth day. We found that moderate intensity running led to an immediate improvement in motor acquisition for both a single session and on multiple sessions across subsequent days, but had no effect on between-day retention. This effect was driven by improved movement accuracy, as opposed to speed. However, the benefit of exercise was dependent upon motor learning occurring immediately after exercise–resting for a period of one hour after exercise diminished the effect. These results demonstrate that moderate intensity exercise can prime the nervous system for the acquisition of new motor skills, and suggest that similar exercise protocols may be effective in improving the outcomes of movement rehabilitation programs. PMID:26506413

Which neuropsychological functions predict various processing speed components in children with and without attention-deficit/hyperactivity disorder?

PubMed

Vadnais, Sarah A; Kibby, Michelle Y; Jagger-Rickels, Audreyana C

2018-01-01

We identified statistical predictors of four processing speed (PS) components in a sample of 151 children with and without attention-deficit/hyperactivity disorder (ADHD). Performance on perceptual speed was predicted by visual attention/short-term memory, whereas incidental learning/psychomotor speed was predicted by verbal working memory. Rapid naming was predictive of each PS component assessed, and inhibition predicted all but one task, suggesting a shared need to identify/retrieve stimuli rapidly and inhibit incorrect responding across PS components. Hence, we found both shared and unique predictors of perceptual, cognitive, and output speed, suggesting more specific terminology should be used in future research on PS in ADHD.

A machine learning approach for gait speed estimation using skin-mounted wearable sensors: From healthy controls to individuals with multiple sclerosis.

PubMed

McGinnis, Ryan S; Mahadevan, Nikhil; Moon, Yaejin; Seagers, Kirsten; Sheth, Nirav; Wright, John A; DiCristofaro, Steven; Silva, Ikaro; Jortberg, Elise; Ceruolo, Melissa; Pindado, Jesus A; Sosnoff, Jacob; Ghaffari, Roozbeh; Patel, Shyamal

2017-01-01

Gait speed is a powerful clinical marker for mobility impairment in patients suffering from neurological disorders. However, assessment of gait speed in coordination with delivery of comprehensive care is usually constrained to clinical environments and is often limited due to mounting demands on the availability of trained clinical staff. These limitations in assessment design could give rise to poor ecological validity and limited ability to tailor interventions to individual patients. Recent advances in wearable sensor technologies have fostered the development of new methods for monitoring parameters that characterize mobility impairment, such as gait speed, outside the clinic, and therefore address many of the limitations associated with clinical assessments. However, these methods are often validated using normal gait patterns; and extending their utility to subjects with gait impairments continues to be a challenge. In this paper, we present a machine learning method for estimating gait speed using a configurable array of skin-mounted, conformal accelerometers. We establish the accuracy of this technique on treadmill walking data from subjects with normal gait patterns and subjects with multiple sclerosis-induced gait impairments. For subjects with normal gait, the best performing model systematically overestimates speed by only 0.01 m/s, detects changes in speed to within less than 1%, and achieves a root-mean-square-error of 0.12 m/s. Extending these models trained on normal gait to subjects with gait impairments yields only minor changes in model performance. For example, for subjects with gait impairments, the best performing model systematically overestimates speed by 0.01 m/s, quantifies changes in speed to within 1%, and achieves a root-mean-square-error of 0.14 m/s. Additional analyses demonstrate that there is no correlation between gait speed estimation error and impairment severity, and that the estimated speeds maintain the clinical significance of ground truth speed in this population. These results support the use of wearable accelerometer arrays for estimating walking speed in normal subjects and their extension to MS patient cohorts with gait impairment.

Does the Podcast Video Playback Speed Affect Comprehension for Novel Curriculum Delivery? A Randomized Trial.

PubMed

Song, Kristine; Chakraborty, Amit; Dawson, Matthew; Dugan, Adam; Adkins, Brian; Doty, Christopher

2018-01-01

Medical education is a rapidly evolving field that has been using new technology to improve how medical students learn. One of the recent implementations in medical education is the recording of lectures for the purpose of playback at various speeds. Though previous studies done via surveys have shown a subjective increase in the rate of knowledge acquisition when learning from sped-up lectures, no quantitative studies have measured information retention. The purpose of this study was to compare mean test scores on written assessments to objectively determine if watching a video of a recorded lecture at 1.5× speed was significantly different than 1.0× speed for the immediate retention of novel material. Fifty-four University of Kentucky medical students volunteered to participate in this study. The subjects were divided into two separate groups: Group A and Group B. Each group watched two separate videos, the first at 1.5× speed and the second at 1.0× speed, then completed assessments following each. The topics of the two videos were ultrasonography artifacts and transducers. Group A watched the artifacts video first at 1.5× speed followed by the transducers video at 1.0× speed. Group B watched the transducers video first at 1.5× speed followed by the artifacts video at 1.0× speed. The percentage correct on the written assessment were calculated for each subject at each video speed. The mean and standard deviation were also calculated using a t-test to determine if there was a significant difference in assessment scores between 1.5× and 1.0× speeds. There was a significant (p=0.0188) detriment in performance on the artifacts quiz at 1.5× speed (mean 61.4; 95% confidence interval [CI]-53.9, 68.9) compared to the control group at normal speed (mean 72.7; 95% CI-66.8, 78.6). On the transducers assessment, there was not a significant (p=0.1365) difference in performance in the 1.5× speed group (mean 66.9; CI- 59.8, 74.0) compared to the control group (mean 73.8; CI- 67.7, 79.8). These findings suggest that, unlike previously published studies that showed subjective improvement in performance with sped-up video-recorded lectures compared to normal speed, objective performance may be worse.

Modelling Rate for Change of Speed in Calculus Proposal of Inductive Inquiry

ERIC Educational Resources Information Center

Sokolowski, Andrzej

2014-01-01

Research has shown that students have difficulties with understanding the process of determining whether an object is speeding up or slowing down, especially when it is applied to the analysis of motion in the negative direction. As inductively organized learning through its scaffolding sequencing supports the process of knowledge acquisition…

Effects of First-Grade Number Knowledge Tutoring With Contrasting Forms of Practice.

PubMed

Fuchs, Lynn S; Geary, David C; Compton, Donald L; Fuchs, Douglas; Schatschneider, Christopher; Hamlett, Carol L; Deselms, Jacqueline; Seethaler, Pamela M; Wilson, Julie; Craddock, Caitlin F; Bryant, Joan D; Luther, Kurstin; Changas, Paul

2013-01-01

The purpose of this study was to investigate the effects of 1st-grade number knowledge tutoring with contrasting forms of practice. Tutoring occurred 3 times per week for 16 weeks. In each 30-min session, the major emphasis (25 min) was number knowledge; the other 5 min provided practice in 1 of 2 forms. Nonspeeded practice reinforced relations and principles addressed in number knowledge tutoring. Speeded practice promoted quick responding and use of efficient counting procedures to generate many correct responses. At-risk students were randomly assigned to number knowledge tutoring with speeded practice ( n = 195), number knowledge tutoring with nonspeeded practice ( n = 190), and control (no tutoring, n = 206). Each tutoring condition produced stronger learning than control on all 4 mathematics outcomes. Speeded practice produced stronger learning than nonspeeded practice on arithmetic and 2-digit calculations, but effects were comparable on number knowledge and word problems. Effects of both practice conditions on arithmetic were partially mediated by increased reliance on retrieval, but only speeded practice helped at-risk children compensate for weak reasoning ability.

Effects of First-Grade Number Knowledge Tutoring With Contrasting Forms of Practice

PubMed Central

Fuchs, Lynn S.; Geary, David C.; Compton, Donald L.; Fuchs, Douglas; Schatschneider, Christopher; Hamlett, Carol L.; DeSelms, Jacqueline; Seethaler, Pamela M.; Wilson, Julie; Craddock, Caitlin F.; Bryant, Joan D.; Luther, Kurstin; Changas, Paul

2013-01-01

The purpose of this study was to investigate the effects of 1st-grade number knowledge tutoring with contrasting forms of practice. Tutoring occurred 3 times per week for 16 weeks. In each 30-min session, the major emphasis (25 min) was number knowledge; the other 5 min provided practice in 1 of 2 forms. Nonspeeded practice reinforced relations and principles addressed in number knowledge tutoring. Speeded practice promoted quick responding and use of efficient counting procedures to generate many correct responses. At-risk students were randomly assigned to number knowledge tutoring with speeded practice (n = 195), number knowledge tutoring with nonspeeded practice (n = 190), and control (no tutoring, n = 206). Each tutoring condition produced stronger learning than control on all 4 mathematics outcomes. Speeded practice produced stronger learning than nonspeeded practice on arithmetic and 2-digit calculations, but effects were comparable on number knowledge and word problems. Effects of both practice conditions on arithmetic were partially mediated by increased reliance on retrieval, but only speeded practice helped at-risk children compensate for weak reasoning ability. PMID:24065865

Speed tracking control of pneumatic motor servo systems using observation-based adaptive dynamic sliding-mode control

NASA Astrophysics Data System (ADS)

Chen, Syuan-Yi; Gong, Sheng-Sian

2017-09-01

This study aims to develop an adaptive high-precision control system for controlling the speed of a vane-type air motor (VAM) pneumatic servo system. In practice, the rotor speed of a VAM depends on the input mass air flow, which can be controlled by the effective orifice area (EOA) of an electronic throttle valve (ETV). As the control variable of a second-order pneumatic system is the integral of the EOA, an observation-based adaptive dynamic sliding-mode control (ADSMC) system is proposed to derive the differential of the control variable, namely, the EOA control signal. In the ADSMC system, a proportional-integral-derivative fuzzy neural network (PIDFNN) observer is used to achieve an ideal dynamic sliding-mode control (DSMC), and a supervisor compensator is designed to eliminate the approximation error. As a result, the ADSMC incorporates the robustness of a DSMC and the online learning ability of a PIDFNN. To ensure the convergence of the tracking error, a Lyapunov-based analytical method is employed to obtain the adaptive algorithms required to tune the control parameters of the online ADSMC system. Finally, our experimental results demonstrate the precision and robustness of the ADSMC system for highly nonlinear and time-varying VAM pneumatic servo systems.

Cognitive and psychomotor effects of risperidone in schizophrenia and schizoaffective disorder.

PubMed

Houthoofd, Sofie A M K; Morrens, Manuel; Sabbe, Bernard G C

2008-09-01

The aim of this review was to discuss data from double-blind, randomized controlled trials (RCTs) that have investigated the effects of oral and long-acting injectable risperidone on cognitive and psychomotor functioning in patients with schizophrenia or schizoaffective disorder. PubMed/MEDLINE and the Institute of Scientific Information Web of Science database were searched for relevant English-language double-blind RCTs published between March 2000 and July 2008, using the terms schizophrenia, schizoaffective disorder, cognition, risperidone, psychomotor, processing speed, attention, vigilance, working memory, verbal learning, visual learning, reasoning, problem solving, social cognition, MATRICS, and long-acting. Relevant studies included patients with schizophrenia or schizoaffective disorder. Cognitive domains were delineated at the Consensus Conferences of the National Institute of Mental Health-Measurement And Treatment Research to Improve Cognition in Schizophrenia (NIMH-MATRICS). The tests employed to assess each domain and psychomotor functioning, and the within-group and between-group comparisons of risperidone with haloperidol and other atypical antipsychotics, are presented. The results of individual tests were included when they were individually presented and interpretable for either drug; outcomes that were presented as cluster scores or factor structures were excluded. A total of 12 articles were included in this review. Results suggested that the use of oral risperidone appeared to be associated with within-group improvements on the cognitive domains of processing speed, attention/vigilance, verbal and visual learning and memory, and reasoning and problem solving in patients with schizophrenia or schizoaffective disorder. Risperidone and haloperidol seemed to generate similar beneficial effects (on the domains of processing speed, attention/vigilance, [verbal and nonverbal] working memory, and visual learning and memory, as well as psychomotor functioning), although the results for verbal fluency, verbal learning and memory, and reasoning and problem solving were not unanimous, and no comparative data on social cognition were available. Similar cognitive effects were found with risperidone, olanzapine, and quetiapine on the domains of verbal working memory and reasoning and problem solving, as well as verbal fluency. More research is needed on the domains in which study results were contradictory. For olanzapine versus risperidone, these were verbal and visual learning and memory and psychomotor functioning. No comparative data for olanzapine and risperidone were available for the social cognition domain. For quetiapine versus risperidone, the domains in which no unanimity was found were processing speed, attention/vigilance, nonverbal working memory, and verbal learning and memory. The limited available reports on risperidone versus clozapine suggest that: risperidone was associated with improved, and clozapine with worsened, performance on the nonverbal working memory domain; risperidone improved and clozapine did not improve reasoning and problem-solving performance; clozapine improved, and risperidone did not improve, social cognition performance. Use of long-acting injectable risperidone seemed to be associated with improved performance in the domains of attention/vigilance, verbal learning and memory, and reasoning and problem solving, as well as psychomotor functioning. The results for the nonverbal working memory domain were indeterminate, and no clear improvement was seen in the social cognition domain. The domains of processing speed, verbal working memory, and visual learning and memory, as well as verbal fluency, were not assessed. The results of this review of within-group comparisons of oral risperidone suggest that the agent appeared to be associated with improved functioning in the cognitive domains of processing speed, attention/vigilance, verbal and visual learning and memory, and reasoning and problem solving in patients with schizophrenia or schizoaffective disorder. Long-acting injectable risperidone seemed to be associated with improved functioning in the domains of attention/vigilance, verbal learning and memory, and reasoning and problem solving, as well as psychomotor functioning, in patients with schizophrenia or schizoaffective disorder.

Explicit instruction of rules interferes with visuomotor skill transfer.

PubMed

Tanaka, Kanji; Watanabe, Katsumi

2017-06-01

In the present study, we examined the effects of explicit knowledge, obtained through instruction or spontaneous detection, on the transfer of visuomotor sequence learning. In the learning session, participants learned a visuomotor sequence, via trial and error. In the transfer session, the order of the sequence was reversed from that of the learning session. Before the commencement of the transfer session, some participants received explicit instruction regarding the reversal rule (i.e., Instruction group), while the others did not receive any information and were sorted into either an Aware or Unaware group, as assessed by interview conducted after the transfer session. Participants in the Instruction and Aware groups performed with fewer errors than the Unaware group in the transfer session. The participants in the Instruction group showed slower speed than the Aware and Unaware groups in the transfer session, and the sluggishness likely persisted even in late learning. These results suggest that explicit knowledge reduces errors in visuomotor skill transfer, but may interfere with performance speed, particularly when explicit knowledge is provided, as opposed to being spontaneously discovered.

Oral Braille Reading Decoding Strategies of Middle School Students Who Are Blind or Have Low Vision

ERIC Educational Resources Information Center

Nannemann, Allison C.; Bruce, Susan M.; Hussey, Colleen; Vercollone, Becky S.; McCarthy, Mary

2017-01-01

Students who are visually impaired may face unique literacy challenges as they learn to read and write braille. One such challenge relates to slower reading speeds for students who read braille as compared to those who read print. In addition to learning letters, sounds, grammar, and spelling, braille readers must learn contractions and…

A Distance Learning Review--The Communicational Module "Learning on Demand--Anywhere at Any Time"

ERIC Educational Resources Information Center

Tatkovic, Nevenka; Ruzic, Maja

2004-01-01

The society of knowledge refers to the society marked with the principle which requires that knowledge, information and life-time learning hold a key to success in the world of IT technology. Internet, World Wide Web, Web Based Education and ever so growing speed of IT and communicational technologies have enabled the application of new modes,…

Sleep-dependent learning and motor-skill complexity

PubMed Central

Kuriyama, Kenichi; Stickgold, Robert; Walker, Matthew P.

2004-01-01

Learning of a procedural motor-skill task is known to progress through a series of unique memory stages. Performance initially improves during training, and continues to improve, without further rehearsal, across subsequent periods of sleep. Here, we investigate how this delayed sleep-dependent learning is affected when the task characteristics are varied across several degrees of difficulty, and whether this improvement differentially enhances individual transitions of the motor-sequence pattern being learned. We report that subjects show similar overnight improvements in speed whether learning a five-element unimanual sequence (17.7% improvement), a nine-element unimanual sequence (20.2%), or a five-element bimanual sequence (17.5%), but show markedly increased overnight improvement (28.9%) with a nine-element bimanual sequence. In addition, individual transitions within the motor-sequence pattern that appeared most difficult at the end of training showed a significant 17.8% increase in speed overnight, whereas those transitions that were performed most rapidly at the end of training showed only a non-significant 1.4% improvement. Together, these findings suggest that the sleep-dependent learning process selectively provides maximum benefit to motor-skill procedures that proved to be most difficult prior to sleep. PMID:15576888

Design and Development of the Blackbird: Challenges and Lessons Learned

NASA Technical Reports Server (NTRS)

Merlin, Peter W.

2009-01-01

The Lockheed Blackbirds hold a unique place in the development of aeronautics. In their day, the A-12, YF-12, M-21, D-21, and SR-71 variants outperformed all other jet airplanes in terms of altitude and speed. Now retired, they remain the only production aircraft capable of sustained Mach 3 cruise and operational altitudes above 80,000 feet. In this paper the author describes the design evolution of the Blackbird from Lockheed's early Archangel studies for the Central Intelligence Agency through Senior Crown, production of the Air Force's SR-71. He describes the construction and materials challenges faced by Lockheed, the Blackbird's performance characteristics and capabilities, and the National Aeronautics and Space Administration's role in using the aircraft as a flying laboratory to collect data on materials, structures, loads, heating, aerodynamics, and performance for high-speed aircraft.

Speech Motor Sequence Learning: Acquisition and Retention in Parkinson Disease and Normal Aging.

PubMed

Whitfield, Jason A; Goberman, Alexander M

2017-06-10

The aim of the current investigation was to examine speech motor sequence learning in neurologically healthy younger adults, neurologically healthy older adults, and individuals with Parkinson disease (PD) over a 2-day period. A sequential nonword repetition task was used to examine learning over 2 days. Participants practiced a sequence of 6 monosyllabic nonwords that was retested following nighttime sleep. The speed and accuracy of the nonword sequence were measured, and learning was inferred by examining performance within and between sessions. Though all groups exhibited comparable improvements of the nonword sequence performance during the initial session, between-session retention of the nonword sequence differed between groups. Younger adult controls exhibited offline gains, characterized by an increase in the speed and accuracy of nonword sequence performance across sessions, whereas older adults exhibited stable between-session performance. Individuals with PD exhibited offline losses, marked by an increase in sequence duration between sessions. The current results demonstrate that both PD and normal aging affect retention of speech motor learning. Furthermore, these data suggest that basal ganglia dysfunction associated with PD may affect the later stages of speech motor learning. Findings from the current investigation are discussed in relation to studies examining consolidation of nonspeech motor learning.

Study on the feasibility of provision of distance learning programmes in surgery to Malawi.

PubMed

Mains, Edward A A; Blackmur, James P; Dewhurst, David; Ward, Ross M; Garden, O James; Wigmore, Stephen J

2011-12-01

Medical educational opportunities and resources are considerably limited in the developing world. The expansion of computing and Internet access means that there exists a potential to provide education to students through distance learning programmes. This study investigated the feasibility of providing distance learning course in surgery in Malawi. The study investigated the user requirements, technical requirements and Internet connections in two teaching hospitals in Malawi. In addition the appropriateness of current course material from the Edinburgh Surgical Sciences Qualification to Malawi trainees was assessed. The study found a high degree of interest from Malawian trainees in distance learning. The provision of basic science modules such as anatomy and physiology and the ability to access journals were considered highly desirable. The current ESSQ course would require extensive re-modelling to make it suitable to an African trainee's requirements. Internet speeds remain slow and access is currently expensive. There is considerable interest in distance learning programmes in Malawi but access to them is limited partly because of slow and expensive Internet access. Understanding the needs of trainees in countries such as Malawi will allow better direction of educational aid and resources to support surgical training. Copyright © 2010 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

Does input influence uptake? Links between maternal talk, processing speed and vocabulary size in Spanish-learning children

PubMed Central

Hurtado, Nereyda; Marchman, Virginia A.; Fernald, Anne

2010-01-01

It is well established that variation in caregivers' speech is associated with language outcomes, yet little is known about the learning principles that mediate these effects. This longitudinal study (n = 27) explores whether Spanish-learning children's early experiences with language predict efficiency in real-time comprehension and vocabulary learning. Measures of mothers' speech at 18 months were examined in relation to children's speech processing efficiency and reported vocabulary at 18 and 24 months. Children of mothers who provided more input at 18 months knew more words and were faster in word recognition at 24 months. Moreover, multiple regression analyses indicated that the influences of caregiver speech on speed of word recognition and vocabulary were largely overlapping. This study provides the first evidence that input shapes children's lexical processing efficiency and that vocabulary growth and increasing facility in spoken word comprehension work together to support the uptake of the information that rich input affords the young language learner. PMID:19046145

Least Square Fast Learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler.

PubMed

Li, Guoqiang; Niu, Peifeng; Wang, Huaibao; Liu, Yongchao

2014-03-01

This paper presents a novel artificial neural network with a very fast learning speed, all of whose weights and biases are determined by the twice Least Square method, so it is called Least Square Fast Learning Network (LSFLN). In addition, there is another difference from conventional neural networks, which is that the output neurons of LSFLN not only receive the information from the hidden layer neurons, but also receive the external information itself directly from the input neurons. In order to test the validity of LSFLN, it is applied to 6 classical regression applications, and also employed to build the functional relation between the combustion efficiency and operating parameters of a 300WM coal-fired boiler. Experimental results show that, compared with other methods, LSFLN with very less hidden neurons could achieve much better regression precision and generalization ability at a much faster learning speed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Students’ Spatial Ability through Open-Ended Approach Aided by Cabri 3D

NASA Astrophysics Data System (ADS)

Priatna, N.

2017-09-01

The use of computer software such as Cabri 3D for learning activities is very unlimited. Students can adjust their learning speed according to their level of ability. Open-ended approach strongly supports the use of computer software in learning, because the goal of open-ended learning is to help developing creative activities and mathematical mindset of students through problem solving simultaneously. In other words, creative activities and mathematical mindset of students should be developed as much as possible in accordance with the ability of spatial ability of each student. Spatial ability is the ability of students in constructing and representing geometry models. This study aims to determine the improvement of spatial ability of junior high school students who obtained learning with open-ended approach aided by Cabri 3D. It adopted a quasi-experimental method with the non-randomized control group pretest-posttest design and the 2×3 factorial model. The instrument of the study is spatial ability test. Based on analysis of the data, it is found that the improvement of spatial ability of students who received open-ended learning aided by Cabri 3D was greater than students who received expository learning, both as a whole and based on the categories of students’ initial mathematical ability.

Efficient Learning for the Poor: New Insights into Literacy Acquisition for Children

NASA Astrophysics Data System (ADS)

Abadzi, Helen

2008-11-01

Reading depends on the speed of visual recognition and capacity of short-term memory. To understand a sentence, the mind must read it fast enough to capture it within the limits of the short-term memory. This means that children must attain a minimum speed of fairly accurate reading to understand a passage. Learning to read involves "tricking" the brain into perceiving groups of letters as coherent words. This is achieved most efficiently by pairing small units consistently with sounds rather than learning entire words. To link the letters with sounds, explicit and extensive practice is needed; the more complex the spelling of a language, the more practice is necessary. However, schools of low-income students often waste instructional time and lack reading resources, so students cannot get sufficient practice to automatize reading and may remain illiterate for years. Lack of reading fluency in the early grades creates inefficiencies that affect the entire educational system. Neurocognitive research on reading points to benchmarks and monitoring indicators. All students should attain reading speeds of 45-60 words per minute by the end of grade 2 and 120-150 words per minute for grades 6-8.

Effectiveness of a self-regulated remedial program for handwriting difficulties.

PubMed

Van Waelvelde, Hilde; De Roubaix, Amy; Steppe, Lien; Troubleyn, Evy; De Mey, Barbara; Dewitte, Griet; Debrabant, Julie; Van de Velde, Dominique

2017-09-01

Handwriting difficulties may have pervasive effects on a child's school performance. I Can! is a remedial handwriting program with a focus on self-regulated learning and applying motor learning principles combined with a behavioural approach. It is developed for typically developing children with handwriting problems. The study aim was to evaluate the program's effectiveness. Thirty-one children aged 7-8 year participated in a cross-over study. Handwriting quality and speed were repeatedly assessed by means of the Systematic Screening of Handwriting Difficulties test. Difficulties addressed were fluency in letter formation, fluency in letter connections, letter height, regularity of letter height, space between words, and line path. Mixed model analysis revealed improved quality of writing and speed for all children but significantly more improvement in handwriting quality for the children participating in the program. Although writing speed improved over time, no additional effects of the program occurred. 'I Can!' is found to be an effective instructive program to ameliorate handwriting quality in typically developing children with handwriting difficulties. The program's success was by a therapy burst of only 7 weeks focusing on the child's self-regulated learning capacities, within an individualized education plan according to their needs and goals.

The development of the abilities to acquire novel detailed orthographic representations and maintain them in long-term memory.

PubMed

Binamé, Florence; Poncelet, Martine

2016-03-01

Previous studies have clearly demonstrated that the development of orthographic representations relies on phonological recoding. However, substantial questions persist about the remaining unexplained variance in the acquisition of word-specific orthographic knowledge that is still underspecified. The main aim of this study was to explore whether two cognitive factors-sensitivity to orthographic regularities and short-term memory (STM) for serial order-make independent contributions to the acquisition of novel orthographic representations beyond that of the phonological core component and the level of preexisting word-specific orthographic knowledge. To this end, we had children from second to sixth grades learn novel written word forms using a repeated spelling practice paradigm. The speed at which children learned the word forms and their long-term retention (1week and 1month later) were assessed. Hierarchical regression analyses revealed that phonological recoding, preexisting word-specific orthographic knowledge, and order STM explained a portion of the variance in orthographic learning speed, whereas phonological recoding, preexisting word-specific orthographic knowledge, and orthographic sensitivity each explained a portion of variance in the long-term retention of the newly created orthographic representations. A secondary aim of the study was to determine the developmental trajectory of the abilities to acquire novel orthographic word forms over the course of primary schooling. As expected, results showed an effect of age on both learning speed and long-term retention. The specific roles of orthographic sensitivity and order STM as independent factors involved in different steps of orthographic learning are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Motivational state, reward value, and Pavlovian cues differentially affect skilled forelimb grasping in rats

PubMed Central

de Clauser, Larissa; Kasper, Hansjörg; Schwab, Martin E.

2016-01-01

Motor skills represent high-precision movements performed at optimal speed and accuracy. Such motor skills are learned with practice over time. Besides practice, effects of motivation have also been shown to influence speed and accuracy of movements, suggesting that fast movements are performed to maximize gained reward over time as noted in previous studies. In rodents, skilled motor performance has been successfully modeled with the skilled grasping task, in which animals use their forepaw to grasp for sugar pellet rewards through a narrow window. Using sugar pellets, the skilled grasping task is inherently tied to motivation processes. In the present study, we performed three experiments modulating animals’ motivation during skilled grasping by changing the motivational state, presenting different reward value ratios, and displaying Pavlovian stimuli. We found in all three studies that motivation affected the speed of skilled grasping movements, with the strongest effects seen due to motivational state and reward value. Furthermore, accuracy of the movement, measured in success rate, showed a strong dependence on motivational state as well. Pavlovian cues had only minor effects on skilled grasping, but results indicate an inverse Pavlovian-instrumental transfer effect on movement speed. These findings have broad implications considering the increasing use of skilled grasping in studies of motor system structure, function, and recovery after injuries. PMID:27194796

Reduced asymmetry in motor skill learning in left-handed compared to right-handed individuals.

PubMed

McGrath, Robert L; Kantak, Shailesh S

2016-02-01

Hemispheric specialization for motor control influences how individuals perform and adapt to goal-directed movements. In contrast to adaptation, motor skill learning involves a process wherein one learns to synthesize novel movement capabilities in absence of perturbation such that they are performed with greater accuracy, consistency and efficiency. Here, we investigated manual asymmetry in acquisition and retention of a complex motor skill that requires speed and accuracy for optimal performance in right-handed and left-handed individuals. We further determined if degree of handedness influences motor skill learning. Ten right-handed (RH) and 10 left-handed (LH) adults practiced two distinct motor skills with their dominant or nondominant arms during separate sessions two-four weeks apart. Learning was quantified by changes in the speed-accuracy tradeoff function measured at baseline and one-day retention. Manual asymmetry was evident in the RH group but not the LH group. RH group demonstrated significantly greater skill improvement for their dominant-right hand than their nondominant-left hand. In contrast, for the LH group, both dominant and nondominant hands demonstrated comparable learning. Less strongly-LH individuals (lower EHI scores) exhibited more learning of their dominant hand. These results suggest that while hemispheric specialization influences motor skill learning, these effects may be influenced by handedness. Copyright © 2015 Elsevier B.V. All rights reserved.

On adaptive learning rate that guarantees convergence in feedforward networks.

PubMed

Behera, Laxmidhar; Kumar, Swagat; Patnaik, Awhan

2006-09-01

This paper investigates new learning algorithms (LF I and LF II) based on Lyapunov function for the training of feedforward neural networks. It is observed that such algorithms have interesting parallel with the popular backpropagation (BP) algorithm where the fixed learning rate is replaced by an adaptive learning rate computed using convergence theorem based on Lyapunov stability theory. LF II, a modified version of LF I, has been introduced with an aim to avoid local minima. This modification also helps in improving the convergence speed in some cases. Conditions for achieving global minimum for these kind of algorithms have been studied in detail. The performances of the proposed algorithms are compared with BP algorithm and extended Kalman filtering (EKF) on three bench-mark function approximation problems: XOR, 3-bit parity, and 8-3 encoder. The comparisons are made in terms of number of learning iterations and computational time required for convergence. It is found that the proposed algorithms (LF I and II) are much faster in convergence than other two algorithms to attain same accuracy. Finally, the comparison is made on a complex two-dimensional (2-D) Gabor function and effect of adaptive learning rate for faster convergence is verified. In a nutshell, the investigations made in this paper help us better understand the learning procedure of feedforward neural networks in terms of adaptive learning rate, convergence speed, and local minima.

Relationships between Fine-Motor, Visual-Motor, and Visual Perception Scores and Handwriting Legibility and Speed

ERIC Educational Resources Information Center

Klein, Sheryl; Guiltner, Val; Sollereder, Patti; Cui, Ying

2011-01-01

Occupational therapists assess fine motor, visual motor, visual perception, and visual skill development, but knowledge of the relationships between scores on sensorimotor performance measures and handwriting legibility and speed is limited. Ninety-nine students in grades three to six with learning and/or behavior problems completed the Upper-Limb…

Conservation Abilities, Visuospatial Skills, and Numerosity Processing Speed: Association with Math Achievement and Math Difficulties in Elementary School Children

ERIC Educational Resources Information Center

Lambert, Katharina; Spinath, Birgit

2018-01-01

The aim of the present study was to investigate the associations between elementary school children's mathematical achievement and their conservation abilities, visuospatial skills, and numerosity processing speed. We also assessed differences in these abilities between children with different types of learning problems. In Study 1 (N = 229), we…

Teaching the DIG Generation

ERIC Educational Resources Information Center

Renard, Lisa

2005-01-01

Instant digital communication is going to say and the wise teacher needs to acknowledge and keep pace with the technology that eases and speeds up the way the DIG (digital immediate gratification) generation learns. Some DIG- friendly strategies that teachers can employ to make learning more attractive and meaningful are presented.

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…

The Effects of Stimulus Presentation Rate on the Short-Term Memory of Learning Disabled Children.

ERIC Educational Resources Information Center

Tarver, Sara G.; Ellsworth, Patricia S.

To test the hypothesis that the developmental lag in verbal rehearsal which has been documented for the learning disabled is due to a naming speed deficit (i.e., slow retrieval of stimulus names), the serial recall performance of 64 learning disabled children at four grade levels (1, 3, 5, and 7) was compared under three stimulus presentation…

Ultrahigh- and high-speed photography, videography, and photonics '91; Proceedings of the Meeting, San Diego, CA, July 24-26, 1991

NASA Astrophysics Data System (ADS)

Jaanimagi, Paul A.

1992-01-01

This volume presents papers grouped under the topics on advances in streak and framing camera technology, applications of ultrahigh-speed photography, characterizing high-speed instrumentation, high-speed electronic imaging technology and applications, new technology for high-speed photography, high-speed imaging and photonics in detonics, and high-speed velocimetry. The papers presented include those on a subpicosecond X-ray streak camera, photocathodes for ultrasoft X-ray region, streak tube dynamic range, high-speed TV cameras for streak tube readout, femtosecond light-in-flight holography, and electrooptical systems characterization techniques. Attention is also given to high-speed electronic memory video recording techniques, high-speed IR imaging of repetitive events using a standard RS-170 imager, use of a CCD array as a medium-speed streak camera, the photography of shock waves in explosive crystals, a single-frame camera based on the type LD-S-10 intensifier tube, and jitter diagnosis for pico- and femtosecond sources.

Differences in energy expenditure during high-speed versus standard-speed yoga: A randomized sequence crossover trial.

PubMed

Potiaumpai, Melanie; Martins, Maria Carolina Massoni; Rodriguez, Roberto; Mooney, Kiersten; Signorile, Joseph F

2016-12-01

To compare energy expenditure and volume of oxygen consumption and carbon dioxide production during a high-speed yoga and a standard-speed yoga program. Randomized repeated measures controlled trial. A laboratory of neuromuscular research and active aging. Sun-Salutation B was performed, for eight minutes, at a high speed versus and a standard-speed separately while oxygen consumption was recorded. Caloric expenditure was calculated using volume of oxygen consumption and carbon dioxide production. Difference in energy expenditure (kcal) of HSY and SSY. Significant differences were observed in energy expenditure between yoga speeds with high-speed yoga producing significantly higher energy expenditure than standard-speed yoga (MD=18.55, SE=1.86, p<0.01). Significant differences were also seen between high-speed and standard-speed yoga for volume of oxygen consumed and carbon dioxide produced. High-speed yoga results in a significantly greater caloric expenditure than standard-speed yoga. High-speed yoga may be an effective alternative program for those targeting cardiometabolic markers. Copyright © 2016 Elsevier Ltd. All rights reserved.

Learning Optimized Local Difference Binaries for Scalable Augmented Reality on Mobile Devices.

PubMed

Xin Yang; Kwang-Ting Cheng

2014-06-01

The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile augmented reality (AR) system. However, existing descriptors are either too computationally expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly efficient, robust and distinctive binary descriptor, called Learning-based Local Difference Binary (LLDB). LLDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones with the goal of maximizing the Hamming distance between mismatches while minimizing it between matches. Experimental results demonstrate that LLDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Compared to the state-of-the-art binary descriptors, primarily designed for speed, LLDB has similar efficiency for descriptor construction, while achieving a greater accuracy and faster matching speed when matching over a large database with 2.3M descriptors on mobile devices.

Developing course lecture notes on high-speed rail.

DOT National Transportation Integrated Search

2017-07-15

1. Introduction a. World-wide Development of High-Speed Rail (Japan, Europe, China) b. High-speed Rail in the U.S. 2. High-Speed Rail Infrastructure a. Geometric Design of High Speed Rail i. Horizontal Curve ii. Vertical Curve iii. Grade and Turnout ...

Making long-term memories in minutes: a spaced learning pattern from memory research in education

PubMed Central

Kelley, Paul; Whatson, Terry

2013-01-01

Memory systems select from environmental stimuli those to encode permanently. Repeated stimuli separated by timed spaces without stimuli can initiate Long-Term Potentiation (LTP) and long-term memory (LTM) encoding. These processes occur in time scales of minutes, and have been demonstrated in many species. This study reports on using a specific timed pattern of three repeated stimuli separated by 10 min spaces drawn from both behavioral and laboratory studies of LTP and LTM encoding. A technique was developed based on this pattern to test whether encoding complex information into LTM in students was possible using the pattern within a very short time scale. In an educational context, stimuli were periods of highly compressed instruction, and spaces were created through 10 min distractor activities. Spaced Learning in this form was used as the only means of instruction for a national curriculum Biology course, and led to very rapid LTM encoding as measured by the high-stakes test for the course. Remarkably, learning at a greatly increased speed and in a pattern that included deliberate distraction produced significantly higher scores than random answers (p < 0.00001) and scores were not significantly different for experimental groups (one hour spaced learning) and control groups (four months teaching). Thus learning per hour of instruction, as measured by the test, was significantly higher for the spaced learning groups (p < 0.00001). In a third condition, spaced learning was used to replace the end of course review for one of two examinations. Results showed significantly higher outcomes for the course using spaced learning (p < 0.0005). The implications of these findings and further areas for research are briefly considered. PMID:24093012

Cognitive and Emotional Factors in Children with Mathematical Learning Disabilities

ERIC Educational Resources Information Center

Passolunghi, Maria Chiara

2011-01-01

Emotional and cognitive factors were examined in 18 children with mathematical learning disabilities (MLD), compared with 18 normally achieving children, matched for chronological age, school level, gender and verbal IQ. Working memory, short-term memory, inhibitory processes, speed of processing and level of anxiety in mathematics were assessed…

Suggestopedia: A New Way to Learn.

ERIC Educational Resources Information Center

Thomas, Elaine

Suggestopedia is a recent teaching technique which enables students to learn with impressive speed, little conscious effort, and a great deal of pleasure. Developed by Georgi Lozanov, suggestopedia is based on the assumption that a number of environmental, social, and psychological variables can be altered to make more effective use of students'…

Enhancing Extensive Reading with Data-Driven Learning

ERIC Educational Resources Information Center

Hadley, Gregory; Charles, Maggie

2017-01-01

This paper investigates using data-driven learning (DDL) as a means of stimulating greater lexicogrammatical knowledge and reading speed among lower proficiency learners in an extensive reading program. For 16 weekly 90-minute sessions, an experimental group (12 students) used DDL materials created from a corpus developed from the Oxford Bookworms…

WebLab of a DC Motor Speed Control Didactical Experiment

ERIC Educational Resources Information Center

Bauer, Karine; Mendes, Luciano

2012-01-01

Purpose: Weblabs are an additional resource in the execution of experiments in control engineering education, making learning process more flexible both in time, by allowing extra class laboratory activities, and space, bringing the learning experience to remote locations where experimentation facilities would not be available. The purpose of this…

Perceptual learning modules in mathematics: enhancing students' pattern recognition, structure extraction, and fluency.

PubMed

Kellman, Philip J; Massey, Christine M; Son, Ji Y

2010-04-01

Learning in educational settings emphasizes declarative and procedural knowledge. Studies of expertise, however, point to other crucial components of learning, especially improvements produced by experience in the extraction of information: perceptual learning (PL). We suggest that such improvements characterize both simple sensory and complex cognitive, even symbolic, tasks through common processes of discovery and selection. We apply these ideas in the form of perceptual learning modules (PLMs) to mathematics learning. We tested three PLMs, each emphasizing different aspects of complex task performance, in middle and high school mathematics. In the MultiRep PLM, practice in matching function information across multiple representations improved students' abilities to generate correct graphs and equations from word problems. In the Algebraic Transformations PLM, practice in seeing equation structure across transformations (but not solving equations) led to dramatic improvements in the speed of equation solving. In the Linear Measurement PLM, interactive trials involving extraction of information about units and lengths produced successful transfer to novel measurement problems and fraction problem solving. Taken together, these results suggest (a) that PL techniques have the potential to address crucial, neglected dimensions of learning, including discovery and fluent processing of relations; (b) PL effects apply even to complex tasks that involve symbolic processing; and (c) appropriately designed PL technology can produce rapid and enduring advances in learning. Copyright © 2009 Cognitive Science Society, Inc.

Broad-based visual benefits from training with an integrated perceptual-learning video game.

PubMed

Deveau, Jenni; Lovcik, Gary; Seitz, Aaron R

2014-06-01

Perception is the window through which we understand all information about our environment, and therefore deficits in perception due to disease, injury, stroke or aging can have significant negative impacts on individuals' lives. Research in the field of perceptual learning has demonstrated that vision can be improved in both normally seeing and visually impaired individuals, however, a limitation of most perceptual learning approaches is their emphasis on isolating particular mechanisms. In the current study, we adopted an integrative approach where the goal is not to achieve highly specific learning but instead to achieve general improvements to vision. We combined multiple perceptual learning approaches that have individually contributed to increasing the speed, magnitude and generality of learning into a perceptual-learning based video-game. Our results demonstrate broad-based benefits of vision in a healthy adult population. Transfer from the game includes; improvements in acuity (measured with self-paced standard eye-charts), improvement along the full contrast sensitivity function, and improvements in peripheral acuity and contrast thresholds. The use of this type of this custom video game framework built up from psychophysical approaches takes advantage of the benefits found from video game training while maintaining a tight link to psychophysical designs that enable understanding of mechanisms of perceptual learning and has great potential both as a scientific tool and as therapy to help improve vision. Copyright © 2014 Elsevier B.V. All rights reserved.

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.

PubMed

Zhang, Kai; Zuo, Wangmeng; Chen, Yunjin; Meng, Deyu; Zhang, Lei

2017-07-01

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.

Pulmonary emphysema classification based on an improved texton learning model by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryujiro; Kanematsu, Masayuki; Fujita, Hiroshi

2013-03-01

In this paper, we present a texture classification method based on texton learned via sparse representation (SR) with new feature histogram maps in the classification of emphysema. First, an overcomplete dictionary of textons is learned via KSVD learning on every class image patches in the training dataset. In this stage, high-pass filter is introduced to exclude patches in smooth area to speed up the dictionary learning process. Second, 3D joint-SR coefficients and intensity histograms of the test images are used for characterizing regions of interest (ROIs) instead of conventional feature histograms constructed from SR coefficients of the test images over the dictionary. Classification is then performed using a classifier with distance as a histogram dissimilarity measure. Four hundreds and seventy annotated ROIs extracted from 14 test subjects, including 6 paraseptal emphysema (PSE) subjects, 5 centrilobular emphysema (CLE) subjects and 3 panlobular emphysema (PLE) subjects, are used to evaluate the effectiveness and robustness of the proposed method. The proposed method is tested on 167 PSE, 240 CLE and 63 PLE ROIs consisting of mild, moderate and severe pulmonary emphysema. The accuracy of the proposed system is around 74%, 88% and 89% for PSE, CLE and PLE, respectively.

Magnetic characterization of the stator core of a high-speed motor made of an ultrathin electrical steel sheet using the magnetic property evaluation system

NASA Astrophysics Data System (ADS)

Oka, Mohachiro; Enokizono, Masato; Mori, Yuji; Yamazaki, Kazumasa

2018-04-01

Recently, the application areas for electric motors have been expanding. For instance, electric motors are used in new technologies such as rovers, drones, cars, and robots. The motor used in such machinery should be small, high-powered, highly-efficient, and high-speed. In such motors, loss at high-speed rotation must be especially minimal. Eddy-current loss in the stator core is known to increase greatly during loss at high-speed rotation of the motor. To produce an efficient high-speed motor, we are developing a stator core for a motor using an ultrathin electrical steel sheet with only a small amount of eddy-current loss. Furthermore, the magnetic property evaluation for efficient, high-speed motor stator cores that use conventional commercial frequency is insufficient. Thus, we made a new high-speed magnetic property evaluation system to evaluate the magnetic properties of the efficient high-speed motor stator core. This system was composed of high-speed A/D converters, D/A converters, and a high-speed power amplifier. In experiments, the ultrathin electrical steel sheet dramatically suppressed iron loss and, in particular, eddy-current loss. In addition, a new high-speed magnetic property evaluation system accurately evaluated the magnetic properties of the efficient high-speed motor stator core.

Does the Podcast Video Playback Speed Affect Comprehension for Novel Curriculum Delivery? A Randomized Trial

PubMed Central

Song, Kristine; Chakraborty, Amit; Dawson, Matthew; Dugan, Adam; Adkins, Brian; Doty, Christopher

2018-01-01

Introduction Medical education is a rapidly evolving field that has been using new technology to improve how medical students learn. One of the recent implementations in medical education is the recording of lectures for the purpose of playback at various speeds. Though previous studies done via surveys have shown a subjective increase in the rate of knowledge acquisition when learning from sped-up lectures, no quantitative studies have measured information retention. The purpose of this study was to compare mean test scores on written assessments to objectively determine if watching a video of a recorded lecture at 1.5× speed was significantly different than 1.0× speed for the immediate retention of novel material. Methods Fifty-four University of Kentucky medical students volunteered to participate in this study. The subjects were divided into two separate groups: Group A and Group B. Each group watched two separate videos, the first at 1.5× speed and the second at 1.0× speed, then completed assessments following each. The topics of the two videos were ultrasonography artifacts and transducers. Group A watched the artifacts video first at 1.5× speed followed by the transducers video at 1.0× speed. Group B watched the transducers video first at 1.5× speed followed by the artifacts video at 1.0× speed. The percentage correct on the written assessment were calculated for each subject at each video speed. The mean and standard deviation were also calculated using a t-test to determine if there was a significant difference in assessment scores between 1.5× and 1.0× speeds. Results There was a significant (p=0.0188) detriment in performance on the artifacts quiz at 1.5× speed (mean 61.4; 95% confidence interval [CI]-53.9, 68.9) compared to the control group at normal speed (mean 72.7; 95% CI−66.8, 78.6). On the transducers assessment, there was not a significant (p=0.1365) difference in performance in the 1.5× speed group (mean 66.9; CI− 59.8, 74.0) compared to the control group (mean 73.8; CI− 67.7, 79.8). Conclusion These findings suggest that, unlike previously published studies that showed subjective improvement in performance with sped-up video-recorded lectures compared to normal speed, objective performance may be worse. PMID:29383063

78 FR 22031 - California High-Speed Rail Authority-Construction Exemption-In Merced, Madera and Fresno Counties...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-12

... High-Speed Rail Authority--Construction Exemption--In Merced, Madera and Fresno Counties, CA AGENCY... High-Speed Rail Authority (Authority). This Final EIS is titled ``California High-Speed Train: Merced... Final EIS assesses the potential environmental impacts of constructing and operating a high-speed...

Harnessing the Potential of ICTs for Literacy Teaching and Learning: Effective Literacy and Numeracy Programmes Using Radio, TV, Mobile Phones, Tablets, and Computers

ERIC Educational Resources Information Center

Hanemann, Ulrike, Ed.

2014-01-01

Different technologies have been used for decades to support adult education and learning. These include radio, television and audio and video cassettes. More recently digital ICTs such as computers, tablets, e-books, and mobile technology have spread at great speed and also found their way into the teaching and learning of literacy and numeracy…

The learning environment and resident burnout: a national study.

PubMed

van Vendeloo, Stefan N; Prins, David J; Verheyen, Cees C P M; Prins, Jelle T; van den Heijkant, Fleur; van der Heijden, Frank M M A; Brand, Paul L P

2018-04-01

Concerns exist about the negative impact of burnout on the professional and personal lives of residents. It is suggested that the origins of burnout among residents are rooted in the learning environment. We aimed to evaluate the association between the learning environment and burnout in a national sample of Dutch residents. We conducted a cross-sectional online survey among all Dutch residents in September 2015. We measured the learning environment using the three domain scores on content, organization, and atmosphere from the Scan of Postgraduate Educational Environment Domains (SPEED) and burnout using the Dutch version of the Maslach Burnout Inventory (UBOS-C). Of 1,231 responding residents (33 specialties), 185 (15.0%) met criteria for burnout. After adjusting for demographic (age, gender and marital status) and work-related factors (year of training, type of teaching hospital and type of specialty), we found a consistent inverse association between SPEED scores and the risk of burnout (aOR 0.54, 95% CI 0.46 to 0.62, p < 0.001). We found a strong and consistent inverse association between the perceived quality of the learning environment and burnout among residents. This suggests that the learning environment is of key importance in preventing resident burnout.

The African disability scooter: efficiency testing in paediatric amputees in Malawi

PubMed Central

Beckles, Verona; McCahill, Jennifer L.; Stebbins, Julie; Mkandawire, Nyengo; Church, John C. T.; Lavy, Chris

2016-01-01

Abstract Purpose: The African Disability Scooter (ADS) was developed for lower limb amputees, to improve mobility and provide access to different terrains. The aim of this study was to test the efficiency of the ADS in Africa over different terrains. Method: Eight subjects with a mean age of 12 years participated. Energy expenditure and speed were calculated over different terrains using the ADS, a prosthetic limb, and crutches. Repeated testing was completed on different days to assess learning effect. Results: Speed was significantly faster with the ADS on a level surface compared to crutch walking. This difference was maintained when using the scooter on rough terrain. Oxygen cost was halved with the scooter on level ground compared to crutch walking. There were no significant differences in oxygen consumption or heart rate. There were significant differences in oxygen cost and speed between days using the scooter over level ground, suggesting the presence of a learning effect. Conclusions: This study demonstrates that the ADS is faster and more energy efficient than crutch walking in young individuals with amputations, and should be considered as an alternative to a prosthesis where this is not available. The presence of a learning effect suggests supervision and training is required when the scooter is first issued.Implications for RehabilitationThe African Disability Scooter:is faster than crutch walking in amputees;is more energy efficient than walking with crutches;supervised use is needed when learning to use the device;is a good alternative/adjunct for mobility. PMID:25316033

More than just tapping: index finger-tapping measures procedural learning in schizophrenia.

PubMed

Da Silva, Felipe N; Irani, Farzin; Richard, Jan; Brensinger, Colleen M; Bilker, Warren B; Gur, Raquel E; Gur, Ruben C

2012-05-01

Finger-tapping has been widely studied using behavioral and neuroimaging paradigms. Evidence supports the use of finger-tapping as an endophenotype in schizophrenia, but its relationship with motor procedural learning remains unexplored. To our knowledge, this study presents the first use of index finger-tapping to study procedural learning in individuals with schizophrenia or schizoaffective disorder (SCZ/SZA) as compared to healthy controls. A computerized index finger-tapping test was administered to 1169 SCZ/SZA patients (62% male, 88% right-handed), and 689 healthy controls (40% male, 93% right-handed). Number of taps per trial and learning slopes across trials for the dominant and non-dominant hands were examined for motor speed and procedural learning, respectively. Both healthy controls and SCZ/SZA patients demonstrated procedural learning for their dominant hand but not for their non-dominant hand. In addition, patients showed a greater capacity for procedural learning even though they demonstrated more variability in procedural learning compared to healthy controls. Left-handers of both groups performed better than right-handers and had less variability in mean number of taps between non-dominant and dominant hands. Males also had less variability in mean tap count between dominant and non-dominant hands than females. As expected, patients had a lower mean number of taps than healthy controls, males outperformed females and dominant-hand trials had more mean taps than non-dominant hand trials in both groups. The index finger-tapping test can measure both motor speed and procedural learning, and motor procedural learning may be intact in SCZ/SZA patients. Copyright © 2012 Elsevier B.V. All rights reserved.

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation.

PubMed

Vasudevan, Erin V L; Hamzey, Rami J; Kirk, Eileen M

2017-08-23

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. However, studying human locomotor learning can be challenging. During infancy and childhood, the neuromuscular system is quite immature, and it is unlikely that locomotor learning during early stages of development is governed by the same mechanisms as in adulthood. By the time humans reach maturity, they are so proficient at walking that it is difficult to come up with a sufficiently novel task to study de novo locomotor learning. The split-belt treadmill, which has two belts that can drive each leg at a different speed, enables the study of both short- (i.e., immediate) and long-term (i.e., over minutes-days; a form of motor learning) gait modifications in response to a novel change in the walking environment. Individuals can easily be screened for previous exposure to the split-belt treadmill, thus ensuring that all experimental participants have no (or equivalent) prior experience. This paper describes a typical split-belt treadmill adaptation protocol that incorporates testing methods to quantify locomotor learning and generalization of this learning to other walking contexts. A discussion of important considerations for designing split-belt treadmill experiments follows, including factors like treadmill belt speeds, rest breaks, and distractors. Additionally, potential but understudied confounding variables (e.g., arm movements, prior experience) are considered in the discussion.

Atmospheric Ionizing Radiation (AIR): Analysis, Results, and Lessons Learned From the June 1997 ER-2 Campaign

NASA Technical Reports Server (NTRS)

Wilson, J. W. (Editor); Jones, I. W. (Editor); Maiden, D. L. (Editor); Goldhagen, P. (Editor)

2003-01-01

The United States initiated a program to assess the technology required for an environmentally safe and operationally efficient High Speed Civil Transport (HSCT) for entrance on the world market after the turn of the century. Due to the changing regulations on radiation exposures and the growing concerns over uncertainty in our knowledge of atmospheric radiations, the NASA High Speed Research Project Office (HSRPO) commissioned a review of "Radiation Exposure and High-Altitude Flight" by the National Council on Radiation Protection and Measurements (NCRP). On the basis of the NCRP recommendations, the HSRPO funded a flight experiment to resolve the environmental uncertainty in the atmospheric ionizing radiation levels as a step in developing an approach to minimize the radiation impact on HSCT operations. To minimize costs in this project, an international investigator approach was taken to assure coverage with instrument sensitivity across the range of particle types and energies to allow unique characterization of the diverse radiation components. The present workshop is a result of the flight measurements made at the maximum intensity of the solar cycle modulated background radiation levels during the month of June 1997.

Development and Testing of a High-Speed Real-Time Kinematic Precise DGPS Positioning System Between Two Aircraft

DTIC Science & Technology

2006-09-01

work-horse for this thesis. He spent hours writing some of the more tedious code, and as much time helping me learn C++ and Linux . He was always there...compared with C++, and the need to use Linux as the operating system, the filter was coded using C++ and KDevelop [28] in SUSE LINUX Professional 9.2 [42...The driving factor for using Linux was the operating system’s ability to access the serial ports in a reliable fashion. Under the original MATLAB® and

A visual tracking method based on improved online multiple instance learning

NASA Astrophysics Data System (ADS)

He, Xianhui; Wei, Yuxing

2016-09-01

Visual tracking is an active research topic in the field of computer vision and has been well studied in the last decades. The method based on multiple instance learning (MIL) was recently introduced into the tracking task, which can solve the problem that template drift well. However, MIL method has relatively poor performance in running efficiency and accuracy, due to its strong classifiers updating strategy is complicated, and the speed of the classifiers update is not always same with the change of the targets' appearance. In this paper, we present a novel online effective MIL (EMIL) tracker. A new update strategy for strong classifier was proposed to improve the running efficiency of MIL method. In addition, to improve the t racking accuracy and stability of the MIL method, a new dynamic mechanism for learning rate renewal of the classifier and variable search window were proposed. Experimental results show that our method performs good performance under the complex scenes, with strong stability and high efficiency.

Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase.

PubMed

de Ávila, Maurício Boff; de Azevedo, Walter Filgueira

2018-04-20

In this study, we describe the development of new machine learning models to predict inhibition of the enzyme 3-dehydroquinate dehydratase (DHQD). This enzyme is the third step of the shikimate pathway and is responsible for the synthesis of chorismate, which is a natural precursor of aromatic amino acids. The enzymes of shikimate pathway are absent in humans, which make them protein targets for the design of antimicrobial drugs. We focus our study on the crystallographic structures of DHQD in complex with competitive inhibitors, for which experimental inhibition constant data is available. Application of supervised machine learning techniques was able to elaborate a robust DHQD-targeted model to predict binding affinity. Combination of high-resolution crystallographic structures and binding information indicates that the prevalence of intermolecular electrostatic interactions between DHQD and competitive inhibitors is of pivotal importance for the binding affinity against this enzyme. The present findings can be used to speed up virtual screening studies focused on the DHQD structure. © 2018 John Wiley & Sons A/S.

Learning for Autonomous Navigation

NASA Technical Reports Server (NTRS)

Angelova, Anelia; Howard, Andrew; Matthies, Larry; Tang, Benyang; Turmon, Michael; Mjolsness, Eric

2005-01-01

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

The diagnosing of plasmas using spectroscopy and imaging on Proto-MPEX

NASA Astrophysics Data System (ADS)

Baldwin, K. A.; Biewer, T. M.; Crouse Powers, J.; Hardin, R.; Johnson, S.; McCleese, A.; Shaw, G. C.; Showers, M.; Skeen, C.

2015-11-01

The Prototype Material Plasma Exposure eXperiment (Proto-MPEX) is a linear plasma device being developed at Oak Ridge National Laboratory (ORNL). This machine plans to study plasma-material interaction (PMI) physics relevant to future fusion reactors. We tested and learned to use tools of spectroscopy and imaging. These tools consist of a spectrometer, a high speed camera, an infrared camera, and a thermocouple. The spectrometer measures the color of the light from the plasma and its intensity. We also used a high speed camera to see how the magnetic field acts on the plasma, and how it is heated to the fourth state of matter. The thermocouples measure the temperature of the objects they are placed against, which in this case are the end plates of the machine. We also used the infrared camera to see the heat pattern of the plasma on the end plates. Data from these instruments will be shown. This work was supported by the US. D.O.E. contract DE-AC05-00OR22725, and the Oak Ridge Associated Universities ARC program.

Handwriting assessment to distinguish comorbid learning difficulties from attention deficit hyperactivity disorder in Chinese adolescents: A case-control study.

PubMed

Li-Tsang, Cecilia W P; Li, Tim M H; Lau, Mandy S W; Ho, Choco H Y; Leung, Howard W H

2018-05-15

Attention deficit hyperactivity disorder (ADHD) and learning difficulties (LDs) are proposed as 2 overlapping disorders. The objective of this study was to investigate the handwriting performance in ADHD and comorbid ADHD-LD adolescents. The study examined the Chinese and English handwriting performance and sensorimotor skills of 32 ADHD, 12 ADHD-LD, and their matched controls. Participants with ADHD had comparable writing time and speed, but the readability was lower than their controls. Participants with ADHD-LD had lower writing speeds in both Chinese and English handwriting than their controls. The ADHD and ADHD-LD groups also showed larger variations in either speed or pen pressure than their controls. Chinese handwriting assessment effectively classified ADHD and ADHD-LD with good sensitivity and positive predictive value. Clinicians should be aware of the fundamental difference between the 2 disorders and make good use of handwriting assessment as a reference to deliver effective therapies and trainings. Copyright © 2018 John Wiley & Sons, Ltd.

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

PubMed Central

Kusne, Aaron Gilad; Gao, Tieren; Mehta, Apurva; Ke, Liqin; Nguyen, Manh Cuong; Ho, Kai-Ming; Antropov, Vladimir; Wang, Cai-Zhuang; Kramer, Matthew J.; Long, Christian; Takeuchi, Ichiro

2014-01-01

Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in multi-dimensional data. Here, we show that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost. Data collected at a synchrotron beamline are analyzed on the fly, and by integrating experimental data with the inorganic crystal structure database (ICSD), we can substantially enhance the accuracy in classifying the structural phases across ternary phase spaces. We have used this approach to identify a novel magnetic phase with enhanced magnetic anisotropy which is a candidate for rare-earth free permanent magnet. PMID:25220062

Visual Prediction in Infancy: What Is the Association with Later Vocabulary?

ERIC Educational Resources Information Center

Ellis, Erica M.; Gonzalez, Marybel Robledo; Deák, Gedeon O.

2014-01-01

Young infants can learn statistical regularities and patterns in sequences of events. Studies have demonstrated a relationship between early sequence learning skills and later development of cognitive and language skills. We investigated the relation between infants' visual response speed to novel event sequences, and their later receptive and…

Automated vehicle identification tags in San Antonio : lessons learned from the metropolitan model deployment initiative : unique method for collecting arterial travel speed information

DOT National Transportation Integrated Search

2000-10-01

This report demonstrates a unique solution to the challenge of providing accurate, timely estimates of arterial travel times to the motoring public. In particular, it discusses the lessons learned in deploying the Vehicle Tag Project in San Antonio, ...

The Contributions of Domain-General and Numerical Factors to Third-Grade Arithmetic Skills and Mathematical Learning Disability

ERIC Educational Resources Information Center

Cowan, Richard; Powell, Daisy

2014-01-01

Explanations of the marked individual differences in elementary school mathematical achievement and mathematical learning disability (MLD or dyscalculia) have involved domain-general factors (working memory, reasoning, processing speed, and oral language) and numerical factors that include single-digit processing efficiency and multidigit skills…

A Teaching-Learning Sequence about Weather Map Reading

ERIC Educational Resources Information Center

Mandrikas, Achilleas; Stavrou, Dimitrios; Skordoulis, Constantine

2017-01-01

In this paper a teaching-learning sequence (TLS) introducing pre-service elementary teachers (PET) to weather map reading, with emphasis on wind assignment, is presented. The TLS includes activities about recognition of wind symbols, assignment of wind direction and wind speed on a weather map and identification of wind characteristics in a…

Infant & Toddler Programs: The Workforce

ERIC Educational Resources Information Center

Child Care, Inc., 2006

2006-01-01

Children's earliest experiences set the stage for school success and adult productivity. In the first three years of life, the brain grows at breakneck speed, creating more than a trillion pathways for learning and development. By the age of three, 85 percent of the brain's capacity is in place, creating the ability to speak, learn, and reason.…

Speeding Up Team Learning.

ERIC Educational Resources Information Center

Edmondson, Amy; Bohmer, Richard; Pisano, Gary

2001-01-01

A study of 16 cardiac surgery teams looked at how the teams adapted to new ways of working. The challenge of team management is to implement new processes as quickly as possible. Steps for creating a learning team include selecting a mix of skills and expertise, framing the challenge, and creating an environment of psychological safety. (JOW)

The Learning Experiences of Students with Dyslexia in a Greek Higher Education Institution

ERIC Educational Resources Information Center

Stampoltzis , Aglaia; Tsitsou, Elisavet; Plesti, Helen; Kalouri, Rani

2015-01-01

Dyslexia is the most common declared disability at universities which primarily affects reading, writing, speed of processing and organization. Many students with dyslexia have "invisible" difficulties that require different types of accommodations. The aim of this study is to give voice to the learning experiences of ten students with…

Brain-Based Teaching in the Digital Age

ERIC Educational Resources Information Center

Sprenger, Marilee

2010-01-01

In the digital age, your students have the ways, means, and speed to gather any information they want. But they need your guidance more than ever. Discover how digital technology is actually changing your students' brains. Learn why this creates new obstacles for teachers, but also opens up potential new pathways for learning. You will understand…

Learning Styles and Computers.

ERIC Educational Resources Information Center

Geisert, Gene; Dunn, Rita

Although the use of computers in the classroom has been heralded as a major breakthrough in education, many educators have yet to use computers to their fullest advantage. This is perhaps due to the traditional assumption that students differed only in their speed of learning. However, new research indicates that students differ in their style of…

Learning a Theory of Causality

ERIC Educational Resources Information Center

Goodman, Noah D.; Ullman, Tomer D.; Tenenbaum, Joshua B.

2011-01-01

The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be…

Inquiry and 21st-Century Learning

ERIC Educational Resources Information Center

Pappas, Marjorie L.

2009-01-01

Over the last eight years, the primary focus in schools has been on passing standardized tests based on a core curriculum. The emphasis on learning content is in direct contrast to the world outside the school walls where the technological capability to provide access to content, i.e., information at lightning speed, already exists. In fact,…

Norm-Optimal ILC Applied to a High-Speed Rack Feeder

NASA Astrophysics Data System (ADS)

Schindele, Dominik; Aschemann, Harald; Ritzke, Jöran

2010-09-01

Rack feeders as automated conveying systems for high bay rackings are of high practical importance. To shorten the transport times by using trajectories with increased kinematic values accompanying control measures for a reduction of the excited structural vibrations are necessary. In this contribution, the model-based design of a norm-optimal iterative learning control structure is presented. The rack feeder is modelled as an elastic multibody system. For the mathematical description of the bending deflections a Ritz ansatz is introduced. The tracking control design is performed separately for both axes using decentralised state space representations. Both the achievable performance and the resulting tracking accuracy of the proposed control concept are shown by measurement results from the experimental set-up.

36 CFR 1192.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false High-speed rail cars... TRANSPORTATION VEHICLES Other Vehicles and Systems § 1192.175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including but not limited to those using “maglev” or high speed...

36 CFR 1192.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 36 Parks, Forests, and Public Property 3 2012-07-01 2012-07-01 false High-speed rail cars... TRANSPORTATION VEHICLES Other Vehicles and Systems § 1192.175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including but not limited to those using “maglev” or high speed...

36 CFR 1192.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 36 Parks, Forests, and Public Property 3 2011-07-01 2011-07-01 false High-speed rail cars... TRANSPORTATION VEHICLES Other Vehicles and Systems § 1192.175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including but not limited to those using “maglev” or high speed...

36 CFR § 1192.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 36 Parks, Forests, and Public Property 3 2013-07-01 2012-07-01 true High-speed rail cars... TRANSPORTATION VEHICLES Other Vehicles and Systems § 1192.175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including but not limited to those using “maglev” or high speed...

36 CFR 1192.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 3 2014-07-01 2014-07-01 false High-speed rail cars... TRANSPORTATION VEHICLES Other Vehicles and Systems § 1192.175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including but not limited to those using “maglev” or high speed...

Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery.

PubMed

Zhao, Yi; Ma, Jiale; Li, Xiaohui; Zhang, Jie

2018-02-27

An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset 'UAV_Fire'. A 15-layered self-learning DCNN architecture named 'Fire_Net' is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, 'Fire_Net' guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified.

Saliency Detection and Deep Learning-Based Wildfire Identification in UAV Imagery

PubMed Central

Zhao, Yi; Ma, Jiale; Li, Xiaohui

2018-01-01

An unmanned aerial vehicle (UAV) equipped with global positioning systems (GPS) can provide direct georeferenced imagery, mapping an area with high resolution. So far, the major difficulty in wildfire image classification is the lack of unified identification marks, the fire features of color, shape, texture (smoke, flame, or both) and background can vary significantly from one scene to another. Deep learning (e.g., DCNN for Deep Convolutional Neural Network) is very effective in high-level feature learning, however, a substantial amount of training images dataset is obligatory in optimizing its weights value and coefficients. In this work, we proposed a new saliency detection algorithm for fast location and segmentation of core fire area in aerial images. As the proposed method can effectively avoid feature loss caused by direct resizing; it is used in data augmentation and formation of a standard fire image dataset ‘UAV_Fire’. A 15-layered self-learning DCNN architecture named ‘Fire_Net’ is then presented as a self-learning fire feature exactor and classifier. We evaluated different architectures and several key parameters (drop out ratio, batch size, etc.) of the DCNN model regarding its validation accuracy. The proposed architecture outperformed previous methods by achieving an overall accuracy of 98%. Furthermore, ‘Fire_Net’ guarantied an average processing speed of 41.5 ms per image for real-time wildfire inspection. To demonstrate its practical utility, Fire_Net is tested on 40 sampled images in wildfire news reports and all of them have been accurately identified. PMID:29495504

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

PubMed

Rosselló, Josep L; Alomar, Miquel L; Morro, Antoni; Oliver, Antoni; Canals, Vincent

2016-08-01

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology. In this work, we show a new low-cost methodology to implement high-density LSM by using Boolean gates. The proposed method is based on the use of probabilistic computing concepts to reduce hardware requirements, thus considerably increasing the neuron count per chip. The result is a highly functional system that is applied to high-speed time series forecasting.

Do Effort and Reward at Work Predict Changes in Cognitive Function? First Longitudinal Results from the Representative German Socio-Economic Panel.

PubMed

Riedel, Natalie; Siegrist, Johannes; Wege, Natalia; Loerbroks, Adrian; Angerer, Peter; Li, Jian

2017-11-15

It has been suggested that work characteristics, such as mental demands, job control, and occupational complexity, are prospectively related to cognitive function. However, current evidence on links between psychosocial working conditions and cognitive change over time is inconsistent. In this study, we applied the effort-reward imbalance model that allows to build on previous research on mental demands and to introduce reward-based learning as a principle with beneficial effect on cognitive function. We aimed to investigate whether high effort, high reward, and low over-commitment in 2006 were associated with positive changes in cognitive function in terms of perceptual speed and word fluency (2006-2012), and whether the co-manifestation of high effort and high reward would yield the strongest association. To this end, we used data on 1031 employees who participated in a large and representative study. Multivariate linear regression analyses supported our main hypotheses (separate and combined effects of effort and reward), particularly on changes in perceptual speed, whereas the effects of over-commitment did not reach the level of statistical significance. Our findings extend available knowledge by examining the course of cognitive function over time. If corroborated by further evidence, organization-based measures in the workplace can enrich efforts towards preventing cognitive decline in ageing workforces.

Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging.

PubMed

Liu, Dengyu; Gu, Jinwei; Hitomi, Yasunobu; Gupta, Mohit; Mitsunaga, Tomoo; Nayar, Shree K

2014-02-01

Cameras face a fundamental trade-off between spatial and temporal resolution. Digital still cameras can capture images with high spatial resolution, but most high-speed video cameras have relatively low spatial resolution. It is hard to overcome this trade-off without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing, and reconstructing the space-time volume to overcome this trade-off. Our approach has two important distinctions compared to previous works: 1) We achieve sparse representation of videos by learning an overcomplete dictionary on video patches, and 2) we adhere to practical hardware constraints on sampling schemes imposed by architectures of current image sensors, which means that our sampling function can be implemented on CMOS image sensors with modified control units in the future. We evaluate components of our approach, sampling function and sparse representation, by comparing them to several existing approaches. We also implement a prototype imaging system with pixel-wise coded exposure control using a liquid crystal on silicon device. System characteristics such as field of view and modulation transfer function are evaluated for our imaging system. Both simulations and experiments on a wide range of scenes show that our method can effectively reconstruct a video from a single coded image while maintaining high spatial resolution.

Intrinsic Functional Connectivity in the Adult Brain and Success in Second-Language Learning.

PubMed

Chai, Xiaoqian J; Berken, Jonathan A; Barbeau, Elise B; Soles, Jennika; Callahan, Megan; Chen, Jen-Kai; Klein, Denise

2016-01-20

There is considerable variability in an individual's ability to acquire a second language (L2) during adulthood. Using resting-state fMRI data acquired before training in English speakers who underwent a 12 week intensive French immersion training course, we investigated whether individual differences in intrinsic resting-state functional connectivity relate to a person's ability to acquire an L2. We focused on two key aspects of language processing--lexical retrieval in spontaneous speech and reading speed--and computed whole-brain functional connectivity from two regions of interest in the language network, namely the left anterior insula/frontal operculum (AI/FO) and the visual word form area (VWFA). Connectivity between the left AI/FO and left posterior superior temporal gyrus (STG) and between the left AI/FO and dorsal anterior cingulate cortex correlated positively with improvement in L2 lexical retrieval in spontaneous speech. Connectivity between the VWFA and left mid-STG correlated positively with improvement in L2 reading speed. These findings are consistent with the different language functions subserved by subcomponents of the language network and suggest that the human capacity to learn an L2 can be predicted by an individual's intrinsic functional connectivity within the language network. Significance statement: There is considerable variability in second-language learning abilities during adulthood. We investigated whether individual differences in intrinsic functional connectivity in the adult brain relate to success in second-language learning, using resting-state functional magnetic resonance imaging in English speakers who underwent a 12 week intensive French immersion training course. We found that pretraining functional connectivity within two different language subnetworks correlated strongly with learning outcome in two different language skills: lexical retrieval in spontaneous speech and reading speed. Our results suggest that the human capacity to learn a second language can be predicted by an individual's intrinsic functional connectivity within the language network. Copyright © 2016 the authors 0270-6474/16/360755-07$15.00/0.

Acute, low-dose methamphetamine administration improves attention/information processing speed and working memory in methamphetamine-dependent individuals displaying poorer cognitive performance at baseline

PubMed Central

Mahoney, James J.; Jackson, Brian J.; Kalechstein, Ari D.; De La Garza, Richard; Newton, Thomas F.

2012-01-01

Abstinent methamphetamine (Meth) dependent individuals demonstrate poorer performance on tests sensitive to attention/information processing speed, learning and memory, and working memory when compared to non-Meth dependent individuals. The poorer performance on these tests may contribute to the morbidity associated with Meth-dependence. In light of this, we sought to determine the effects of acute, low-dose Meth administration on attention, working memory, and verbal learning and memory in 19 non-treatment seeking, Meth-dependent individuals. Participants were predominantly male (89%), Caucasian (63%), and cigarette smokers (63%). Following a four day, drug-free washout period, participants were given a single-blind intravenous infusion of saline, followed the next day by 30 mg of Meth. A battery of neurocognitive tasks was administered before and after each infusion, and performance on measures of accuracy and reaction time were compared between conditions. While acute Meth exposure did not affect test performance for the entire sample, participants who demonstrated relatively poor performance on these tests at baseline, identified using a median split on each test, showed significant improvement on measures of attention/information processing speed and working memory when administered Meth. Improved performance was seen on the following measures of working memory: choice reaction time task (p≤0.04), a 1-back task (p≤0.01), and a 2-back task (p≤0.04). In addition, those participants demonstrating high neurocognitive performance at baseline experienced similar or decreased performance following Meth exposure. These findings suggest that acute administration of Meth may temporarily improve Meth-associated neurocognitive performance in those individuals experiencing lower cognitive performance at baseline. As a result, stimulants may serve as a successful treatment for improving cognitive functioning in those Meth-dependent individuals experiencing neurocognitive impairment. PMID:21122811

Experimental study of hybrid-knife endoscopic submucosal dissection (ESD) versus standard ESD in a Western country.

PubMed

De-la-Peña, Joaquín; Calderón, Ángel; Esteban, José Miguel; López-Rosés, Leopoldo; Martínez-Ares, David; Nogales, Óscar; Orive-Calzada, Aitor; Rodríguez, Sarbelio; Sánchez-Hernández, Eloy; Vila, Juan; Fernández-Esparrach, Gloria

2014-02-01

Endoscopic submucosal dissection (ESD) is an effective but time-consuming treatment for early neoplasia that requires a high level of expertise. The objective of this study was to assess the efficacy and learning curve of gastric ESD with a hybrid knife with high pressure water jet and to compare with standard ESD. We performed a prospective non survival animal study comparing hybrid-knife and standard gastric ESD. Variables recorded were: Number of en-bloc ESD, number of ESD with all marks included (R0), size of specimens, time and speed of dissection and adverse events. Ten endoscopists performed a total of 50 gastric ESD (30 hybrid-knife and 20 standard). Forty-six (92 %) ESD were en-bloc and 25 (50 %) R0 (hybrid-knife: n = 13, 44 %; standard: n = 16, 80 %; p = 0.04). Hybrid-knife ESD was faster than standard (time: 44.6 +/- 21.4 minutes vs. 68.7 +/- 33.5 minutes; p = 0.009 and velocity: 20.8 +/- 9.2 mm(2)/min vs. 14.3 +/- 9.3 mm(2)/min (p = 0.079). Adverse events were not different. There was no change in speed with any of two techniques (hybrid-knife: From 20.33 +/- 15.68 to 28.18 +/- 20.07 mm(2)/min; p = 0.615 and standard: From 6.4 +/- 0.3 to 19.48 +/- 19.21 mm(2)/min; p = 0.607). The learning curve showed a significant improvement in R0 rate in the hybrid-knife group (from 30 % to 100 %). despite the initial performance of hybrid-knife ESD is worse than standard ESD, the learning curve with hybrid knife ESD is short and is associated with a rapid improvement. The introduction of new tools to facilitate ESD should be implemented with caution in order to avoid a negative impact on the results.

Siamese convolutional networks for tracking the spine motion

NASA Astrophysics Data System (ADS)

Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong

2017-09-01

Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

Assessment of potential aerodynamic effects on personnel and equipment in proximity to high-speed train operations : safety of high-speed ground transportation systems

DOT National Transportation Integrated Search

1999-12-01

Amtrak is planning to provide high-speed passenger train service at speeds significantly higher than their current top speed of 125 mph, and with these higher speeds, there are concerns with safety from the aerodynamic effects created by a passing tr...

EEG functional connectivity, axon delays and white matter disease.

PubMed

Nunez, Paul L; Srinivasan, Ramesh; Fields, R Douglas

2015-01-01

Both structural and functional brain connectivities are closely linked to white matter disease. We discuss several such links of potential interest to neurologists, neurosurgeons, radiologists, and non-clinical neuroscientists. Treatment of brains as genuine complex systems suggests major emphasis on the multi-scale nature of brain connectivity and dynamic behavior. Cross-scale interactions of local, regional, and global networks are apparently responsible for much of EEG's oscillatory behaviors. Finite axon propagation speed, often assumed to be infinite in local network models, is central to our conceptual framework. Myelin controls axon speed, and the synchrony of impulse traffic between distant cortical regions appears to be critical for optimal mental performance and learning. Several experiments suggest that axon conduction speed is plastic, thereby altering the regional and global white matter connections that facilitate binding of remote local networks. Combined EEG and high resolution EEG can provide distinct multi-scale estimates of functional connectivity in both healthy and diseased brains with measures like frequency and phase spectra, covariance, and coherence. White matter disease may profoundly disrupt normal EEG coherence patterns, but currently these kinds of studies are rare in scientific labs and essentially missing from clinical environments. Copyright © 2014 International Federation of Clinical Neurophysiology. All rights reserved.

Factor Structure of ImPACT® in Adolescent Student Athletes.

PubMed

Gerrard, Paul B; Iverson, Grant L; Atkins, Joseph E; Maxwell, Bruce A; Zafonte, Ross; Schatz, Philip; Berkner, Paul D

2017-02-01

ImPACT ® (Immediate Post-Concussion Assessment and Cognitive Testing) is a computerized neuropsychological screening battery, which is widely used to measure the acute effects of sport-related concussion and to monitor recovery from injury. This study examined the factor structure of ImPACT ® in several samples of high school student athletes. We hypothesized that a 2-factor structure would be present in all samples. A sample of 4,809 adolescent student athletes was included, and subgroups with a history of treatment for headaches or a self-reported history of learning problems or attention-deficit hyperactivity disorder were analyzed separately. Exploratory principal axis factor analyses with Promax rotations were used. As hypothesized, both the combination of Verbal Memory and Visual Memory Composite scores loaded on one (Memory) factor, while Visual Motor Speed and Reaction Time loaded on a different (Speed) factor, in the total sample and in all subgroups. These results provide reasonably compelling evidence, across multiple samples, which ImPACT ® measures 2 distinct factors: memory and speed. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Automatic Quality Inspection of Percussion Cap Mass Production by Means of 3D Machine Vision and Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Tellaeche, A.; Arana, R.; Ibarguren, A.; Martínez-Otzeta, J. M.

The exhaustive quality control is becoming very important in the world's globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.

2008 13th Expeditionary Warfare Conference

DTIC Science & Technology

2008-10-23

Ships 6 Joint High Speed Vessel (JHSV) • Program Capability – High speed lift ship capable of transporting cargo and personnel across intra... high - speed aluminum trimaran hullform that enables the ship to reach sustainable speeds of over 40 knots and range in excess of 3,500 nautical miles...advancing concepts for a very high speed , manned submersible,

Evaluating the relationship between white matter integrity, cognition, and varieties of video game learning.

PubMed

Ray, Nicholas R; O'Connell, Margaret A; Nashiro, Kaoru; Smith, Evan T; Qin, Shuo; Basak, Chandramallika

2017-01-01

Many studies are currently researching the effects of video games, particularly in the domain of cognitive training. Great variability exists among video games however, and few studies have attempted to compare different types of video games. Little is known, for instance, about the cognitive processes or brain structures that underlie learning of different genres of video games. To examine the cognitive and neural underpinnings of two different types of game learning in order to evaluate their common and separate correlates, with the hopes of informing future intervention research. Participants (31 younger adults and 31 older adults) completed an extensive cognitive battery and played two different genres of video games, one action game and one strategy game, for 1.5 hours each. DTI scans were acquired for each participant, and regional fractional anisotropy (FA) values were extracted using the JHU atlas. Behavioral results indicated that better performance on tasks of working memory and perceptual discrimination was related to enhanced learning in both games, even after controlling for age, whereas better performance on a perceptual speed task was uniquely related with enhanced learning of the strategy game. DTI results indicated that white matter FA in the right fornix/stria terminalis was correlated with action game learning, whereas white matter FA in the left cingulum/hippocampus was correlated with strategy game learning, even after controlling for age. Although cognition, to a large extent, was a common predictor of both types of game learning, regional white matter FA could separately predict action and strategy game learning. Given the neural and cognitive correlates of strategy game learning, strategy games may provide a more beneficial training tool for adults suffering from memory-related disorders or declines in processing speed, particularly older adults.

White-matter structural connectivity predicts short-term melody and rhythm learning in non-musicians.

PubMed

Vaquero, Lucía; Ramos-Escobar, Neus; François, Clément; Penhune, Virginia; Rodríguez-Fornells, Antoni

2018-06-18

Music learning has received increasing attention in the last decades due to the variety of functions and brain plasticity effects involved during its practice. Most previous reports interpreted the differences between music experts and laymen as the result of training. However, recent investigations suggest that these differences are due to a combination of genetic predispositions with the effect of music training. Here, we tested the relationship of the dorsal auditory-motor pathway with individual behavioural differences in short-term music learning. We gathered structural neuroimaging data from 44 healthy non-musicians (28 females) before they performed a rhythm- and a melody-learning task during a single behavioural session, and manually dissected the arcuate fasciculus (AF) in both hemispheres. The macro- and microstructural organization of the AF (i.e., volume and FA) predicted the learning rate and learning speed in the musical tasks, but only in the right hemisphere. Specifically, the volume of the right anterior segment predicted the synchronization improvement during the rhythm task, the FA in the right long segment was correlated with the learning rate in the melody task, and the volume and FA of the right whole AF predicted the learning speed during the melody task. This is the first study finding a specific relation between different branches within the AF and rhythmic and melodic materials. Our results support the relevant function of the AF as the structural correlate of both auditory-motor transformations and the feedback-feedforward loop, and suggest a crucial involvement of the anterior segment in error-monitoring processes related to auditory-motor learning. These findings have implications for both the neuroscience of music field and second-language learning investigations. Copyright © 2018. Published by Elsevier Inc.

Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Kühnlein, Meike; Appelhans, Tim; Nauss, Thomas

2016-03-01

Machine learning (ML) algorithms have successfully been demonstrated to be valuable tools in satellite-based rainfall retrievals which show the practicability of using ML algorithms when faced with high dimensional and complex data. Moreover, recent developments in parallel computing with ML present new possibilities for training and prediction speed and therefore make their usage in real-time systems feasible. This study compares four ML algorithms - random forests (RF), neural networks (NNET), averaged neural networks (AVNNET) and support vector machines (SVM) - for rainfall area detection and rainfall rate assignment using MSG SEVIRI data over Germany. Satellite-based proxies for cloud top height, cloud top temperature, cloud phase and cloud water path serve as predictor variables. The results indicate an overestimation of rainfall area delineation regardless of the ML algorithm (averaged bias = 1.8) but a high probability of detection ranging from 81% (SVM) to 85% (NNET). On a 24-hour basis, the performance of the rainfall rate assignment yielded R2 values between 0.39 (SVM) and 0.44 (AVNNET). Though the differences in the algorithms' performance were rather small, NNET and AVNNET were identified as the most suitable algorithms. On average, they demonstrated the best performance in rainfall area delineation as well as in rainfall rate assignment. NNET's computational speed is an additional advantage in work with large datasets such as in remote sensing based rainfall retrievals. However, since no single algorithm performed considerably better than the others we conclude that further research in providing suitable predictors for rainfall is of greater necessity than an optimization through the choice of the ML algorithm.

Implicit Memory in Monkeys: Development of a Delay Eyeblink Conditioning System with Parallel Electromyographic and High-Speed Video Measurements.

PubMed

Kishimoto, Yasushi; Yamamoto, Shigeyuki; Suzuki, Kazutaka; Toyoda, Haruyoshi; Kano, Masanobu; Tsukada, Hideo; Kirino, Yutaka

2015-01-01

Delay eyeblink conditioning, a cerebellum-dependent learning paradigm, has been applied to various mammalian species but not yet to monkeys. We therefore developed an accurate measuring system that we believe is the first system suitable for delay eyeblink conditioning in a monkey species (Macaca mulatta). Monkey eyeblinking was simultaneously monitored by orbicularis oculi electromyographic (OO-EMG) measurements and a high-speed camera-based tracking system built around a 1-kHz CMOS image sensor. A 1-kHz tone was the conditioned stimulus (CS), while an air puff (0.02 MPa) was the unconditioned stimulus. EMG analysis showed that the monkeys exhibited a conditioned response (CR) incidence of more than 60% of trials during the 5-day acquisition phase and an extinguished CR during the 2-day extinction phase. The camera system yielded similar results. Hence, we conclude that both methods are effective in evaluating monkey eyeblink conditioning. This system incorporating two different measuring principles enabled us to elucidate the relationship between the actual presence of eyelid closure and OO-EMG activity. An interesting finding permitted by the new system was that the monkeys frequently exhibited obvious CRs even when they produced visible facial signs of drowsiness or microsleep. Indeed, the probability of observing a CR in a given trial was not influenced by whether the monkeys closed their eyelids just before CS onset, suggesting that this memory could be expressed independently of wakefulness. This work presents a novel system for cognitive assessment in monkeys that will be useful for elucidating the neural mechanisms of implicit learning in nonhuman primates.

Naming Speed and Effortful and Automatic Inhibition in Children with Arithmetic Learning Disabilities

ERIC Educational Resources Information Center

D'Amico, Antonella; Passolunghi, Maria Chiara

2009-01-01

We report a two-year longitudinal study aimed at investigating the rate of access to numerical and non-numerical information in long-term memory and the functioning of automatic and effortful cognitive inhibition processes in children with arithmetical learning disabilities (ALDs). Twelve children with ALDs, of age 9.3 years, and twelve…

Setting the Pace: Experiments with Keller's PSI

ERIC Educational Resources Information Center

Purao, Sandeep; Sein, Maung; Nilsen, Hallgeir; Larsen, Even Åby

2017-01-01

The ideal of self-paced learning, which was introduced nearly 50 years ago by Keller in his Personalized System of Instruction (PSI), has not yet been widely adopted. In spite of its perceived promise of helping students to learn at the speed aligned to their individual backgrounds, motivation, and skills, PSI has been challenging to implement.…

Pioneering a web-Based Museum in Taiwan: Design and Implementation of Lifelong Distance Learning of Science Education.

ERIC Educational Resources Information Center

Young, Shelley Shwu-Ching; Huang, Yi-Long; Jang, Jyh-Shing Roger

2000-01-01

Describes the development and implementation process of a Web-based science museum in Taiwan. Topics include use of the Internet; lifelong distance learning; museums and the Internet; objectives of the science museum; funding; categories of exhibitions; analysis of Web users; homepage characteristics; graphics and the effect on speed; and future…

An Exploratory Analysis of a Middle School Science Curriculum: Implications for Students with Learning Disabilities

ERIC Educational Resources Information Center

Taylor, Gregory S.; Hord, Casey

2016-01-01

An exploratory study of a middle school curriculum directly aligned with the Next Generation Science Standards was conducted with a focus on how the curriculum addresses the instructional needs of students with learning disabilities. A descriptive analysis of a lesson on speed and velocity was conducted and implications discussed for students with…

The Role of Electronic Pocket Dictionaries as an English Learning Tool among Chinese Students

ERIC Educational Resources Information Center

Jian, Hua-Li; Sandnes, Frode Eika; Law, Kris M. Y.; Huang, Yo-Ping; Huang, Yueh-Min

2009-01-01

This study addressed the role of electronic pocket dictionaries as a language learning tool among university students in Hong Kong and Taiwan. The target groups included engineering and humanities students at both undergraduate and graduate level. Speed of reference was found to be the main motivator for using an electronic pocket dictionary.…

High-speed and ultrahigh-speed cinematographic recording techniques

NASA Astrophysics Data System (ADS)

Miquel, J. C.

1980-12-01

A survey is presented of various high-speed and ultrahigh-speed cinematographic recording systems (covering a range of speeds from 100 to 14-million pps). Attention is given to the functional and operational characteristics of cameras and to details of high-speed cinematography techniques (including image processing, and illumination). A list of cameras (many of them French) available in 1980 is presented

Testing and Performance Verification of a High Bypass Ratio Turbofan Rotor in an Internal Flow Component Test Facility

NASA Technical Reports Server (NTRS)

VanZante, Dale E.; Podboy, Gary G.; Miller, Christopher J.; Thorp, Scott A.

2009-01-01

A 1/5 scale model rotor representative of a current technology, high bypass ratio, turbofan engine was installed and tested in the W8 single-stage, high-speed, compressor test facility at NASA Glenn Research Center (GRC). The same fan rotor was tested previously in the GRC 9x15 Low Speed Wind Tunnel as a fan module consisting of the rotor and outlet guide vanes mounted in a flight-like nacelle. The W8 test verified that the aerodynamic performance and detailed flow field of the rotor as installed in W8 were representative of the wind tunnel fan module installation. Modifications to W8 were necessary to ensure that this internal flow facility would have a flow field at the test package that is representative of flow conditions in the wind tunnel installation. Inlet flow conditioning was designed and installed in W8 to lower the fan face turbulence intensity to less than 1.0 percent in order to better match the wind tunnel operating environment. Also, inlet bleed was added to thin the casing boundary layer to be more representative of a flight nacelle boundary layer. On the 100 percent speed operating line the fan pressure rise and mass flow rate agreed with the wind tunnel data to within 1 percent. Detailed hot film surveys of the inlet flow, inlet boundary layer and fan exit flow were compared to results from the wind tunnel. The effect of inlet casing boundary layer thickness on fan performance was quantified. Challenges and lessons learned from testing this high flow, low static pressure rise fan in an internal flow facility are discussed.

Optimal Sensor Management and Signal Processing for New EMI Systems

DTIC Science & Technology

2010-09-01

adaptive techniques that would improve the speed of data collection and increase the mobility of a TEMTADS system. Although an active learning technique...data, SIG has simulated the active selection based on the data already collected at Camp SLO. In this setup, the active learning approach was constrained...to work only on a 5x5 grid (corresponding to twenty five transmitters and co-located receivers). The first technique assumes that active learning will

Unique characteristics of motor adaptation during walking in young children.

PubMed

Musselman, Kristin E; Patrick, Susan K; Vasudevan, Erin V L; Bastian, Amy J; Yang, Jaynie F

2011-05-01

Children show precocious ability in the learning of languages; is this the case with motor learning? We used split-belt walking to probe motor adaptation (a form of motor learning) in children. Data from 27 children (ages 8-36 mo) were compared with those from 10 adults. Children walked with the treadmill belts at the same speed (tied belt), followed by walking with the belts moving at different speeds (split belt) for 8-10 min, followed again by tied-belt walking (postsplit). Initial asymmetries in temporal coordination (i.e., double support time) induced by split-belt walking were slowly reduced, with most children showing an aftereffect (i.e., asymmetry in the opposite direction to the initial) in the early postsplit period, indicative of learning. In contrast, asymmetries in spatial coordination (i.e., center of oscillation) persisted during split-belt walking and no aftereffect was seen. Step length, a measure of both spatial and temporal coordination, showed intermediate effects. The time course of learning in double support and step length was slower in children than in adults. Moreover, there was a significant negative correlation between the size of the initial asymmetry during early split-belt walking (called error) and the aftereffect for step length. Hence, children may have more difficulty learning when the errors are large. The findings further suggest that the mechanisms controlling temporal and spatial adaptation are different and mature at different times.

Dimension Reduction With Extreme Learning Machine.

PubMed

Kasun, Liyanaarachchi Lekamalage Chamara; Yang, Yan; Huang, Guang-Bin; Zhang, Zhengyou

2016-08-01

Data may often contain noise or irrelevant information, which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms, such as principal component analysis (PCA), non-negative matrix factorization (NMF), random projection (RP), and auto-encoder (AE), is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed, and learns the between-class scatter subspace. To this end, this paper investigates a linear and non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights, respectively. Experimental results on USPS handwritten digit recognition data set, CIFAR-10 object recognition, and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time, and normalized mean square error.

Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks.

PubMed

Annunziata, Roberto; Trucco, Emanuele

2016-11-01

Deep learning has shown great potential for curvilinear structure (e.g., retinal blood vessels and neurites) segmentation as demonstrated by a recent auto-context regression architecture based on filter banks learned by convolutional sparse coding. However, learning such filter banks is very time-consuming, thus limiting the amount of filters employed and the adaptation to other data sets (i.e., slow re-training). We address this limitation by proposing a novel acceleration strategy to speed-up convolutional sparse coding filter learning for curvilinear structure segmentation. Our approach is based on a novel initialisation strategy (warm start), and therefore it is different from recent methods improving the optimisation itself. Our warm-start strategy is based on carefully designed hand-crafted filters (SCIRD-TS), modelling appearance properties of curvilinear structures which are then refined by convolutional sparse coding. Experiments on four diverse data sets, including retinal blood vessels and neurites, suggest that the proposed method reduces significantly the time taken to learn convolutional filter banks (i.e., up to -82%) compared to conventional initialisation strategies. Remarkably, this speed-up does not worsen performance; in fact, filters learned with the proposed strategy often achieve a much lower reconstruction error and match or exceed the segmentation performance of random and DCT-based initialisation, when used as input to a random forest classifier.

Product Quality Modelling Based on Incremental Support Vector Machine

NASA Astrophysics Data System (ADS)

Wang, J.; Zhang, W.; Qin, B.; Shi, W.

2012-05-01

Incremental Support vector machine (ISVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. It is suitable for the problem of sequentially arriving field data and has been widely used for product quality prediction and production process optimization. However, the traditional ISVM learning does not consider the quality of the incremental data which may contain noise and redundant data; it will affect the learning speed and accuracy to a great extent. In order to improve SVM training speed and accuracy, a modified incremental support vector machine (MISVM) is proposed in this paper. Firstly, the margin vectors are extracted according to the Karush-Kuhn-Tucker (KKT) condition; then the distance from the margin vectors to the final decision hyperplane is calculated to evaluate the importance of margin vectors, where the margin vectors are removed while their distance exceed the specified value; finally, the original SVs and remaining margin vectors are used to update the SVM. The proposed MISVM can not only eliminate the unimportant samples such as noise samples, but also can preserve the important samples. The MISVM has been experimented on two public data and one field data of zinc coating weight in strip hot-dip galvanizing, and the results shows that the proposed method can improve the prediction accuracy and the training speed effectively. Furthermore, it can provide the necessary decision supports and analysis tools for auto control of product quality, and also can extend to other process industries, such as chemical process and manufacturing process.

Laryngeal High-Speed Videoendoscopy: Rationale and Recommendation for Accurate and Consistent Terminology

PubMed Central

Deliyski, Dimitar D.; Hillman, Robert E.

2015-01-01

Purpose The authors discuss the rationale behind the term laryngeal high-speed videoendoscopy to describe the application of high-speed endoscopic imaging techniques to the visualization of vocal fold vibration. Method Commentary on the advantages of using accurate and consistent terminology in the field of voice research is provided. Specific justification is described for each component of the term high-speed videoendoscopy, which is compared and contrasted with alternative terminologies in the literature. Results In addition to the ubiquitous high-speed descriptor, the term endoscopy is necessary to specify the appropriate imaging technology and distinguish among modalities such as ultrasound, magnetic resonance imaging, and nonendoscopic optical imaging. Furthermore, the term video critically indicates the electronic recording of a sequence of optical still images representing scenes in motion, in contrast to strobed images using high-speed photography and non-optical high-speed magnetic resonance imaging. High-speed videoendoscopy thus concisely describes the technology and can be appended by the desired anatomical nomenclature such as laryngeal. Conclusions Laryngeal high-speed videoendoscopy strikes a balance between conciseness and specificity when referring to the typical high-speed imaging method performed on human participants. Guidance for the creation of future terminology provides clarity and context for current and future experiments and the dissemination of results among researchers. PMID:26375398

Implicit and explicit learning in schizophrenics treated with olanzapine and with classic neuroleptics.

PubMed

Stevens, Andreas; Schwarz, Jürgen; Schwarz, Benedikt; Ruf, Ilona; Kolter, Thomas; Czekalla, Joerg

2002-03-01

Novel and classic neuroleptics differ in their effects on limbic striatal/nucleus accumbens (NA) and prefrontal cortex (PFC) dopamine turnover, suggesting differential effects on implicit and explicit learning as well as on anhedonia. The present study investigates whether such differences can be demonstrated in a naturalistic sample of schizophrenic patients. Twenty-five inpatients diagnosed with DSM-IV schizophrenic psychosis and treated for at least 14 days with the novel neuroleptic olanzapine were compared with 25 schizophrenics taking classic neuroleptics and with 25 healthy controls, matched by age and education level. PFC/NA-dependent implicit learning was assessed by a serial reaction time task (SRTT) and compared with cerebellum-mediated classical eye-blink conditioning and explicit visuospatial memory. Anhedonia was measured with the Snaith-Hamilton-Pleasure Scale (SHAPS). Implicit (SRTT) and psychomotor speed, but not explicit (visuospatial) learning were superior in the olanzapine-treated group as compared to the patients on classic neuroleptics. Compared to healthy controls, olanzapine-treated schizophrenics showed similar implicit learning, but reduced explicit (visuospatial) memory performance. Acquisition of eyeblink conditioning was not different between the three groups. There was no difference with regard to anhedonia and SANS scores between the patients. Olanzapine seems to interfere less with unattended learning and motor speed than classical neuroleptics. In daily life, this may translate into better adaptation to a rapidly changing environment. The effects seem specific, as in explicit learning and eyeblink conditioning no difference to classic NL was found.

Moderators of noise-induced cognitive change in healthy adults.

PubMed

Wright, Bernice Al; Peters, Emmanuelle R; Ettinger, Ulrich; Kuipers, Elizabeth; Kumari, Veena

2016-01-01

Environmental noise causes cognitive impairment, particularly in executive function and episodic memory domains, in healthy populations. However, the possible moderating influences on this relationship are less clear. This study assessed 54 healthy participants (24 men) on a cognitive battery (measuring psychomotor speed, attention, executive function, working memory, and verbal learning and memory) under three (quiet, urban, and social) noise conditions. IQ, subjective noise sensitivity, sleep, personality, paranoia, depression, anxiety, stress, and schizotypy were assessed on a single occasion. We found significantly slower psychomotor speed (urban), reduced working memory and episodic memory (urban and social), and more cautious decision-making (executive function, urban) under noise conditions. There was no effect of sex. Variance in urban noise-induced changes in psychomotor speed, attention, Trail Making B-A (executive function), and immediate recall and social noise-induced changes in verbal fluency (executive function) and immediate recall were explained by a combination of baseline cognition and paranoia, noise sensitivity, sleep, or cognitive disorganization. Higher baseline cognition (but not IQ) predicted greater impairment under urban and social noise for most cognitive variables. Paranoia predicted psychomotor speed, attention, and executive function impairment. Subjective noise sensitivity predicted executive function and memory impairment. Poor sleep quality predicted less memory impairment. Finally, lower levels of cognitive disorganization predicted slower psychomotor speed and greater memory impairment. The identified moderators should be considered in studies aiming to reduce the detrimental effects of occupational and residential noise. These results highlight the importance of studying noise effects in clinical populations characterized by high levels of the paranoia, sleep disturbances, noise sensitivity, and cognitive disorganization.

Moderators of noise-induced cognitive change in healthy adults

PubMed Central

Wright, Bernice AL; Peters, Emmanuelle R; Ettinger, Ulrich; Kuipers, Elizabeth; Kumari, Veena

2016-01-01

Environmental noise causes cognitive impairment, particularly in executive function and episodic memory domains, in healthy populations. However, the possible moderating influences on this relationship are less clear. This study assessed 54 healthy participants (24 men) on a cognitive battery (measuring psychomotor speed, attention, executive function, working memory, and verbal learning and memory) under three (quiet, urban, and social) noise conditions. IQ, subjective noise sensitivity, sleep, personality, paranoia, depression, anxiety, stress, and schizotypy were assessed on a single occasion. We found significantly slower psychomotor speed (urban), reduced working memory and episodic memory (urban and social), and more cautious decision-making (executive function, urban) under noise conditions. There was no effect of sex. Variance in urban noise-induced changes in psychomotor speed, attention, Trail Making B-A (executive function), and immediate recall and social noise-induced changes in verbal fluency (executive function) and immediate recall were explained by a combination of baseline cognition and paranoia, noise sensitivity, sleep, or cognitive disorganization. Higher baseline cognition (but not IQ) predicted greater impairment under urban and social noise for most cognitive variables. Paranoia predicted psychomotor speed, attention, and executive function impairment. Subjective noise sensitivity predicted executive function and memory impairment. Poor sleep quality predicted less memory impairment. Finally, lower levels of cognitive disorganization predicted slower psychomotor speed and greater memory impairment. The identified moderators should be considered in studies aiming to reduce the detrimental effects of occupational and residential noise. These results highlight the importance of studying noise effects in clinical populations characterized by high levels of the paranoia, sleep disturbances, noise sensitivity, and cognitive disorganization. PMID:27157685

14 CFR 23.253 - High speed characteristics.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false High speed characteristics. 23.253 Section... Requirements § 23.253 High speed characteristics. If a maximum operating speed VMO/MMO is established under § 23.1505(c), the following speed increase and recovery characteristics must be met: (a) Operating...

78 FR 36823 - California High-Speed Rail Authority-Construction Exemption-in Merced, Madera and Fresno Counties...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-06-19

...-Speed Rail Authority--Construction Exemption--in Merced, Madera and Fresno Counties, Cal AGENCY: Surface...-Speed Rail Authority (Authority) to construct an approximately 65- mile high-speed passenger rail line... statewide California High-Speed Train System. This exemption is subject to environmental mitigation...

14 CFR 23.253 - High speed characteristics.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false High speed characteristics. 23.253 Section... Requirements § 23.253 High speed characteristics. If a maximum operating speed VMO/MMO is established under § 23.1505(c), the following speed increase and recovery characteristics must be met: (a) Operating...

Predictors of older drivers' involvement in high-range speeding behavior.

PubMed

Chevalier, Anna; Coxon, Kristy; Rogers, Kris; Chevalier, Aran John; Wall, John; Brown, Julie; Clarke, Elizabeth; Ivers, Rebecca; Keay, Lisa

2017-02-17

Even small increases in vehicle speed raise crash risk and resulting injury severity. Older drivers are at increased risk of involvement in casualty crashes and injury compared to younger drivers. However, there is little objective evidence about older drivers' speeding. This study investigates the nature and predictors of high-range speeding among drivers aged 75-94 years. Speed per second was estimated using Global Positioning System devices installed in participants' vehicles. High-range speeding events were defined as traveling an average 10+km/h above the speed limit over 30 seconds. Descriptive analysis examined speeding events by participant characteristics and mileage driven. Regression analyses were used to examine the association between involvement in high-range speeding events and possible predictive factors. Most (96%, 182/190) participants agreed to have their vehicle instrumented, and speeding events were accurately recorded for 97% (177/182) of participants. While 77% (136/177) of participants were involved in one or more high-range events, 42% (75/177) were involved in greater than five events during 12-months of data collection. Participants involved in high-range events drove approximately twice as many kilometres as those not involved. High-range events tended to be infrequent (median = 6 per 10,000 km; IQR = 2-18). The rate of high-range speeding was associated with better cognitive function and attention to the driving environment. This suggests those older drivers with poorer cognition and visual attention may drive more cautiously, thereby reducing their high-range speeding behavior.

Acute and Chronic Altitude-Induced Cognitive Dysfunction in Children and Adolescents.

PubMed

Rimoldi, Stefano F; Rexhaj, Emrush; Duplain, Hervé; Urben, Sébastien; Billieux, Joël; Allemann, Yves; Romero, Catherine; Ayaviri, Alejandro; Salinas, Carlos; Villena, Mercedes; Scherrer, Urs; Sartori, Claudio

2016-02-01

To assess whether exposure to high altitude induces cognitive dysfunction in young healthy European children and adolescents during acute, short-term exposure to an altitude of 3450 m and in an age-matched European population permanently living at this altitude. We tested executive function (inhibition, shifting, and working memory), memory (verbal, short-term visuospatial, and verbal episodic memory), and speed processing ability in: (1) 48 healthy nonacclimatized European children and adolescents, 24 hours after arrival at high altitude and 3 months after return to low altitude; (2) 21 matched European subjects permanently living at high altitude; and (3) a matched control group tested twice at low altitude. Short-term hypoxia significantly impaired all but 2 (visuospatial memory and processing speed) of the neuropsychological abilities that were tested. These impairments were even more severe in the children permanently living at high altitude. Three months after return to low altitude, the neuropsychological performances significantly improved and were comparable with those observed in the control group tested only at low altitude. Acute short-term exposure to an altitude at which major tourist destinations are located induces marked executive and memory deficits in healthy children. These deficits are equally marked or more severe in children permanently living at high altitude and are expected to impair their learning abilities. Copyright © 2016 Elsevier Inc. All rights reserved.

The role of partial knowledge in statistical word learning

PubMed Central

Fricker, Damian C.; Yu, Chen; Smith, Linda B.

2013-01-01

A critical question about the nature of human learning is whether it is an all-or-none or a gradual, accumulative process. Associative and statistical theories of word learning rely critically on the later assumption: that the process of learning a word's meaning unfolds over time. That is, learning the correct referent for a word involves the accumulation of partial knowledge across multiple instances. Some theories also make an even stronger claim: Partial knowledge of one word–object mapping can speed up the acquisition of other word–object mappings. We present three experiments that test and verify these claims by exposing learners to two consecutive blocks of cross-situational learning, in which half of the words and objects in the second block were those that participants failed to learn in Block 1. In line with an accumulative account, Re-exposure to these mis-mapped items accelerated the acquisition of both previously experienced mappings and wholly new word–object mappings. But how does partial knowledge of some words speed the acquisition of others? We consider two hypotheses. First, partial knowledge of a word could reduce the amount of information required for it to reach threshold, and the supra-threshold mapping could subsequently aid in the acquisition of new mappings. Alternatively, partial knowledge of a word's meaning could be useful for disambiguating the meanings of other words even before the threshold of learning is reached. We construct and compare computational models embodying each of these hypotheses and show that the latter provides a better explanation of the empirical data. PMID:23702980

Anomaly detection driven active learning for identifying suspicious tracks and events in WAMI video

NASA Astrophysics Data System (ADS)

Miller, David J.; Natraj, Aditya; Hockenbury, Ryler; Dunn, Katherine; Sheffler, Michael; Sullivan, Kevin

2012-06-01

We describe a comprehensive system for learning to identify suspicious vehicle tracks from wide-area motion (WAMI) video. First, since the road network for the scene of interest is assumed unknown, agglomerative hierarchical clustering is applied to all spatial vehicle measurements, resulting in spatial cells that largely capture individual road segments. Next, for each track, both at the cell (speed, acceleration, azimuth) and track (range, total distance, duration) levels, extreme value feature statistics are both computed and aggregated, to form summary (p-value based) anomaly statistics for each track. Here, to fairly evaluate tracks that travel across different numbers of spatial cells, for each cell-level feature type, a single (most extreme) statistic is chosen, over all cells traveled. Finally, a novel active learning paradigm, applied to a (logistic regression) track classifier, is invoked to learn to distinguish suspicious from merely anomalous tracks, starting from anomaly-ranked track prioritization, with ground-truth labeling by a human operator. This system has been applied to WAMI video data (ARGUS), with the tracks automatically extracted by a system developed in-house at Toyon Research Corporation. Our system gives promising preliminary results in highly ranking as suspicious aerial vehicles, dismounts, and traffic violators, and in learning which features are most indicative of suspicious tracks.

Does temporal contiguity moderate contingency learning in a speeded performance task?

PubMed

Schmidt, James R; De Houwer, Jan

2012-01-01

In four experiments, we varied the time between the onset of distracting nonwords and target colour words in a word-word version of the colour-word contingency learning paradigm. Contingencies were created by pairing a distractor nonword more often with one target colour word than with other colour words. A contingency effect corresponds to faster responses to the target colour word on high-contingency trials (i.e., distractor nonword followed by the target colour word with which it appears most often) than on low-contingency trials (i.e., distractor nonword followed by a target colour word with which it appears only occasionally). Roughly equivalent-sized contingency effects were found at stimulus-onset asynchronies (SOAs) of 50, 250, and 450 ms in Experiment 1, and 50, 500, and 1,000 ms in Experiment 2. In Experiment 3, a contingency effect was observed at SOAs of -50, -200, and -350 ms. In Experiment 4, interstimulus interval (ISI) was varied along with SOA, and learning was equivalent for 200-, 700-, and 1,200-ms SOAs. Together, these experiments suggest that the distracting stimulus does not need to be presented in close temporal contiguity with the response to induce learning. Relations to past research on causal judgement and implications for further contingency learning research are discussed.

Intelligent measurement and compensation of linear motor force ripple: a projection-based learning approach in the presence of noise

NASA Astrophysics Data System (ADS)

Liu, Yang; Song, Fazhi; Yang, Xiaofeng; Dong, Yue; Tan, Jiubin

2018-06-01

Due to their structural simplicity, linear motors are increasingly receiving attention for use in high velocity and high precision applications. The force ripple, as a space-periodic disturbance, however, would deteriorate the achievable dynamic performance. Conventional force ripple measurement approaches are time-consuming and have high requirements on the experimental conditions. In this paper, a novel learning identification algorithm is proposed for force ripple intelligent measurement and compensation. Existing identification schemes always use all the error signals to update the parameters in the force ripple. However, the error induced by noise is non-effective for force ripple identification, and even deteriorates the identification process. In this paper only the most pertinent information in the error signal is utilized for force ripple identification. Firstly, the effective error signals caused by the reference trajectory and the force ripple are extracted by projecting the overall error signals onto a subspace spanned by the physical model of the linear motor as well as the sinusoidal model of the force ripple. The time delay in the linear motor is compensated in the basis functions. Then, a data-driven approach is proposed to design the learning gain. It balances the trade-off between convergence speed and robustness against noise. Simulation and experimental results validate the proposed method and confirm its effectiveness and superiority.

Research on natural frequency based on modal test for high speed vehicles

NASA Astrophysics Data System (ADS)

Ma, Guangsong; He, Guanglin; Guo, Yachao

2018-04-01

High speed vehicle as a vibration system, resonance generated in flight may be harmful to high speed vehicles. It is possible to solve the resonance problem by acquiring the natural frequency of the high-speed aircraft and then taking some measures to avoid the natural frequency of the high speed vehicle. Therefore, In this paper, the modal test of the high speed vehicle was carried out by using the running hammer method and the PolyMAX modal parameter identification method. Firstly, the total frequency response function, coherence function of the high speed vehicle are obtained by the running hammer stimulation test, and through the modal assurance criterion (MAC) to determine the accuracy of the estimated parameters. Secondly, the first three order frequencies, the pole steady state diagram of the high speed vehicles is obtained by the PolyMAX modal parameter identification method. At last, the natural frequency of the vibration system was accurately obtained by the running hammer method.

Linking Individual Learning Styles to Approach-Avoidance Motivational Traits and Computational Aspects of Reinforcement Learning

PubMed Central

Carl Aberg, Kristoffer; Doell, Kimberly C.; Schwartz, Sophie

2016-01-01

Learning how to gain rewards (approach learning) and avoid punishments (avoidance learning) is fundamental for everyday life. While individual differences in approach and avoidance learning styles have been related to genetics and aging, the contribution of personality factors, such as traits, remains undetermined. Moreover, little is known about the computational mechanisms mediating differences in learning styles. Here, we used a probabilistic selection task with positive and negative feedbacks, in combination with computational modelling, to show that individuals displaying better approach (vs. avoidance) learning scored higher on measures of approach (vs. avoidance) trait motivation, but, paradoxically, also displayed reduced learning speed following positive (vs. negative) outcomes. These data suggest that learning different types of information depend on associated reward values and internal motivational drives, possibly determined by personality traits. PMID:27851807

Rapid learning: a breakthrough agenda.

PubMed

Etheredge, Lynn M

2014-07-01

A "rapid-learning health system" was proposed in a 2007 thematic issue of Health Affairs. The system was envisioned as one that uses evidence-based medicine to quickly determine the best possible treatments for patients. It does so by drawing on electronic health records and the power of big data to access large volumes of information from a variety of sources at high speed. The foundation for a rapid-learning health system was laid during 2007-13 by workshops, policy papers, large public investments in databases and research programs, and developing learning systems. Challenges now include implementing a new clinical research system with several hundred million patients, modernizing clinical trials and registries, devising and funding research on national priorities, and analyzing genetic and other factors that influence diseases and responses to treatment. Next steps also should aim to improve comparative effectiveness research; build on investments in health information technology to standardize handling of genetic information and support information exchange through apps and software modules; and develop new tools, data, and information for clinical decision support. Further advances will require commitment, leadership, and public-private and global collaboration. Project HOPE—The People-to-People Health Foundation, Inc.

Teaching Physics with Basketball

NASA Astrophysics Data System (ADS)

Chanpichai, N.; Wattanakasiwich, P.

2010-07-01

Recently, technologies and computer takes important roles in learning and teaching, including physics. Advance in technologies can help us better relating physics taught in the classroom to the real world. In this study, we developed a module on teaching a projectile motion through shooting a basketball. Students learned about physics of projectile motion, and then they took videos of their classmates shooting a basketball by using the high speed camera. Then they analyzed videos by using Tracker, a video analysis and modeling tool. While working with Tracker, students learned about the relationships between three kinematics graphs. Moreover, they learned about a real projectile motion (with an air resistance) through modeling tools. Students' abilities to interpret kinematics graphs were investigated before and after the instruction by using the Test of Understanding Graphs in Kinematics (TUG-K). The maximum normalized gain or is 0.77, which indicated students' improvement in determining displacement from the velocity-time graph. The minimum is 0.20, which indicated that most students still have difficulties interpreting the change in velocity from the acceleration-time graph. Results from evaluation questionnaires revealed that students also satisfied with the instructions that related physics contents to shooting basketball.

Comparative use of podcasts vs. lecture transcripts as learning aids for dental students.

PubMed

Allen, Kenneth L; Katz, Ralph V

2011-06-01

The purpose of this project was to describe dental students' use of lecture podcasts versus written lecture transcripts as learning aids under three different circumstances: studying for an exam, reviewing an attended lecture, and reviewing a missed lecture. Additional analyses were performed to see whether demographic differences (e.g., age, gender, language skills, and computer skills) or grade differences were associated with preferences for using podcast versus written lecture transcripts of class notes. Fifty-one percent (n=171) of the second-year dental students at the New York University College of Dentistry voluntarily participated in this survey. The major findings were that 1) a high percentage of students (70-92 percent) used one or both aids in all three utilization circumstances with a consistent preference for podcast use, especially when reviewing a missed lecture; 2) course grades were not associated with the preferred use of either lecture aid; and 3) over half the students listened to the podcasts at speeds that were one and one-half or two times faster than normal speech, especially younger students. Further studies are warranted to delve into the current student generation's preferred learning styles and the resultant learning outcomes associated with those preferences.

Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA.

PubMed

Jiansen Li; Jianqi Sun; Ying Song; Yanran Xu; Jun Zhao

2014-01-01

An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution dictionary and a high-resolution dictionary, for sparse coding and image updating respectively. However, the amount of data is huge for three-dimensional reconstruction, especially when the number of slices is large. Thus, the procedure is time-consuming. In this paper, we first utilize the NVIDIA Corporation's compute unified device architecture (CUDA) programming model to design the parallel algorithms on graphics processing unit (GPU) to accelerate the reconstruction procedure. The main optimizations operate in the dictionary learning algorithm and the image updating part, such as the orthogonal matching pursuit (OMP) algorithm and the k-singular value decomposition (K-SVD) algorithm. Then we develop another version of CUDA code with algorithmic optimization. Experimental results show that more than 324 times of speedup is achieved compared with the CPU-only codes when the number of MRI slices is 24.

Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making.

PubMed

Drugowitsch, Jan; DeAngelis, Gregory C; Angelaki, Dora E; Pouget, Alexandre

2015-06-19

For decisions made under time pressure, effective decision making based on uncertain or ambiguous evidence requires efficient accumulation of evidence over time, as well as appropriately balancing speed and accuracy, known as the speed/accuracy trade-off. For simple unimodal stimuli, previous studies have shown that human subjects set their speed/accuracy trade-off to maximize reward rate. We extend this analysis to situations in which information is provided by multiple sensory modalities. Analyzing previously collected data (Drugowitsch et al., 2014), we show that human subjects adjust their speed/accuracy trade-off to produce near-optimal reward rates. This trade-off can change rapidly across trials according to the sensory modalities involved, suggesting that it is represented by neural population codes rather than implemented by slow neuronal mechanisms such as gradual changes in synaptic weights. Furthermore, we show that deviations from the optimal speed/accuracy trade-off can be explained by assuming an incomplete gradient-based learning of these trade-offs.

Concepts for Multi-Speed Rotorcraft Drive System - Status of Design and Testing at NASA GRC

NASA Technical Reports Server (NTRS)

Stevens, Mark A.; Lewicki, David G.; Handschuh, Robert F.

2015-01-01

In several studies and on-going developments for advanced rotorcraft, the need for variable/multi-speed capable rotors has been raised. Speed changes of up to 50 percent have been proposed for future rotorcraft to improve vehicle performance. A rotor speed change during operation not only requires a rotor that can perform effectively over the operating speed/load range, but also requires a propulsion system possessing these same capabilities. A study was completed investigating possible drive system arrangements that can accommodate up to a 50 percent speed change. Key drivers were identified from which simplicity and weight were judged as central. This paper presents the current status of two gear train concepts coupled with the first of two clutch types developed and tested thus far with focus on design lessons learned and areas requiring development. Also, a third concept is presented, a dual input planetary differential as leveraged from a simple planetary with fixed carrier.

Potential scenarios of concern for high speed rail operations

DOT National Transportation Integrated Search

2011-03-16

Currently, multiple operating authorities are proposing the : introduction of high-speed rail service in the United States. : While high-speed rail service shares a number of basic : principles with conventional-speed rail service, the operational : ...

Concepts for Multi-Speed Rotorcraft Drive System - Status of Design and Testing at NASA GRC

NASA Technical Reports Server (NTRS)

Stevens, Mark A.; Lewicki, David G.; Handschuh, Robert F.

2015-01-01

In several studies and on-going developments for advanced rotorcraft, the need for variable multi-speed capable rotors has been raised. Speed changes of up to 50 have been proposed for future rotorcraft to improve vehicle performance. A rotor speed change during operation not only requires a rotor that can perform effectively over the operating speedload range, but also requires a propulsion system possessing these same capabilities. A study was completed investigating possible drive system arrangements that can accommodate up to a 50 speed change. Key drivers were identified from which simplicity and weight were judged as central. This paper presents the current status of two gear train concepts coupled with the first of two clutch types developed and tested thus far with focus on design lessons learned and areas requiring development. Also, a third concept is presented, a dual input planetary differential as leveraged from a simple planetary with fixed carrier.

14 CFR 25.253 - High-speed characteristics.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false High-speed characteristics. 25.253 Section...-speed characteristics. (a) Speed increase and recovery characteristics. The following speed increase and... inadvertent speed increases (including upsets in pitch and roll) must be simulated with the airplane trimmed...

14 CFR 25.253 - High-speed characteristics.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 14 Aeronautics and Space 1 2011-01-01 2011-01-01 false High-speed characteristics. 25.253 Section...-speed characteristics. (a) Speed increase and recovery characteristics. The following speed increase and... inadvertent speed increases (including upsets in pitch and roll) must be simulated with the airplane trimmed...

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Theta Neurofeedback Effects on Motor Memory Consolidation and Performance Accuracy: An Apparent Paradox?

PubMed

Reiner, Miriam; Lev, Dror D; Rosen, Amit

2018-05-15

Previous studies have shown that theta neurofeedback enhances motor memory consolidation on an easy-to-learn finger-tapping task. However, the simplicity of the finger-tapping task precludes evaluating the putative effects of elevated theta on performance accuracy. Mastering a motor sequence is classically assumed to entail faster performance with fewer errors. The speed-accuracy tradeoff (SAT) principle states that as action speed increases, motor performance accuracy decreases. The current study investigated whether theta neurofeedback could improve both performance speed and performance accuracy, or would only enhance performance speed at the cost of reduced accuracy. A more complex task was used to study the effects of parietal elevated theta on 45 healthy volunteers The findings confirmed previous results on the effects of theta neurofeedback on memory consolidation. In contrast to the two control groups, in the theta-neurofeedback group the speed-accuracy tradeoff was reversed. The speed-accuracy tradeoff patterns only stabilized after a night's sleep implying enhancement in terms of both speed and accuracy. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Propylthiouracil

MedlinePlus

... produces too much thyroid hormone, speeding the body's metabolism, and causing certain symptoms) in adults and children ... to learn about take-back programs in your community. See the FDA's Safe Disposal of Medicines website ( ...

High-speed adaptive optics for imaging of the living human eye

PubMed Central

Yu, Yongxin; Zhang, Tianjiao; Meadway, Alexander; Wang, Xiaolin; Zhang, Yuhua

2015-01-01

The discovery of high frequency temporal fluctuation of human ocular wave aberration dictates the necessity of high speed adaptive optics (AO) correction for high resolution retinal imaging. We present a high speed AO system for an experimental adaptive optics scanning laser ophthalmoscope (AOSLO). We developed a custom high speed Shack-Hartmann wavefront sensor and maximized the wavefront detection speed based upon a trade-off among the wavefront spatial sampling density, the dynamic range, and the measurement sensitivity. We examined the temporal dynamic property of the ocular wavefront under the AOSLO imaging condition and improved the dual-thread AO control strategy. The high speed AO can be operated with a closed-loop frequency up to 110 Hz. Experiment results demonstrated that the high speed AO system can provide improved compensation for the wave aberration up to 30 Hz in the living human eye. PMID:26368408

49 CFR 236.1007 - Additional requirements for high-speed service.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 4 2011-10-01 2011-10-01 false Additional requirements for high-speed service..., AND APPLIANCES Positive Train Control Systems § 236.1007 Additional requirements for high-speed... by this subpart, and which have been utilized on high-speed rail systems with similar technical and...

The Optimum Conditions of Foreign Languages in Primary Education

ERIC Educational Resources Information Center

Giannikas, Christina Nicole

2014-01-01

The aim of the paper is to review the primary language learning situation in Europe and shed light on the benefits it carries. Early language learning is the biggest policy development in education and has developed in rapid speed over the past 30 years; this article considers the effects and advantages of the optimum condition of an early start,…

The Effect of Rehearsal Learning and Warm-up on the Speed of Different Swimming Strokes

ERIC Educational Resources Information Center

Magno, Carlo; Mascardo, Elizabeth

2009-01-01

The study investigated the effects of rehearsal learning and warm-up exercise on the time of performing different swimming strokes. The study was conducted among 202 college freshmen students taking up a course on physical education concentrated in swimming. The design employed is a mixed factorial (2 X 2) where time of swimming is measured before…

Immersive virtual reality simulations in nursing education.

PubMed

Kilmon, Carol A; Brown, Leonard; Ghosh, Sumit; Mikitiuk, Artur

2010-01-01

This article explores immersive virtual reality as a potential educational strategy for nursing education and describes an immersive learning experience now being developed for nurses. This pioneering project is a virtual reality application targeting speed and accuracy of nurse response in emergency situations requiring cardiopulmonary resuscitation. Other potential uses and implications for the development of virtual reality learning programs are discussed.

The Personal Development Plan Practice Questionnaire: The Development and Validation of an Instrument to Assess the Employee's Perception of Personal Development Plan Practice

ERIC Educational Resources Information Center

Beausaert, Simon; Segers, Mien; Gijselaers, Wim

2011-01-01

Confronted with the speed of technological advancements and increasing global competition, organizations have come to realize that their employees' continuous learning drives business success. A popular tool to support and enhance continuous learning is the personal development plan (PDP). Despite its popularity, empirical evidence of the…

Machine learning on-a-chip: a high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications.

PubMed

Sun, Yuwen; Cheng, Allen C

2012-07-01

Artificial neural networks (ANNs) are a promising machine learning technique in classifying non-linear electrocardiogram (ECG) signals and recognizing abnormal patterns suggesting risks of cardiovascular diseases (CVDs). In this paper, we propose a new reusable neuron architecture (RNA) enabling a performance-efficient and cost-effective silicon implementation for ANN. The RNA architecture consists of a single layer of physical RNA neurons, each of which is designed to use minimal hardware resource (e.g., a single 2-input multiplier-accumulator is used to compute the dot product of two vectors). By carefully applying the principal of time sharing, RNA can multiplexs this single layer of physical neurons to efficiently execute both feed-forward and back-propagation computations of an ANN while conserving the area and reducing the power dissipation of the silicon. A three-layer 51-30-12 ANN is implemented in RNA to perform the ECG classification for CVD detection. This RNA hardware also allows on-chip automatic training update. A quantitative design space exploration in area, power dissipation, and execution speed between RNA and three other implementations representative of different reusable hardware strategies is presented and discussed. Compared with an equivalent software implementation in C executed on an embedded microprocessor, the RNA ASIC achieves three orders of magnitude improvements in both the execution speed and the energy efficiency. Copyright © 2012 Elsevier Ltd. All rights reserved.

Program Helps Simulate Neural Networks

NASA Technical Reports Server (NTRS)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

49 CFR 38.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 1 2011-10-01 2011-10-01 false High-speed rail cars, monorails and systems. 38....175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including... for high-platform, level boarding and shall comply with § 38.111(a) of this part for each type of car...

49 CFR 38.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 1 2014-10-01 2014-10-01 false High-speed rail cars, monorails and systems. 38....175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including... for high-platform, level boarding and shall comply with § 38.111(a) of this part for each type of car...

49 CFR 38.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 1 2013-10-01 2013-10-01 false High-speed rail cars, monorails and systems. 38....175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including... for high-platform, level boarding and shall comply with § 38.111(a) of this part for each type of car...

49 CFR 38.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 1 2010-10-01 2010-10-01 false High-speed rail cars, monorails and systems. 38....175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including... for high-platform, level boarding and shall comply with § 38.111(a) of this part for each type of car...

49 CFR 38.175 - High-speed rail cars, monorails and systems.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 1 2012-10-01 2012-10-01 false High-speed rail cars, monorails and systems. 38....175 High-speed rail cars, monorails and systems. (a) All cars for high-speed rail systems, including... for high-platform, level boarding and shall comply with § 38.111(a) of this part for each type of car...

High Speed Balancing Applied to the T700 Engine

NASA Technical Reports Server (NTRS)

Walton, J.; Lee, C.; Martin, M.

1989-01-01

The work performed under Contracts NAS3-23929 and NAS3-24633 is presented. MTI evaluated the feasibility of high-speed balancing for both the T700 power turbine rotor and the compressor rotor. Modifications were designed for the existing Corpus Christi Army Depot (CCAD) T53/T55 high-speed balancing system for balancing T700 power turbine rotors. Tests conducted under these contracts included a high-speed balancing evaluation for T700 power turbines in the Army/NASA drivetrain facility at MTI. The high-speed balancing tests demonstrated the reduction of vibration amplitudes at operating speed for both low-speed balanced and non-low-speed balanced T700 power turbines. In addition, vibration data from acceptance tests of T53, T55, and T700 engines were analyzed and a vibration diagnostic procedure developed.

Large grain instruction and phonological awareness skill influence rime sensitivity, processing speed, and early decoding skill in adult L2 learners

PubMed Central

Brennan, Christine; Booth, James R.

2016-01-01

Linguistic knowledge, cognitive ability, and instruction influence how adults acquire a second orthography yet it remains unclear how different forms of instruction influence grain size sensitivity and subsequent decoding skill and speed. Thirty-seven monolingual, literate English-speaking adults were trained on a novel artificial orthography given initial instruction that directed attention to either large or small grain size units (i.e., words or letters). We examined how initial instruction influenced processing speed (i.e., reaction time (RT)) and sensitivity to different orthographic grain sizes (i.e., rimes and letters). Directing attention to large grain size units during initial instruction resulted in higher accuracy for rimes, whereas directing attention to smaller grain size units resulted in slower RTs across all measures. Additionally, phonological awareness skill modulated early learning effects, compensating for the limitations of the initial instruction provided. Collectively, these findings suggest that when adults are learning to read a second orthography, consideration should be given to how initial instruction directs attention to different grain sizes and inherent phonological awareness ability. PMID:27829705

Is Romantic Desire Predictable? Machine Learning Applied to Initial Romantic Attraction.

PubMed

Joel, Samantha; Eastwick, Paul W; Finkel, Eli J

2017-10-01

Matchmaking companies and theoretical perspectives on close relationships suggest that initial attraction is, to some extent, a product of two people's self-reported traits and preferences. We used machine learning to test how well such measures predict people's overall tendencies to romantically desire other people (actor variance) and to be desired by other people (partner variance), as well as people's desire for specific partners above and beyond actor and partner variance (relationship variance). In two speed-dating studies, romantically unattached individuals completed more than 100 self-report measures about traits and preferences that past researchers have identified as being relevant to mate selection. Each participant met each opposite-sex participant attending a speed-dating event for a 4-min speed date. Random forests models predicted 4% to 18% of actor variance and 7% to 27% of partner variance; crucially, however, they were unable to predict relationship variance using any combination of traits and preferences reported before the dates. These results suggest that compatibility elements of human mating are challenging to predict before two people meet.

Spatio-Temporal Simulation and Analysis of Regional Ecological Security Based on Lstm

NASA Astrophysics Data System (ADS)

Gong, C.; Qi, L.; Heming, L.; Karimian, H.; Yuqin, M.

2017-10-01

Region is a complicated system, where human, nature and society interact and influence. Quantitative modeling and simulation of ecology in the region are the key to realize the strategy of regional sustainable development. Traditional machine learning methods have made some achievements in the modeling of regional ecosystems, but it is difficult to determine the learning characteristics and to realize spatio-temporal simulation. Deep learning does not need prior identification of training characteristics, have excellent feature learning ability, can improve the accuracy of model prediction, so the use of deep learning model has a significant advantage. Therefore, we use net primary productivity (NPP), atmospheric optical depth (AOD), moderate-resolution imaging spectrometer (MODIS), Normalized Difference Vegetation Index (NDVI), landcover and population data, and use LSTM to do spatio-temporal simulation. We conduct spatial analysis and driving force analysis. The conclusions are as follows: the ecological deficit of northwestern Henan and urban communities such as Zhengzhou is higher. The reason of former lies in the weak land productivity of the Loess Plateau, the irrational crop cultivation mode. The latter lies in the high consumption of resources in the large urban agglomeration; The positive trend of Henan ecological development from 2013 is mainly due to the effective environmental protection policy in the 12th five-year plan; The main driver of the sustained ecological deficit growth of Henan in 2004-2013 is high-speed urbanization, increasing population and goods consumption. This article provides relevant basic scientific support and reference for the regional ecological scientific management and construction.

NASA/FAA Tailplane Icing Program: Flight Test Report

NASA Technical Reports Server (NTRS)

Ratvasky, Thomas P.; VanZante, Judith Foss; Sim, Alex

2000-01-01

This report presents results from research flights that explored the characteristics of an ice-contaminated tailplane using various simulated ice shapes attached to the leading edge of the horizontal tailplane. A clean leading edge provided the baseline case, then three ice shapes were flown in order of increasing severity. Flight tests included both steady state and dynamic maneuvers. The steady state points were 1G wings level and steady heading sideslips. The primary dynamic maneuvers were pushovers to various G-levels; elevator doublets; and thrust transitions. These maneuvers were conducted for a full range of flap positions and aircraft angle of attack where possible. The analysis of this data set has clearly demonstrated the detrimental effects of ice contamination on aircraft stability and controllability. Paths to tailplane stall were revealed through parameter isolation and transition studies. These paths are (1) increasing ice shape severity, (2) increasing flap deflection, (3) high or low speeds, depending on whether the aircraft is in a steady state (high speed) or pushover maneuver (low speed), and (4) increasing thrust. The flight research effort was very comprehensive, but did not examine effects of tailplane design and location, or other aircraft geometry configuration effects. However, this effort provided the role of some of the parameters in promoting tailplane stall. The lessons learned will provide guidance to regulatory agencies, aircraft manufacturers, and operators on ice-contaminated tailplane stall in the effort to increase aviation safety and reduce the fatal accident rate.

Development of a driving method suitable for ultrahigh-speed shooting in a 2M-fps 300k-pixel single-chip color camera

NASA Astrophysics Data System (ADS)

Yonai, J.; Arai, T.; Hayashida, T.; Ohtake, H.; Namiki, J.; Yoshida, T.; Etoh, T. Goji

2012-03-01

We have developed an ultrahigh-speed CCD camera that can capture instantaneous phenomena not visible to the human eye and impossible to capture with a regular video camera. The ultrahigh-speed CCD was specially constructed so that the CCD memory between the photodiode and the vertical transfer path of each pixel can store 144 frames each. For every one-frame shot, the electric charges generated from the photodiodes are transferred in one step to the memory of all the parallel pixels, making ultrahigh-speed shooting possible. Earlier, we experimentally manufactured a 1M-fps ultrahigh-speed camera and tested it for broadcasting applications. Through those tests, we learned that there are cases that require shooting speeds (frame rate) of more than 1M fps; hence we aimed to develop a new ultrahigh-speed camera that will enable much faster shooting speeds than what is currently possible. Since shooting at speeds of more than 200,000 fps results in decreased image quality and abrupt heating of the image sensor and drive circuit board, faster speeds cannot be achieved merely by increasing the drive frequency. We therefore had to improve the image sensor wiring layout and the driving method to develop a new 2M-fps, 300k-pixel ultrahigh-speed single-chip color camera for broadcasting purposes.

Accelerating k-NN Algorithm with Hybrid MPI and OpenSHMEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Jian; Hamidouche, Khaled; Zheng, Jie

2015-08-05

Machine Learning algorithms are benefiting from the continuous improvement of programming models, including MPI, MapReduce and PGAS. k-Nearest Neighbors (k-NN) algorithm is a widely used machine learning algorithm, applied to supervised learning tasks such as classification. Several parallel implementations of k-NN have been proposed in the literature and practice. However, on high-performance computing systems with high-speed interconnects, it is important to further accelerate existing designs of the k-NN algorithm through taking advantage of scalable programming models. To improve the performance of k-NN on large-scale environment with InfiniBand network, this paper proposes several alternative hybrid MPI+OpenSHMEM designs and performs a systemicmore » evaluation and analysis on typical workloads. The hybrid designs leverage the one-sided memory access to better overlap communication with computation than the existing pure MPI design, and propose better schemes for efficient buffer management. The implementation based on k-NN program from MaTEx with MVAPICH2-X (Unified MPI+PGAS Communication Runtime over InfiniBand) shows up to 9.0% time reduction for training KDD Cup 2010 workload over 512 cores, and 27.6% time reduction for small workload with balanced communication and computation. Experiments of running with varied number of cores show that our design can maintain good scalability.« less

Delayed Majority Game with Heterogeneous Learning Speeds for Financial Markets

NASA Astrophysics Data System (ADS)

Yoshimura, Yushi; Yamada, Kenta

There are two famous statistical laws, so-called stylized facts, in financial markets. One is fat tail where the tail of price returns obeys a power law. The other is volatility clustering in which the autocorrelation function of absolute price returns decays with a power law. In order to understand relationships between the stylized facts and dealers' behaviors, we constructed a new agent-based model based on the grand canonical minority game (GCMG) and the Giardina-Bouchaud (GB) model. The recovery of stylized facts by GCMG and GB lacks of robustness. Therefore, based on the GCMG and GB model, we develop a new model that can reproduce stylized facts robustly. Furthermore, we find that heterogeneity of learning speeds of agents is important to reproduce the stylized facts.

Real-time mandibular angle reduction surgical simulation with haptic rendering.

PubMed

Wang, Qiong; Chen, Hui; Wu, Wen; Jin, Hai-Yang; Heng, Pheng-Ann

2012-11-01

Mandibular angle reduction is a popular and efficient procedure widely used to alter the facial contour. The primary surgical instruments, the reciprocating saw and the round burr, employed in the surgery have a common feature: operating at a high-speed. Generally, inexperienced surgeons need a long-time practice to learn how to minimize the risks caused by the uncontrolled contacts and cutting motions in manipulation of instruments with high-speed reciprocation or rotation. A virtual reality-based surgical simulator for the mandibular angle reduction was designed and implemented on a CUDA-based platform in this paper. High-fidelity visual and haptic feedbacks are provided to enhance the perception in a realistic virtual surgical environment. The impulse-based haptic models were employed to simulate the contact forces and torques on the instruments. It provides convincing haptic sensation for surgeons to control the instruments under different reciprocation or rotation velocities. The real-time methods for bone removal and reconstruction during surgical procedures have been proposed to support realistic visual feedbacks. The simulated contact forces were verified by comparing against the actual force data measured through the constructed mechanical platform. An empirical study based on the patient-specific data was conducted to evaluate the ability of the proposed system in training surgeons with various experiences. The results confirm the validity of our simulator.

Scaling Deep Learning on GPU and Knights Landing clusters

DOE PAGES

You, Yang; Buluc, Aydin; Demmel, James

2017-09-26

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learningmore » systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \\textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.« less

Scaling Deep Learning on GPU and Knights Landing clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

You, Yang; Buluc, Aydin; Demmel, James

The speed of deep neural networks training has become a big bottleneck of deep learning research and development. For example, training GoogleNet by ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training process, the current deep learning systems heavily rely on the hardware accelerators. However, these accelerators have limited on-chip memory compared with CPUs. To handle large datasets, they need to fetch data from either CPU memory or remote processors. We use both self-hosted Intel Knights Landing (KNL) clusters and multi-GPU clusters as our target platforms. From an algorithm aspect, current distributed machine learningmore » systems are mainly designed for cloud systems. These methods are asynchronous because of the slow network and high fault-tolerance requirement on cloud systems. We focus on Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original EASGD used round-robin method for communication and updating. The communication is ordered by the machine rank ID, which is inefficient on HPC clusters. First, we redesign four efficient algorithms for HPC systems to improve EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD are faster \\textcolor{black}{than} their existing counterparts (Async SGD, Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design Sync EASGD, which ties for the best performance among all the methods while being deterministic. In addition to the algorithmic improvements, we use some system-algorithm codesign techniques to scale up the algorithms. By reducing the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x speedup over original EASGD on the same platform. We get 91.5% weak scaling efficiency on 4253 KNL cores, which is higher than the state-of-the-art implementation.« less

A Nearest Neighbor Classifier Employing Critical Boundary Vectors for Efficient On-Chip Template Reduction.

PubMed

Xia, Wenjun; Mita, Yoshio; Shibata, Tadashi

2016-05-01

Aiming at efficient data condensation and improving accuracy, this paper presents a hardware-friendly template reduction (TR) method for the nearest neighbor (NN) classifiers by introducing the concept of critical boundary vectors. A hardware system is also implemented to demonstrate the feasibility of using an field-programmable gate array (FPGA) to accelerate the proposed method. Initially, k -means centers are used as substitutes for the entire template set. Then, to enhance the classification performance, critical boundary vectors are selected by a novel learning algorithm, which is completed within a single iteration. Moreover, to remove noisy boundary vectors that can mislead the classification in a generalized manner, a global categorization scheme has been explored and applied to the algorithm. The global characterization automatically categorizes each classification problem and rapidly selects the boundary vectors according to the nature of the problem. Finally, only critical boundary vectors and k -means centers are used as the new template set for classification. Experimental results for 24 data sets show that the proposed algorithm can effectively reduce the number of template vectors for classification with a high learning speed. At the same time, it improves the accuracy by an average of 2.17% compared with the traditional NN classifiers and also shows greater accuracy than seven other TR methods. We have shown the feasibility of using a proof-of-concept FPGA system of 256 64-D vectors to accelerate the proposed method on hardware. At a 50-MHz clock frequency, the proposed system achieves a 3.86 times higher learning speed than on a 3.4-GHz PC, while consuming only 1% of the power of that used by the PC.

Mach 4 Test Results of a Dual-Flowpath, Turbine Based Combined Cycle Inlet

NASA Technical Reports Server (NTRS)

Albertson, Cindy w.; Emami, Saied; Trexler, Carl A.

2006-01-01

An experimental study was conducted to evaluate the performance of a turbine based combined cycle (TBCC) inlet concept, consisting of a low speed turbojet inlet and high speed dual-mode scramjet inlet. The main objectives of the study were (1) to identify any interactions between the low and the high speed inlets during the mode transition phase in which both inlets are operating simultaneously and (2) to determine the effect of the low speed inlet operation on the performance of the high speed inlet. Tests were conducted at a nominal freestream Mach number of 4 using an 8 percent scale model representing a single module of a TBCC inlet. A flat plate was installed upstream of the model to produce a turbulent boundary layer which simulated the full-scale vehicle forebody boundary layer. A flowmeter/back pressure device, with remote actuation, was attached aft of the high speed inlet isolator to simulate the back pressure resulting from dual-mode scramjet combustion. Results indicate that the inlets did not interact with each other sufficiently to affect inlet operability. Flow spillage resulting from a high speed inlet unstart did not propagate far enough upstream to affect the low speed inlet. Also, a low speed inlet unstart did not cause the high speed inlet to unstart. The low speed inlet improved the performance of the high speed inlet at certain conditions by diverting a portion of the boundary layer generated on the forebody plate.

78 FR 28940 - Environmental Impact Statement for the Atlanta to Charlotte Portion of the Southeast High Speed...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-16

... the Atlanta to Charlotte Portion of the Southeast High Speed Rail Corridor AGENCY: Federal Rail... potential passenger rail improvements between Atlanta, GA and Charlotte, NC, along the Southeast High-Speed... federal High-Speed Intercity Passenger Rail (HSIPR) program and includes the development of a Passenger...

75 FR 417 - Certificate of Alternative Compliance for the High Speed Ferry SUSITNA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-05

... Compliance for the High Speed Ferry SUSITNA AGENCY: Coast Guard, DHS. ACTION: Notice. SUMMARY: The Coast Guard announces that a Certificate of Alternative Compliance was issued for the high speed ferry SUSITNA... been issued for the high speed ferry SUSITNA, O.N. 1189367. Full compliance with 72 COLREGS and the...

The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

PubMed

Oyana, Tonny J; Achenie, Luke E K; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.

The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data

PubMed Central

Oyana, Tonny J.; Achenie, Luke E. K.; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM. PMID:22481977

AGARD Index of Publications 1983-1985

DTIC Science & Technology

1987-06-01

a high performance high speed General Aviation propeller the advent of the highly loaded program...distribution data at high speed and CLmax data at low speed are NS3-3036# Saab-.;cania, Linkoping (Sweden). described. A flight wing pressure survey which...also well with predictions based on wind tunnel data. flight at high speed and wind tunnel measurements on a half Reynolds Number and transition

Full-frame, high-speed 3D shape and deformation measurements using stereo-digital image correlation and a single color high-speed camera

NASA Astrophysics Data System (ADS)

Yu, Liping; Pan, Bing

2017-08-01

Full-frame, high-speed 3D shape and deformation measurement using stereo-digital image correlation (stereo-DIC) technique and a single high-speed color camera is proposed. With the aid of a skillfully designed pseudo stereo-imaging apparatus, color images of a test object surface, composed of blue and red channel images from two different optical paths, are recorded by a high-speed color CMOS camera. The recorded color images can be separated into red and blue channel sub-images using a simple but effective color crosstalk correction method. These separated blue and red channel sub-images are processed by regular stereo-DIC method to retrieve full-field 3D shape and deformation on the test object surface. Compared with existing two-camera high-speed stereo-DIC or four-mirror-adapter-assisted singe-camera high-speed stereo-DIC, the proposed single-camera high-speed stereo-DIC technique offers prominent advantages of full-frame measurements using a single high-speed camera but without sacrificing its spatial resolution. Two real experiments, including shape measurement of a curved surface and vibration measurement of a Chinese double-side drum, demonstrated the effectiveness and accuracy of the proposed technique.

Analysis of optical route in a micro high-speed magneto-optic switch

NASA Astrophysics Data System (ADS)

Weng, Zihua; Yang, Guoguang; Huang, Yuanqing; Chen, Zhimin; Zhu, Yun; Wu, Jinming; Lin, Shufen; Mo, Weiping

2005-02-01

A novel micro high-speed 2x2 magneto-optic switch and its optical route, which is used in high-speed all-optical communication network, is designed and analyzed in this paper. The study of micro high-speed magneto-optic switch mainly involves the optical route and high-speed control technique design. The optical route design covers optical route design of polarization in optical switch, the performance analysis and material selection of magneto-optic crystal and magnetic path design in Faraday rotator. The research of high-speed control technique involves the study of nanosecond pulse generator, high-speed magnetic field and its control technique etc. High-speed current transients from nanosecond pulse generator are used to switch the magnetization of the magneto-optic crystal, which propagates a 1550nm optical beam. The optical route design schemes and electronic circuits of high-speed control technique are both simulated on computer and test by the experiments respectively. The experiment results state that the nanosecond pulse generator can output the pulse with rising edge time 3~35ns, voltage amplitude 10~90V and pulse width 10~100ns. Under the control of CPU singlechip, the optical beam can be stably switched and the switching time is less than 1μs currently.

Meteorology Online.

ERIC Educational Resources Information Center

Kahl, Jonathan D. W.

2001-01-01

Describes an activity to learn about meteorology and weather using the internet. Discusses the National Weather Service (NWS) internet site www.weather.gov. Students examine maximum and minimum daily temperatures, wind speed, and direction. (SAH)

Competitive Processes in Cross-Situational Word Learning

PubMed Central

Yurovsky, Daniel; Yu, Chen; Smith, Linda B.

2013-01-01

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. But the information that learners pick up from these regularities is dependent on their learning mechanism. This paper investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input, and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple scales – both within and across trials/situations –learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is then considered from the perspective of a process-level understanding of cross-situational learning. PMID:23607610

Competitive processes in cross-situational word learning.

PubMed

Yurovsky, Daniel; Yu, Chen; Smith, Linda B

2013-07-01

Cross-situational word learning, like any statistical learning problem, involves tracking the regularities in the environment. However, the information that learners pick up from these regularities is dependent on their learning mechanism. This article investigates the role of one type of mechanism in statistical word learning: competition. Competitive mechanisms would allow learners to find the signal in noisy input and would help to explain the speed with which learners succeed in statistical learning tasks. Because cross-situational word learning provides information at multiple scales-both within and across trials/situations-learners could implement competition at either or both of these scales. A series of four experiments demonstrate that cross-situational learning involves competition at both levels of scale, and that these mechanisms interact to support rapid learning. The impact of both of these mechanisms is considered from the perspective of a process-level understanding of cross-situational learning. Copyright © 2013 Cognitive Science Society, Inc.

Automated tracking of whiskers in videos of head fixed rodents.

PubMed

Clack, Nathan G; O'Connor, Daniel H; Huber, Daniel; Petreanu, Leopoldo; Hires, Andrew; Peron, Simon; Svoboda, Karel; Myers, Eugene W

2012-01-01

We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998% and identifies individual whiskers with 99.997% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px×352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception.

Automated Tracking of Whiskers in Videos of Head Fixed Rodents

PubMed Central

Clack, Nathan G.; O'Connor, Daniel H.; Huber, Daniel; Petreanu, Leopoldo; Hires, Andrew; Peron, Simon; Svoboda, Karel; Myers, Eugene W.

2012-01-01

We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998% and identifies individual whiskers with 99.997% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px×352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception. PMID:22792058

A Quality Improvement Study on Avoidable Stressors and Countermeasures Affecting Surgical Motor Performance and Learning

PubMed Central

Conrad, Claudius; Konuk, Yusuf; Werner, Paul D.; Cao, Caroline G.; Warshaw, Andrew L.; Rattner, David W.; Stangenberg, Lars; Ott, Harald C.; Jones, Daniel B.; Miller, Diane L; Gee, Denise W.

2012-01-01

OBJECTIVE To explore how the two most important components of surgical performance - speed and accuracy - are influenced by different forms of stress and what the impact of music on these factors is. SUMMARY BACKGROUND DATA Based on a recently published pilot study on surgical experts, we designed an experiment examining the effects of auditory stress, mental stress, and music on surgical performance and learning, and then correlated the data psychometric measures to the role of music in a novice surgeon’s life. METHODS 31 surgeons were recruited for a crossover study. Surgeons were randomized to four simple standardized tasks to be performed on the Surgical SIM VR laparoscopic simulator, allowing exact tracking of speed and accuracy. Tasks were performed under a variety of conditions, including silence, dichotic music (auditory stress), defined classical music (auditory relaxation), and mental loading (mental arithmetic tasks). Tasks were performed twice to test for memory consolidation and to accommodate for baseline variability. Performance was correlated to the Brief Musical Experience Questionnaire (MEQ). RESULTS Mental loading influences performance with respect to accuracy, speed, and recall more negatively than does auditory stress. Defined classical music might lead to minimally worse performance initially, but leads to significantly improved memory consolidation. Furthermore, psychologic testing of the volunteers suggests that surgeons with greater musical commitment, measured by the MEQ, perform worse under the mental loading condition. CONCLUSION Mental distraction and auditory stress negatively affect specific components of surgical learning and performance. If used appropriately, classical music may positively affect surgical memory consolidation. It also may be possible to predict surgeons’ performance and learning under stress through psychological tests on the role of music in a surgeon’s life. Further investigation is necessary to determine the cognitive processes behind these correlations. PMID:22584632

Cleveland-Columbus-Cincinnati high-speed rail study

DOT National Transportation Integrated Search

2001-07-01

In the past five years, the evaluation of different high-speed rail (HSR) studies in the Midwest has resulted in a realization that high speed rail, with speeds greater than 110 miles per hour, is too expensive in the short term to be implemented in ...

Laser beam welding quality monitoring system based in high-speed (10 kHz) uncooled MWIR imaging sensors

NASA Astrophysics Data System (ADS)

Linares, Rodrigo; Vergara, German; Gutiérrez, Raúl; Fernández, Carlos; Villamayor, Víctor; Gómez, Luis; González-Camino, Maria; Baldasano, Arturo; Castro, G.; Arias, R.; Lapido, Y.; Rodríguez, J.; Romero, Pablo

2015-05-01

The combination of flexibility, productivity, precision and zero-defect manufacturing in future laser-based equipment are a major challenge that faces this enabling technology. New sensors for online monitoring and real-time control of laserbased processes are necessary for improving products quality and increasing manufacture yields. New approaches to fully automate processes towards zero-defect manufacturing demand smarter heads where lasers, optics, actuators, sensors and electronics will be integrated in a unique compact and affordable device. Many defects arising in laser-based manufacturing processes come from instabilities in the dynamics of the laser process. Temperature and heat dynamics are key parameters to be monitored. Low cost infrared imagers with high-speed of response will constitute the next generation of sensors to be implemented in future monitoring and control systems for laser-based processes, capable to provide simultaneous information about heat dynamics and spatial distribution. This work describes the result of using an innovative low-cost high-speed infrared imager based on the first quantum infrared imager monolithically integrated with Si-CMOS ROIC of the market. The sensor is able to provide low resolution images at frame rates up to 10 KHz in uncooled operation at the same cost as traditional infrared spot detectors. In order to demonstrate the capabilities of the new sensor technology, a low-cost camera was assembled on a standard production laser welding head, allowing to register melting pool images at frame rates of 10 kHz. In addition, a specific software was developed for defect detection and classification. Multiple laser welding processes were recorded with the aim to study the performance of the system and its application to the real-time monitoring of laser welding processes. During the experiments, different types of defects were produced and monitored. The classifier was fed with the experimental images obtained. Self-learning strategies were implemented with very promising results, demonstrating the feasibility of using low-cost high-speed infrared imagers in advancing towards a real-time / in-line zero-defect production systems.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors.

PubMed

Cheung, Kit; Schultz, Simon R; Luk, Wayne

2015-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.

NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

PubMed Central

Cheung, Kit; Schultz, Simon R.; Luk, Wayne

2016-01-01

NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation. PMID:26834542

Task-specificity of unilateral anodal and dual-M1 tDCS effects on motor learning.

PubMed

Karok, Sophia; Fletcher, David; Witney, Alice G

2017-01-08

Task-specific effects of transcranial direct current stimulation (tDCS) on motor learning were investigated in 30 healthy participants. In a sham-controlled, mixed design, participants trained on 3 different motor tasks (Purdue Pegboard Test, Visuomotor Grip Force Tracking Task and Visuomotor Wrist Rotation Speed Control Task) over 3 consecutive days while receiving either unilateral anodal over the right primary motor cortex (M1), dual-M1 or sham stimulation. Retention sessions were administered 7 and 28 days after the end of training. In the Purdue Pegboard Test, both anodal and dual-M1 stimulation reduced average completion time approximately equally, an improvement driven by online learning effects and maintained for about 1 week. The Visuomotor Grip Force Tracking Task and the Visuomotor Wrist Rotation Speed Control Task were associated with an advantage of dual-M1 tDCS in consolidation processes both between training sessions and when testing at long-term retention; both were maintained for at least 1 month. This study demonstrates that M1-tDCS enhances and sustains motor learning with different electrode montages. Stimulation-induced effects emerged at different learning phases across the tasks, which strongly suggests that the influence of tDCS on motor learning is dynamic with respect to the functional recruitment of the distributed motor system at the time of stimulation. Divergent findings regarding M1-tDCS effects on motor learning may partially be ascribed to task-specific consequences and the effects of offline consolidation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Developmental changes in spatial learning in the Morris water-maze in young meadow voles, Microtus pennsylvanicus.

PubMed

Galea, L A; Ossenkopp, K P; Kavaliers, M

1994-01-31

Spatial learning in pre- and postweaning meadow voles, (Microtus pennsylvanicus) was examined in a Morris water-maze task. The learning performance of 10-day-old (preweaning) and 15-, 20- and 25-day-old (postweaning) male and female voles was assessed by measuring the latency to reach a hidden platform by each animal twice a day for 5 days. Voles of all age groups were able to learn the spatial task with Day 10 and Day 15 voles acquiring the task more slowly than did Day 20 and Day 25 voles. There were no significant sex differences in task acquisition in any of the four age groups. In addition, although swimming speed was related to age, with older animals swimming faster than younger ones, differences in swim speed did not account for the faster acquisition by the older animals. These results show that both preweaning and postweaning voles can successfully learn a spatial task. This is in contrast to preweaning laboratory rats which cannot successfully acquire a similar spatial task. These findings indicate that there are species differences in the ontogeny of spatial learning, which are likely related to the ecological and behavioural developmental characteristics of the species. Furthermore, in contrast to the sex difference in water-maze performance obtained in adult, breeding meadow voles who demonstrate a sex difference, there were no significant sex differences in the spatial performance of the juvenile voles. This suggests that sex differences in spatial learning in the meadow vole do not appear until voles reach reproductive adulthood.

Telemedicine: An Application in Search of Users

NASA Technical Reports Server (NTRS)

Khandheria, Bijoy K.

1996-01-01

Telemedicine involves the use of telecommunication technologies as a medium for the provision of medical information and services to consumers at sites that are at a distance from the provider. The concept encompasses everything from the telephone system to high-speed, wide-bandwidth transmission with use of fiberoptics, satellites, or a combination of terrestrial and satellite-communication technologies. The peripheral software could be as simple as a typewriter used to type a letter requesting an opinion or as complex as high-capacity parallel processing computers and imaging devices. Although the definition includes telephone, facsimile, and distance learning, the term "Telemedicine" is currently used as a generic label for remote consultation and diagnosis. Telemedicine is not a medical subspecialty but a facilitator of all medical and surgical specialties.

Do Effort and Reward at Work Predict Changes in Cognitive Function? First Longitudinal Results from the Representative German Socio-Economic Panel

PubMed Central

Riedel, Natalie; Siegrist, Johannes; Wege, Natalia; Loerbroks, Adrian; Angerer, Peter; Li, Jian

2017-01-01

It has been suggested that work characteristics, such as mental demands, job control, and occupational complexity, are prospectively related to cognitive function. However, current evidence on links between psychosocial working conditions and cognitive change over time is inconsistent. In this study, we applied the effort–reward imbalance model that allows to build on previous research on mental demands and to introduce reward-based learning as a principle with beneficial effect on cognitive function. We aimed to investigate whether high effort, high reward, and low over-commitment in 2006 were associated with positive changes in cognitive function in terms of perceptual speed and word fluency (2006–2012), and whether the co-manifestation of high effort and high reward would yield the strongest association. To this end, we used data on 1031 employees who participated in a large and representative study. Multivariate linear regression analyses supported our main hypotheses (separate and combined effects of effort and reward), particularly on changes in perceptual speed, whereas the effects of over-commitment did not reach the level of statistical significance. Our findings extend available knowledge by examining the course of cognitive function over time. If corroborated by further evidence, organization-based measures in the workplace can enrich efforts towards preventing cognitive decline in ageing workforces. PMID:29140258

Effects of Fasting During Ramadan Month on Cognitive Function in Muslim Athletes

PubMed Central

Tian, Ho-Heng; Aziz, Abdul-Rashid; Png, Weileen; Wahid, Mohamed Faizul; Yeo, Donald; Constance Png, Ai-Li

2011-01-01

Purpose Our study aimed to profile the effect of fasting during the Ramadan month on cognitive function in a group of healthy Muslim athletes. Methods Eighteen male athletes underwent computerized neuropsychological testing during (fasting) and after (non-fasting) Ramadan. Diet was standardized, and tests were performed at 0900h and 1600h to characterize potential time-of-day (TOD) interactions. Psychomotor function (processing speed), vigilance (visual attention), visual learning and memory, working memory (executive function), verbal learning and memory were examined. Capillary glucose, body temperature, urine specific gravity, and sleep volume were also recorded. Results Fasting effects were observed for psychomotor function (Cohen's d=1.3, P=0.01) and vigilance (d=0.6, P=0.004), with improved performance at 0900h during fasting; verbal learning and memory was poorer at 1600h (d=-0.8, P=0.03). A TOD effect was present for psychomotor function (d=-0.4, P<0.001), visual learning (d=-0.5, P=0.04), verbal learning and memory (d=-1.3, P=0.001), with poorer performances at 1600h. There was no significant fasting effect on visual learning and working memory. Conclusions Our results show that the effect of fasting on cognition is heterogeneous and domain-specific. Performance in functions requiring sustained rapid responses was better in the morning, declining in the late afternoon, whereas performance in non-speed dependent accuracy measures was more resilient. PMID:22375233

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

2017-10-01

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

33 CFR 84.24 - High-speed craft.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 33 Navigation and Navigable Waters 1 2011-07-01 2011-07-01 false High-speed craft. 84.24 Section... RULES ANNEX I: POSITIONING AND TECHNICAL DETAILS OF LIGHTS AND SHAPES § 84.24 High-speed craft. (a) The masthead light of high-speed craft with a length to breadth ratio of less than 3.0 may be placed at a...

76 FR 71353 - Notice of Availability of the Record of Decision for the DesertXpress Enterprises, LLC High-Speed...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-17

..., LLC High-Speed Passenger Train Project AGENCY: Bureau of Land Management, Interior. ACTION: Notice of... (ROD) for the DesertXpress Enterprises, LLC High-Speed Passenger Train Project (DesertXpress Project...-managed lands to build an Electrical Multiple Unit (EMU) high-speed passenger rail line in compliance with...

78 FR 78507 - California High-Speed Rail Authority-Construction Exemption-In Fresno, Kings, Tulare, and Kern...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-26

...)] California High-Speed Rail Authority--Construction Exemption--In Fresno, Kings, Tulare, and Kern Counties, CA By petition filed on September 26, 2013, California High-Speed Rail Authority (Authority), a state... 49 U.S.C. 10901 for authority to construct an approximately 114-mile high-speed passenger rail line...

76 FR 18298 - Notice of Availability of a Final Environmental Impact Statement for the DesertXpress High-Speed...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-01

... Environmental Impact Statement for the DesertXpress High-Speed Passenger Train Project AGENCY: Federal Railroad... for the DesertXpress High-Speed Passenger Train Project (DesertXpress project). FRA is the Lead Agency... and operation of an interstate high-speed passenger train system between Victorville, California and...

33 CFR 84.24 - High-speed craft.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false High-speed craft. 84.24 Section... RULES ANNEX I: POSITIONING AND TECHNICAL DETAILS OF LIGHTS AND SHAPES § 84.24 High-speed craft. (a) The masthead light of high-speed craft with a length to breadth ratio of less than 3.0 may be placed at a...

Does targeted cognitive training reduce educational disparities in cognitive function among cognitively normal older adults?

PubMed

Clark, Daniel O; Xu, Huiping; Unverzagt, Frederick W; Hendrie, Hugh

2016-07-01

The aim of this study was to investigate educational differences in treatment responses to memory, reasoning, and speed of processing cognitive training relative to no-contact control. Secondary analyses of the Advanced Cognitive Training for Independent and Vital Elderly trial were conducted. Two thousand eight hundred older adults were randomized to memory, reasoning, or speed of processing training or no-contact control. A repeated-measures mixed-effects model was used to investigate immediate post-training and 1-year outcomes with sensitivity analyses out to 10 years. Outcomes were as follows: (1) memory composite of Hopkins Verbal Learning Test, Rey Auditory Verbal Learning Test, and Rivermead Behavioral Memory Test; (2) reasoning composite of letter series, letter sets, and word series; and (3) speed of processing measured using three trials of useful field of view and the digit symbol substitution test. The effects of reasoning and memory training did not differ by educational attainment. The effect of speed of processing training did. Those with fewer than 12 years of education experienced a 50% greater effect on the useful field of view test compared with those with 16 or more years of education. The training advantage for those with fewer than 12 years of education was maintained to 3 years post-training. Older adults with less than a secondary education are at elevated risk of dementia, including Alzheimer's disease. The analyses here indicate that speed of processing training is effective in older adults with low educational attainment. Copyright © 2015 John Wiley & Sons, Ltd.

High-Speed Sealift Technology. Volume 1

DTIC Science & Technology

1998-09-01

performance of high - speed commercial and military sealift ships , in advance of detailed design studies, in order to help define realistic future mission...Therefore, the viability of new High - Speed Sealift (HSS) ships (oceangoing cargo vessels capable of at least 40 kt that are able to onload and offload... propulsion power for dynamically supported concepts) VK = average ship speed for a voyage (i.e., sustained or service speed )

Big Data, Deep Learning and Tianhe-2 at Sun Yat-Sen University, Guangzhou

NASA Astrophysics Data System (ADS)

Yuen, D. A.; Dzwinel, W.; Liu, J.; Zhang, K.

2014-12-01

In this decade the big data revolution has permeated in many fields, ranging from financial transactions, medical surveys and scientific endeavors, because of the big opportunities people see ahead. What to do with all this data remains an intriguing question. This is where computer scientists together with applied mathematicians have made some significant inroads in developing deep learning techniques for unraveling new relationships among the different variables by means of correlation analysis and data-assimilation methods. Deep-learning and big data taken together is a grand challenge task in High-performance computing which demand both ultrafast speed and large memory. The Tianhe-2 recently installed at Sun Yat-Sen University in Guangzhou is well positioned to take up this challenge because it is currently the world's fastest computer at 34 Petaflops. Each compute node of Tianhe-2 has two CPUs of Intel Xeon E5-2600 and three Xeon Phi accelerators. The Tianhe-2 has a very large fast memory RAM of 88 Gigabytes on each node. The system has a total memory of 1,375 Terabytes. All of these technical features will allow very high dimensional (more than 10) problem in deep learning to be explored carefully on the Tianhe-2. Problems in seismology which can be solved include three-dimensional seismic wave simulations of the whole Earth with a few km resolution and the recognition of new phases in seismic wave form from assemblage of large data sets.

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

PubMed

Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

2017-08-01

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

Ongoing behavior predicts perceptual report of interval duration

PubMed Central

Gouvêa, Thiago S.; Monteiro, Tiago; Soares, Sofia; Atallah, Bassam V.; Paton, Joseph J.

2014-01-01

The ability to estimate the passage of time is essential for adaptive behavior in complex environments. Yet, it is not known how the brain encodes time over the durations necessary to explain animal behavior. Under temporally structured reinforcement schedules, animals tend to develop temporally structured behavior, and interval timing has been suggested to be accomplished by learning sequences of behavioral states. If this is true, trial to trial fluctuations in behavioral sequences should be predictive of fluctuations in time estimation. We trained rodents in an duration categorization task while continuously monitoring their behavior with a high speed camera. Animals developed highly reproducible behavioral sequences during the interval being timed. Moreover, those sequences were often predictive of perceptual report from early in the trial, providing support to the idea that animals may use learned behavioral patterns to estimate the duration of time intervals. To better resolve the issue, we propose that continuous and simultaneous behavioral and neural monitoring will enable identification of neural activity related to time perception that is not explained by ongoing behavior. PMID:24672473

Calculated performance, stability and maneuverability of high-speed tilting-prop-rotor aircraft

NASA Technical Reports Server (NTRS)

Johnson, Wayne; Lau, Benton H.; Bowles, Jeffrey V.

1986-01-01

The feasibility of operating tilting-prop-rotor aircraft at high speeds is examined by calculating the performance, stability, and maneuverability of representative configurations. The rotor performance is examined in high-speed cruise and in hover. The whirl-flutter stability of the coupled-wing and rotor motion is calculated in the cruise mode. Maneuverability is examined in terms of the rotor-thrust limit during turns in helicopter configuration. Rotor airfoils, rotor-hub configuration, wing airfoil, and airframe structural weights representing demonstrated advance technology are discussed. Key rotor and airframe parameters are optimized for high-speed performance and stability. The basic aircraft-design parameters are optimized for minimum gross weight. To provide a focus for the calculations, two high-speed tilt-rotor aircraft are considered: a 46-passenger, civil transport and an air-combat/escort fighter, both with design speeds of about 400 knots. It is concluded that such high-speed tilt-rotor aircraft are quite practical.

Technology, the Columbus Effect, and the Third Revolution in Learning

DTIC Science & Technology

2001-03-01

comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction , 1, 117–-175. Rogoff, B. (1990). Apprenticeship in thinking ...elementary school mathematics (Suppes, Fletcher, and Zanotti, 1975). Instructional approaches used in these early programs required computers that cost $2–3...supported by considerations of pace: the speed with which students learn material and reach instructional objectives. Easily adjusted pacing is a

The Edison Responsive Environment Learning System, or the Talking Typewriter Developed by Thomas A. Edison Laboratory, a Subsidiary of McGraw Edison Company.

ERIC Educational Resources Information Center

Sanderson, Barbara A.; Kratochvil, Daniel W.

The "Talking Typewriter" is a computerized electric typewriter with visual and audio capabilities. It was designed to create an environment where learning to read would be a successful, enjoyable experience for the student by allowing him to explore, discover relationships, to progress at his own speed, and to receive feedback. This…

Individual Differences in Learning and Cognitive Abilities

DTIC Science & Technology

1989-09-15

conducted by Sir Francis Galton . Galton’s view of intelligence was that it distinguished those individuals who had genius (e.g., demonstrated by making...genius must have more refined sensory and motor faculties. Thus, Galton argued, intelligence could be measured by assessing constructs such as visual...block number) FIELD GROUP SUB-GROUP Learning, individual differences, cognitive abilities, 05 09 intelligence , skill acquisition, perceptual speed, - i

Development of Young Adults' Fine Motor Skills when Learning to Play Percussion Instruments

ERIC Educational Resources Information Center

Gzibovskis, Talis; Marnauza, Mara

2012-01-01

When playing percussion instruments, the main activity is done with the help of a motion or motor skills; to perform it, developed fine motor skills are necessary: the speed and precision of fingers, hands and palms. The aim of the research was to study and test the development of young adults' fine motor skills while learning to play percussion…

Active Learning in the Classroom: A Muscle Identification Game in a Kinesiology Course

ERIC Educational Resources Information Center

McCarroll, Michele L.; Pohle-Krauza, Rachael J.; Martin, Jennifer L.

2009-01-01

It is often difficult for educators to teach a kinesiology and applied anatomy (KAA) course due to the vast amount of information that students are required to learn. In this study, a convenient sample of students ("class A") from one section of a KAA course played the speed muscle introduction and matching game, which is loosely based off the…

Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

NASA Astrophysics Data System (ADS)

Deo, Ravinesh C.; Şahin, Mehmet

2015-02-01

The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

Aerodynamic Characteristics of Airfoils at High Speeds

NASA Technical Reports Server (NTRS)

Briggs, L J; Hull, G F; Dryden, H L

1925-01-01

This report deals with an experimental investigation of the aerodynamical characteristics of airfoils at high speeds. Lift, drag, and center of pressure measurements were made on six airfoils of the type used by the air service in propeller design, at speeds ranging from 550 to 1,000 feet per second. The results show a definite limit to the speed at which airfoils may efficiently be used to produce lift, the lift coefficient decreasing and the drag coefficient increasing as the speed approaches the speed of sound. The change in lift coefficient is large for thick airfoil sections (camber ratio 0.14 to 0.20) and for high angles of attack. The change is not marked for thin sections (camber ratio 0.10) at low angles of attack, for the speed range employed. At high speeds the center of pressure moves back toward the trailing edge of the airfoil as the speed increases. The results indicate that the use of tip speeds approaching the speed of sound for propellers of customary design involves a serious loss in efficiency.

High-speed optical 3D sensing and its applications

NASA Astrophysics Data System (ADS)

Watanabe, Yoshihiro

2016-12-01

This paper reviews high-speed optical 3D sensing technologies for obtaining the 3D shape of a target using a camera. The focusing speed is from 100 to 1000 fps, exceeding normal camera frame rates, which are typically 30 fps. In particular, contactless, active, and real-time systems are introduced. Also, three example applications of this type of sensing technology are introduced, including surface reconstruction from time-sequential depth images, high-speed 3D user interaction, and high-speed digital archiving.

Changes in self-reported driving intentions and attitudes while learning to drive in Great Britain.

PubMed

Helman, S; Kinnear, N A D; McKenna, F P; Allsop, R E; Horswill, M S

2013-10-01

Novice drivers are overrepresented in traffic collisions, especially in their first year of solo driving. It is widely accepted that some driving behaviours (such as speeding and thrill-seeking) increase risk in this group. Increasingly research is suggesting that attitudes and behavioural intentions held in the pre-driver and learning stage are important in determining later driver behaviour in solo driving. In this study we examine changes in several self-reported attitudes and behavioural intentions across the learning stage in a sample of learner drivers in Great Britain. A sample of 204 learner drivers completed a self-report questionnaire near the beginning of their learning, and then again shortly after they passed their practical driving test. Results showed that self-reported intentions regarding speed choice, perceptions regarding skill level, and intentions regarding thrill-seeking (through driving) became less safe over this time period, while self-reported intentions regarding following distance and overtaking tendency became safer. The results are discussed with reference to models of driver behaviour that focus on task difficulty; it is suggested that the manner in which behind-the-wheel experience relates to the risk measures of interest may be the key determining factor in how these change over the course of learning to drive. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

High Speed Rail (HSR) in the United States

DTIC Science & Technology

2009-12-08

Magnetic Levitation ( Maglev ) ...............................................................................................5 High Speed Rail In...commonly referred to as “ maglev .” 6 Passenger Rail Working Group of the National Surface... maglev train in 2003. Because of the greater costs, and relatively minor benefits,11 of operating at extremely high speeds, the top operating speed

Method for upgrading the performance at track transitions for high-speed service : next generation high-speed rail program

DOT National Transportation Integrated Search

2001-09-01

High-speed trains in the speed range of 100 to 160 mph require tracks of nearly perfect geometry and mechanical uniformity, when subjected to moving wheel loads. Therefore, this report briefly describes the remedies being used by various railroads to...

Dynamic balancing of super-critical rotating structures using slow-speed data via parametric excitation

NASA Astrophysics Data System (ADS)

Tresser, Shachar; Dolev, Amit; Bucher, Izhak

2018-02-01

High-speed machinery is often designed to pass several "critical speeds", where vibration levels can be very high. To reduce vibrations, rotors usually undergo a mass balancing process, where the machine is rotated at its full speed range, during which the dynamic response near critical speeds can be measured. High sensitivity, which is required for a successful balancing process, is achieved near the critical speeds, where a single deflection mode shape becomes dominant, and is excited by the projection of the imbalance on it. The requirement to rotate the machine at high speeds is an obstacle in many cases, where it is impossible to perform measurements at high speeds, due to harsh conditions such as high temperatures and inaccessibility (e.g., jet engines). This paper proposes a novel balancing method of flexible rotors, which does not require the machine to be rotated at high speeds. With this method, the rotor is spun at low speeds, while subjecting it to a set of externally controlled forces. The external forces comprise a set of tuned, response dependent, parametric excitations, and nonlinear stiffness terms. The parametric excitation can isolate any desired mode, while keeping the response directly linked to the imbalance. A software controlled nonlinear stiffness term limits the response, hence preventing the rotor to become unstable. These forces warrant sufficient sensitivity required to detect the projection of the imbalance on any desired mode without rotating the machine at high speeds. Analytical, numerical and experimental results are shown to validate and demonstrate the method.

Focalised stimulation using high definition transcranial direct current stimulation (HD-tDCS) to investigate declarative verbal learning and memory functioning.

PubMed

Nikolin, Stevan; Loo, Colleen K; Bai, Siwei; Dokos, Socrates; Martin, Donel M

2015-08-15

Declarative verbal learning and memory are known to be lateralised to the dominant hemisphere and to be subserved by a network of structures, including those located in frontal and temporal regions. These structures support critical components of verbal memory, including working memory, encoding, and retrieval. Their relative functional importance in facilitating declarative verbal learning and memory, however, remains unclear. To investigate the different functional roles of these structures in subserving declarative verbal learning and memory performance by applying a more focal form of transcranial direct current stimulation, "High Definition tDCS" (HD-tDCS). Additionally, we sought to examine HD-tDCS effects and electrical field intensity distributions using computer modelling. HD-tDCS was administered to the left dorsolateral prefrontal cortex (LDLPFC), planum temporale (PT), and left medial temporal lobe (LMTL) to stimulate the hippocampus, during learning on a declarative verbal memory task. Sixteen healthy participants completed a single blind, intra-individual cross-over, sham-controlled study which used a Latin Square experimental design. Cognitive effects on working memory and sustained attention were additionally examined. HD-tDCS to the LDLPFC significantly improved the rate of verbal learning (p=0.03, η(2)=0.29) and speed of responding during working memory performance (p=0.02, η(2)=0.35), but not accuracy (p=0.12, η(2)=0.16). No effect of tDCS on verbal learning, retention, or retrieval was found for stimulation targeted to the LMTL or the PT. Secondary analyses revealed that LMTL stimulation resulted in increased recency (p=0.02, η(2)=0.31) and reduced mid-list learning effects (p=0.01, η(2)=0.39), suggesting an inhibitory effect on learning. HD-tDCS to the LDLPFC facilitates the rate of verbal learning and improved efficiency of working memory may underlie performance effects. This focal method of administrating tDCS has potential for probing and enhancing cognitive functioning. Copyright © 2015 Elsevier Inc. All rights reserved.

Compact type-I coil planet centrifuge for counter-current chromatography

PubMed Central

Yang, Yi; Gu, Dongyu; Liu, Yongqiang; Aisa, Haji Akber; Ito, Yoichiro

2009-01-01

A compact type-I coil planet centrifuge has been developed for performing counter-current chromatography. It has a revolution radius of 10 cm and a column holder height of 5 cm compared with 37 cm and 50 cm in the original prototype, respectively. The reduction in the revolution radius and column length permits application of higher revolution speed and more stable balancing of the rotor which leads us to learn more about its performance and the future potential of type-I coil planet centrifuge. The chromatographic performance of this apparatus was evaluated in terms of retention of the stationary phase (Sf), peak resolution (Rs), theoretical plate (N) and peak retention time (tR). The results of the experiment indicated that increasing the revolution speed slightly improved both the retention of the stationary phase and the peak resolution while the separation time is remarkably shortened to yield an excellent peak resolution at a revolution speed of 800 rpm. With a 12 ml capacity coiled column, DNP-glu, DNP-β-ala and DNP-ala were resolved at Rs of 2.75 and 2.16 within 90 min at a flow rate of 0.4 ml/min. We believe that the compact type-I coil planet centrifuge has a high analytical potential. PMID:20060979

Compact type-I coil planet centrifuge for counter-current chromatography.

PubMed

Yang, Yi; Gu, Dongyu; Liu, Yongqiang; Aisa, Haji Akber; Ito, Yoichiro

2010-02-19

A compact type-I coil planet centrifuge has been developed for performing counter-current chromatography. It has a revolution radius of 10 cm and a column holder height of 5 cm compared with 37 and 50 cm in the original prototype, respectively. The reduction in the revolution radius and column length permits application of higher revolution speed and more stable balancing of the rotor which leads us to learn more about its performance and the future potential of type-I coil planet centrifuge. The chromatographic performance of this apparatus was evaluated in terms of retention of the stationary phase (S(f)), peak resolution (R(s)), theoretical plate (N) and peak retention time (t(R)). The results of the experiment indicated that increasing the revolution speed slightly improved both the retention of the stationary phase and the peak resolution while the separation time is remarkably shortened to yield an excellent peak resolution at a revolution speed of 800 rpm. With a 12 ml capacity coiled column, DNP-DL-glu, DNP-beta-ala and DNP-l-ala were resolved at R(s) of 2.75 and 2.16 within 90 min at a flow rate of 0.4 ml/min. We believe that the compact type-I coil planet centrifuge has a high analytical potential. Published by Elsevier B.V.

The development of newborn object recognition in fast and slow visual worlds

PubMed Central

Wood, Justin N.; Wood, Samantha M. W.

2016-01-01

Object recognition is central to perception and cognition. Yet relatively little is known about the environmental factors that cause invariant object recognition to emerge in the newborn brain. Is this ability a hardwired property of vision? Or does the development of invariant object recognition require experience with a particular kind of visual environment? Here, we used a high-throughput controlled-rearing method to examine whether newborn chicks (Gallus gallus) require visual experience with slowly changing objects to develop invariant object recognition abilities. When newborn chicks were raised with a slowly rotating virtual object, the chicks built invariant object representations that generalized across novel viewpoints and rotation speeds. In contrast, when newborn chicks were raised with a virtual object that rotated more quickly, the chicks built viewpoint-specific object representations that failed to generalize to novel viewpoints and rotation speeds. Moreover, there was a direct relationship between the speed of the object and the amount of invariance in the chick's object representation. Thus, visual experience with slowly changing objects plays a critical role in the development of invariant object recognition. These results indicate that invariant object recognition is not a hardwired property of vision, but is learned rapidly when newborns encounter a slowly changing visual world. PMID:27097925

Development of a 300,000-pixel ultrahigh-speed high-sensitivity CCD

NASA Astrophysics Data System (ADS)

Ohtake, H.; Hayashida, T.; Kitamura, K.; Arai, T.; Yonai, J.; Tanioka, K.; Maruyama, H.; Etoh, T. Goji; Poggemann, D.; Ruckelshausen, A.; van Kuijk, H.; Bosiers, Jan T.

2006-02-01

We are developing an ultrahigh-speed, high-sensitivity broadcast camera that is capable of capturing clear, smooth slow-motion videos even where lighting is limited, such as at professional baseball games played at night. In earlier work, we developed an ultrahigh-speed broadcast color camera1) using three 80,000-pixel ultrahigh-speed, highsensitivity CCDs2). This camera had about ten times the sensitivity of standard high-speed cameras, and enabled an entirely new style of presentation for sports broadcasts and science programs. Most notably, increasing the pixel count is crucially important for applying ultrahigh-speed, high-sensitivity CCDs to HDTV broadcasting. This paper provides a summary of our experimental development aimed at improving the resolution of CCD even further: a new ultrahigh-speed high-sensitivity CCD that increases the pixel count four-fold to 300,000 pixels.

Lubrication of optimized-design tapered-roller bearings to 2.4 million DN

NASA Technical Reports Server (NTRS)

Parker, R. J.; Pinel, S. I.; Signer, Hans R.

1980-01-01

The performance of 120.65 mm (4.75 in.) bore high speed design, tapered roller bearings was investigated at shaft speeds to 20,000 rpm (2.4 million DN) under combined thrust and radial load. The test bearing design was computer optimized for high speed operation. Temperature distribution bearing heat generation were determined as a function of shaft speed, radial and thrust loads, lubricant flow rates, and lubricant inlet temperature. The high speed design, tapered roller bearing operated successfully at shaft speeds up to 20,000 rpm under heavy thrust and radial loads. Bearing temperatures and heat generation with the high speed design bearing were significantly less than those of a modified standard bearing tested previously. Cup cooling was effective in decreasing the high cup temperatures to levels equal to the cone temperature.

Two laboratory methods for the calibration of GPS speed meters

NASA Astrophysics Data System (ADS)

Bai, Yin; Sun, Qiao; Du, Lei; Yu, Mei; Bai, Jie

2015-01-01

The set-ups of two calibration systems are presented to investigate calibration methods of GPS speed meters. The GPS speed meter calibrated is a special type of high accuracy speed meter for vehicles which uses Doppler demodulation of GPS signals to calculate the measured speed of a moving target. Three experiments are performed: including simulated calibration, field-test signal replay calibration, and in-field test comparison with an optical speed meter. The experiments are conducted at specific speeds in the range of 40-180 km h-1 with the same GPS speed meter as the device under calibration. The evaluation of measurement results validates both methods for calibrating GPS speed meters. The relative deviations between the measurement results of the GPS-based high accuracy speed meter and those of the optical speed meter are analyzed, and the equivalent uncertainty of the comparison is evaluated. The comparison results justify the utilization of GPS speed meters as reference equipment if no fewer than seven satellites are available. This study contributes to the widespread use of GPS-based high accuracy speed meters as legal reference equipment in traffic speed metrology.

High-speed high-stress ring shear tests on granular sods and clayey soils

Treesearch

Hiroshi Fukuoka; Kyoji Sassa

1991-01-01

The purposes of this study is to obtain exact knowledge of the influences on friction angle during shear by shearing speeds. Ring shear tests on sandy and clayey materials have been carried out with a newly developed High-speed High-Stress Ring Shear Apparatus to examine if there are some changes in the frictional behaviors of these materials at high shearing speeds of...

Fundamental Structure of High-Speed Reacting Flows: Supersonic Combustion and Detonation

DTIC Science & Technology

2016-04-30

AFRL-AFOSR-VA-TR-2016-0195 Fundamental Structure of High-Speed Reacting Flows: Supersonic Combustion and Detonation Kenneth Yu MARYLAND UNIV COLLEGE...MARCH 2016 4. TITLE AND SUBTITLE FUNDAMENTAL STRUCTURE OF HIGH-SPEED REACTING FLOWS: SUPERSONIC COMBUSTION AND DETONATION 5a. CONTRACT NUMBER...public release. Final Report on Fundamental Structure of High-Speed Reacting Flows: Supersonic Combustion and Detonation Grant

An error reduction algorithm to improve lidar turbulence estimates for wind energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Newman, Jennifer F.; Clifton, Andrew

Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidarsmore » in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.« less

An error reduction algorithm to improve lidar turbulence estimates for wind energy

DOE PAGES

Newman, Jennifer F.; Clifton, Andrew

2017-02-10

Remote-sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers for the measurement of wind speed and direction. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, vertically profiling lidarsmore » in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The Lidar Turbulence Error Reduction Algorithm, L-TERRA, can be applied using only data from a stand-alone vertically profiling lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of physics-based corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. The lessons learned from creating the L-TERRA model for a WINDCUBE v2 lidar can also be applied to other lidar devices. L-TERRA was tested on data from two sites in the Southern Plains region of the United States. The physics-based corrections in L-TERRA brought regression line slopes much closer to 1 at both sites and significantly reduced the sensitivity of lidar turbulence errors to atmospheric stability. The accuracy of machine-learning methods in L-TERRA was highly dependent on the input variables and training dataset used, suggesting that machine learning may not be the best technique for reducing lidar turbulence intensity (TI) error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.« less

Perils of using speed zone data to assess real-world compliance to speed limits.

PubMed

Chevalier, Anna; Clarke, Elizabeth; Chevalier, Aran John; Brown, Julie; Coxon, Kristy; Ivers, Rebecca; Keay, Lisa

2017-11-17

Real-world driving studies, including those involving speeding alert devices and autonomous vehicles, can gauge an individual vehicle's speeding behavior by comparing measured speed with mapped speed zone data. However, there are complexities with developing and maintaining a database of mapped speed zones over a large geographic area that may lead to inaccuracies within the data set. When this approach is applied to large-scale real-world driving data or speeding alert device data to determine speeding behavior, these inaccuracies may result in invalid identification of speeding. We investigated speeding events based on service provider speed zone data. We compared service provider speed zone data (Speed Alert by Smart Car Technologies Pty Ltd., Ultimo, NSW, Australia) against a second set of speed zone data (Google Maps Application Programming Interface [API] mapped speed zones). We found a systematic error in the zones where speed limits of 50-60 km/h, typical of local roads, were allocated to high-speed motorways, which produced false speed limits in the speed zone database. The result was detection of false-positive high-range speeding. Through comparison of the service provider speed zone data against a second set of speed zone data, we were able to identify and eliminate data most affected by this systematic error, thereby establishing a data set of speeding events with a high level of sensitivity (a true positive rate of 92% or 6,412/6,960). Mapped speed zones can be a source of error in real-world driving when examining vehicle speed. We explored the types of inaccuracies found within speed zone data and recommend that a second set of speed zone data be utilized when investigating speeding behavior or developing mapped speed zone data to minimize inaccuracy in estimates of speeding.

Retrofit device and method to improve humidity control of vapor compression cooling systems

DOEpatents

Roth, Robert Paul; Hahn, David C.; Scaringe, Robert P.

2016-08-16

A method and device for improving moisture removal capacity of a vapor compression system is disclosed. The vapor compression system is started up with the evaporator blower initially set to a high speed. A relative humidity in a return air stream is measured with the evaporator blower operating at the high speed. If the measured humidity is above the predetermined high relative humidity value, the evaporator blower speed is reduced from the initially set high speed to the lowest possible speed. The device is a control board connected with the blower and uses a predetermined change in measured relative humidity to control the blower motor speed.

Design of noise barrier inspection system for high-speed railway

NASA Astrophysics Data System (ADS)

Liu, Bingqian; Shao, Shuangyun; Feng, Qibo; Ma, Le; Cholryong, Kim

2016-10-01

The damage of noise barriers will highly reduce the transportation safety of the high-speed railway. In this paper, an online inspection system of noise barrier based on laser vision for the safety of high-speed railway is proposed. The inspection system, mainly consisted of a fast camera and a line laser, installed in the first carriage of the high-speed CIT(Composited Inspection Train).A Laser line was projected on the surface of the noise barriers and the images of the light line were received by the camera while the train is running at high speed. The distance between the inspection system and the noise barrier can be obtained based on laser triangulation principle. The results of field tests show that the proposed system can meet the need of high speed and high accuracy to get the contour distortion of the noise barriers.

The Slap Shot in Ice Hockey

NASA Astrophysics Data System (ADS)

Cross, Rod; Lindsey, Crawford

2018-01-01

An ice hockey player can strike a puck at speeds up to about 45 m/s (100 mph) using a technique known as the slap shot. There is nothing unusual about the speed, since golf balls, tennis balls, and baseballs can also be projected at that speed or even higher. The unusual part is that the player strikes the ice before striking the puck, causing the stick to slow down and to bend. If a tennis player or a golfer did something like that, by hitting the ground before hitting the ball, it would be classed as a miss-hit and the ball would probably dribble away at low speed. Nevertheless, there appears to be a significant advantage in hitting the ice before hitting the puck, otherwise hockey players would have learned from experience not to do that.

High speed imaging - An important industrial tool

NASA Technical Reports Server (NTRS)

Moore, Alton; Pinelli, Thomas E.

1986-01-01

High-speed photography, which is a rapid sequence of photographs that allow an event to be analyzed through the stoppage of motion or the production of slow-motion effects, is examined. In high-speed photography 16, 35, and 70 mm film and framing rates between 64-12,000 frames per second are utilized to measure such factors as angles, velocities, failure points, and deflections. The use of dual timing lamps in high-speed photography and the difficulties encountered with exposure and programming the camera and event are discussed. The application of video cameras to the recording of high-speed events is described.

Analysis and topology optimization design of high-speed driving spindle

NASA Astrophysics Data System (ADS)

Wang, Zhilin; Yang, Hai

2018-04-01

The three-dimensional model of high-speed driving spindle is established by using SOLIDWORKS. The model is imported through the interface of ABAQUS, A finite element analysis model of high-speed driving spindle was established by using spring element to simulate bearing boundary condition. High-speed driving spindle for the static analysis, the spindle of the stress, strain and displacement nephogram, and on the basis of the results of the analysis on spindle for topology optimization, completed the lightweight design of high-speed driving spindle. The design scheme provides guidance for the design of axial parts of similar structures.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nai-Yang, S.

The Chinese National Standard for Emission from Vehicles was published in 1983. The most important one is Emission Standards for Pollutants at Idle Speed from Road Vehicles with Petrol Engines. This paper discusses experiences gained and lessons learned since publication.

The Effect of Neurobehavioral Test Performance on the All-Cause Mortality among US Population

PubMed Central

Wu, Li-Wei; Liaw, Fang-Yih; Wang, Gia-Chi; Wang, Chung-Ching

2016-01-01

Evidence of the association between global cognitive function and mortality is much, but whether specific cognitive function is related to mortality is unclear. To address the paucity of knowledge on younger populations in the US, we analyzed the association between specific cognitive function and mortality in young and middle-aged adults. We analyzed data from 5,144 men and women between 20 and 59 years of age in the Third National Health and Nutrition Examination Survey (1988–94) with mortality follow-up evaluation through 2006. Cognitive function tests, including assessments of executive function/processing speed (symbol digit substitution) and learning recall/short-term memory (serial digit learning), were performed. All-cause mortality was the outcome of interest. After adjusting for multiple variables, total mortality was significantly higher in males with poorer executive function/processing speed (hazard ratio (HR) 2.02; 95% confidence interval 1.36 to 2.99) and poorer recall/short-term memory (HR 1.47; 95% confidence interval 1.02 to 2.12). After adjusting for multiple variables, the mortality risk did not significantly increase among the females in these two cognitive tests groups. In this sample of the US population, poorer executive function/processing speed and poorer learning recall/short-term memory were significantly associated with increased mortality rates, especially in males. This study highlights the notion that poorer specific cognitive function predicts all-cause mortality in young and middle-aged males. PMID:27595105

Difference in muscle activation patterns during high-speed versus standard-speed yoga: A randomized sequence crossover study.

PubMed

Potiaumpai, Melanie; Martins, Maria Carolina Massoni; Wong, Claudia; Desai, Trusha; Rodriguez, Roberto; Mooney, Kiersten; Signorile, Joseph F

2017-02-01

To compare the difference in muscle activation between high-speed yoga and standard-speed yoga and to compare muscle activation of the transitions between poses and the held phases of a yoga pose. Randomized sequence crossover trial SETTING: A laboratory of neuromuscular research and active aging Interventions: Eight minutes of continuous Sun Salutation B was performed, at a high speed versus a standard-speed, separately. Electromyography was used to quantify normalized muscle activation patterns of eight upper and lower body muscles (pectoralis major, medial deltoids, lateral head of the triceps, middle fibers of the trapezius, vastus medialis, medial gastrocnemius, thoracic extensor spinae, and external obliques) during the high-speed and standard-speed yoga protocols. Difference in normalized muscle activation between high-speed yoga and standard-speed yoga. Normalized muscle activity signals were significantly higher in all eight muscles during the transition phases of poses compared to the held phases (p<0.01). There was no significant interaction between speed×phase; however, greater normalized muscle activity was seen for highspeed yoga across the entire session. Our results show that transitions from one held phase of a pose to another produces higher normalized muscle activity than the held phases of the poses and that overall activity is greater during highspeed yoga than standard-speed yoga. Therefore, the transition speed and associated number of poses should be considered when targeting specific improvements in performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

High-Lift Systems on Commercial Subsonic Airliners

NASA Technical Reports Server (NTRS)

Rudolph, Peter K. C.

1996-01-01

The early breed of slow commercial airliners did not require high-lift systems because their wing loadings were low and their speed ratios between cruise and low speed (takeoff and landing) were about 2:1. However, even in those days the benefit of high-lift devices was recognized. Simple trailing-edge flaps were in use, not so much to reduce landing speeds, but to provide better glide-slope control without sideslipping the airplane and to improve pilot vision over the nose by reducing attitude during low-speed flight. As commercial-airplane cruise speeds increased with the development of more powerful engines, wing loadings increased and a real need for high-lift devices emerged to keep takeoff and landing speeds within reasonable limits. The high-lift devices of that era were generally trailing-edge flaps. When jet engines matured sufficiently in military service and were introduced commercially, airplane speed capability had to be increased to best take advantage of jet engine characteristics. This speed increase was accomplished by introducing the wing sweep and by further increasing wing loading. Whereas increased wing loading called for higher lift coefficients at low speeds, wing sweep actually decreased wing lift at low speeds. Takeoff and landing speeds increased on early jet airplanes, and, as a consequence, runways worldwide had to be lengthened. There are economical limits to the length of runways; there are safety limits to takeoff and landing speeds; and there are speed limits for tires. So, in order to hold takeoff and landing speeds within reasonable limits, more powerful high-lift devices were required. Wing trailing-edge devices evolved from plain flaps to Fowler flaps with single, double, and even triple slots. Wing leading edges evolved from fixed leading edges to a simple Krueger flap, and from fixed, slotted leading edges to two- and three-position slats and variable-camber (VC) Krueger flaps. The complexity of high-lift systems probably peaked on the Boeing 747, which has a VC Krueger flap and triple-slotted, inboard and outboard trailing-edge flaps. Since then, the tendency in high-lift system development has been to achieve high levels of lift with simpler devices in order to reduce fleet acquisition and maintenance costs. The intent of this paper is to: (1) review available high-lift devices, their functions, and design criteria; (2) appraise high-lift systems presently in service on commercial air liners; (3) present personal study results on high-lift systems; (4) develop a weight and cost model for high-lift systems; and (5) discuss the development tendencies of future high-lift systems.