Background-free millimeter-wave ultra-wideband signal generation based on a dual-parallel Mach-Zehnder modulator.

PubMed

Zhang, Fangzheng; Pan, Shilong

2013-11-04

A novel scheme for photonic generation of a millimeter-wave ultra-wideband (MMW-UWB) signal is proposed and experimentally demonstrated based on a dual-parallel Mach-Zehnder modulator (DPMZM). In the proposed scheme, a single-frequency radio frequency (RF) signal is applied to one sub-MZM of the DPMZM to achieve optical suppressed-carrier modulation, and an electrical control pulse train is applied to the other sub-MZM biased at the minimum transmission point, to get an on/off switchable optical carrier. By filtering out the optical carrier with one of the first-order sidebands, and properly setting the amplitude of the control pulse, an MMW-UWB pulse train without the residual local oscillation is generated after photo-detection. The generated MMW-UWB signal is background-free, because the low-frequency components in the electrical spectrum are effectively suppressed. In the experiment, an MMW-UWB pulse train centered at 25 GHz with a 10-dB bandwidth of 5.5 GHz is successfully generated. The low frequency components are suppressed by 22 dB.

From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild.

PubMed

Asthana, Akshay; Zafeiriou, Stefanos; Tzimiropoulos, Georgios; Cheng, Shiyang; Pantic, Maja

2015-06-01

We propose a face alignment framework that relies on the texture model generated by the responses of discriminatively trained part-based filters. Unlike standard texture models built from pixel intensities or responses generated by generic filters (e.g. Gabor), our framework has two important advantages. First, by virtue of discriminative training, invariance to external variations (like identity, pose, illumination and expression) is achieved. Second, we show that the responses generated by discriminatively trained filters (or patch-experts) are sparse and can be modeled using a very small number of parameters. As a result, the optimization methods based on the proposed texture model can better cope with unseen variations. We illustrate this point by formulating both part-based and holistic approaches for generic face alignment and show that our framework outperforms the state-of-the-art on multiple "wild" databases. The code and dataset annotations are available for research purposes from http://ibug.doc.ic.ac.uk/resources.

Reactor transient control in support of PFR/TREAT TUCOP experiments

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

Burrows, D.R.; Larsen, G.R.; Harrison, L.J.

1984-01-01

Unique energy deposition and experiment control requirements posed bythe PFR/TREAT series of transient undercooling/overpower (TUCOP) experiments resulted in equally unique TREAT reactor operations. New reactor control computer algorithms were written and used with the TREAT reactor control computer system to perform such functions as early power burst generation (based on test train flow conditions), burst generation produced by a step insertion of reactivity following a controlled power ramp, and shutdown (SCRAM) initiators based on both test train conditions and energy deposition. Specialized hardware was constructed to simulate test train inputs to the control computer system so that computer algorithms couldmore » be tested in real time without irradiating the experiment.« less

Novel layered clustering-based approach for generating ensemble of classifiers.

PubMed

Rahman, Ashfaqur; Verma, Brijesh

2011-05-01

This paper introduces a novel concept for creating an ensemble of classifiers. The concept is based on generating an ensemble of classifiers through clustering of data at multiple layers. The ensemble classifier model generates a set of alternative clustering of a dataset at different layers by randomly initializing the clustering parameters and trains a set of base classifiers on the patterns at different clusters in different layers. A test pattern is classified by first finding the appropriate cluster at each layer and then using the corresponding base classifier. The decisions obtained at different layers are fused into a final verdict using majority voting. As the base classifiers are trained on overlapping patterns at different layers, the proposed approach achieves diversity among the individual classifiers. Identification of difficult-to-classify patterns through clustering as well as achievement of diversity through layering leads to better classification results as evidenced from the experimental results.

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers

PubMed Central

2018-01-01

Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods. PMID:29304512

Working with a Competency-Based Training Package: A Contextual Investigation from the Perspective of a Group of TAFE Teachers

ERIC Educational Resources Information Center

Southren, Michael

2015-01-01

Reforms in the Australian vocational education and training (VET) landscape have generated significant interest in the changes to the delivery and nature of formal (off-the-job) training provided by Registered Training Organisations. Existing research has provided valuable insights into the evolving role of teachers, and speculated upon the…

Noise annoyance through railway traffic - a case study

PubMed Central

2014-01-01

This paper describes an assessment of noise caused by railway traffic in a large Latin American city. Measurements were taken of noise levels generated by trains passing through residential neighborhoods with and without blowing their horns. Noise maps were also calculated showing noise pollution generated by the train traffic. In addition - annoyance of the residents - affected by railway noise, was evaluated based on interviews. The measurements indicated that the noise levels generated by the passage of the train with its horn blowing are extremely high, clearly exceeding the daytime limits of equivalent sound pressure level - Leq = 55 dB(A) - established by the municipal laws No 10.625 of the city of Curitiba. The Leq = 45 dB (A) which is the limit for the night period also are exceeded during the passage of trains. The residents reported feeling affected by the noise generated by passing trains, which causes irritability, headaches, poor concentration and insomnia, and 88% of them claimed that nocturnal noise pollution is the most distressing. This study showed that the vast majority of residents surveyed, (69%) believe that the noise of the train can devalue their property. PMID:24401735

Comparison of the ballistic contractile responses generated during microstimulation of single human motor axons with brief irregular and regular stimuli.

PubMed

Leitch, Michael; Macefield, Vaughan G

2017-08-01

Ballistic contractions are induced by brief, high-frequency (60-100 Hz) trains of action potentials in motor axons. During ramp voluntary contractions, human motoneurons exhibit significant discharge variability of ∼20% and have been shown to be advantageous to the neuromuscular system. We hypothesized that ballistic contractions incorporating discharge variability would generate greater isometric forces than regular trains with zero variability. High-impedance tungsten microelectrodes were inserted into human fibular nerve, and single motor axons were stimulated with both irregular and constant-frequency stimuli at mean frequencies ranging from 57.8 to 68.9 Hz. Irregular trains generated significantly greater isometric peak forces than regular trains over identical mean frequencies. The high forces generated by ballistic contractions are not based solely on high frequencies, but rather a combination of high firing rates and discharge irregularity. It appears that irregular ballistic trains take advantage of the "catchlike property" of muscle, allowing augmentation of force. Muscle Nerve 56: 292-297, 2017. © 2016 Wiley Periodicals, Inc.

Analysis of dynamic behavior of multiple-stage planetary gear train used in wind driven generator.

PubMed

Wang, Jungang; Wang, Yong; Huo, Zhipu

2014-01-01

A dynamic model of multiple-stage planetary gear train composed of a two-stage planetary gear train and a one-stage parallel axis gear is proposed to be used in wind driven generator to analyze the influence of revolution speed and mesh error on dynamic load sharing characteristic based on the lumped parameter theory. Dynamic equation of the model is solved using numerical method to analyze the uniform load distribution of the system. It is shown that the load sharing property of the system is significantly affected by mesh error and rotational speed; load sharing coefficient and change rate of internal and external meshing of the system are of obvious difference from each other. The study provides useful theoretical guideline for the design of the multiple-stage planetary gear train of wind driven generator.

Generation of a femtosecond electron microbunch train from a photocathode using twofold Michelson interferometer

NASA Astrophysics Data System (ADS)

Shevelev, M.; Aryshev, A.; Terunuma, N.; Urakawa, J.

2017-10-01

The interest in producing ultrashort electron bunches has risen sharply among scientists working on the design of high-gradient wakefield accelerators. One attractive approach generating electron bunches is to illuminate a photocathode with a train of femtosecond laser pulses. In this paper we describe the design and testing of a laser system for an rf gun based on a commercial titanium-sapphire laser technology. The technology allows the production of four femtosecond laser pulses with a continuously variable pulse delay. We also use the designed system to demonstrate the experimental generation of an electron microbunch train obtained by illuminating a cesium-telluride semiconductor photocathode. We use conventional diagnostics to characterize the electron microbunches produced and confirm that it may be possible to control the main parameter of an electron microbunch train.

Analysis of Dynamic Behavior of Multiple-Stage Planetary Gear Train Used in Wind Driven Generator

PubMed Central

Wang, Jungang; Wang, Yong; Huo, Zhipu

2014-01-01

A dynamic model of multiple-stage planetary gear train composed of a two-stage planetary gear train and a one-stage parallel axis gear is proposed to be used in wind driven generator to analyze the influence of revolution speed and mesh error on dynamic load sharing characteristic based on the lumped parameter theory. Dynamic equation of the model is solved using numerical method to analyze the uniform load distribution of the system. It is shown that the load sharing property of the system is significantly affected by mesh error and rotational speed; load sharing coefficient and change rate of internal and external meshing of the system are of obvious difference from each other. The study provides useful theoretical guideline for the design of the multiple-stage planetary gear train of wind driven generator. PMID:24511295

Stable passive optical clock generation in SOA-based fiber lasers.

PubMed

Wang, Jing-Yun; Lin, Kuei-Huei; Chen, Hou-Ren

2015-02-15

Stable optical pulse trains are obtained from 1.3-μm and 1.5-μm semiconductor optical amplifier (SOA)-based fiber lasers using passive optical technology. The waveforms depend on SOA currents, and the repetition rates can be tuned by varying the relative length of sub-cavities. The output pulse trains of these SOA-based fiber lasers are stable against intracavity polarization adjustment and environmental perturbation. The optical clock generation is explained in terms of mode competition, self-synchronization, and SOA saturation. Without resorting to any active modulation circuits or devices, the technology used here is simple and may find various applications in the future.

Renaming Teaching Practice through Teacher Reflection Using Critical Incidents on a Virtual Training Course

ERIC Educational Resources Information Center

Badia, Antoni; Becerril, Lorena

2016-01-01

This study approaches teacher learning from a dialogical viewpoint where lecturers' voices used in a training course context reflect how lecturers generated new professional discourse. The design of the training course considered the analysis of several critical incidents (CIs) in online teaching. An analytical framework based on lecturers'…

Environmental Education and Training in Brazil. Discussion Paper No. 84.

ERIC Educational Resources Information Center

Bruan, Ricardo

Brazilian environmental education and training experience is recent but has a concrete base of generating awareness among all levels of the population and also aims to train people to protect, control and manage the use of environmental resources. Several visits were made by the author to organizations, enterprises and institutions from federal,…

Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

NASA Astrophysics Data System (ADS)

Laloy, Eric; Hérault, Romain; Jacques, Diederik; Linde, Niklas

2018-01-01

Probabilistic inversion within a multiple-point statistics framework is often computationally prohibitive for high-dimensional problems. To partly address this, we introduce and evaluate a new training-image based inversion approach for complex geologic media. Our approach relies on a deep neural network of the generative adversarial network (GAN) type. After training using a training image (TI), our proposed spatial GAN (SGAN) can quickly generate 2-D and 3-D unconditional realizations. A key characteristic of our SGAN is that it defines a (very) low-dimensional parameterization, thereby allowing for efficient probabilistic inversion using state-of-the-art Markov chain Monte Carlo (MCMC) methods. In addition, available direct conditioning data can be incorporated within the inversion. Several 2-D and 3-D categorical TIs are first used to analyze the performance of our SGAN for unconditional geostatistical simulation. Training our deep network can take several hours. After training, realizations containing a few millions of pixels/voxels can be produced in a matter of seconds. This makes it especially useful for simulating many thousands of realizations (e.g., for MCMC inversion) as the relative cost of the training per realization diminishes with the considered number of realizations. Synthetic inversion case studies involving 2-D steady state flow and 3-D transient hydraulic tomography with and without direct conditioning data are used to illustrate the effectiveness of our proposed SGAN-based inversion. For the 2-D case, the inversion rapidly explores the posterior model distribution. For the 3-D case, the inversion recovers model realizations that fit the data close to the target level and visually resemble the true model well.

Integration of LCoS-SLM and LabVIEW based software to simulate fundamental optics, wave optics, and Fourier optics

NASA Astrophysics Data System (ADS)

Lyu, Bo-Han; Wang, Chen; Tsai, Chun-Wei

2017-08-01

Jasper Display Corp. (JDC) offer high reflectivity, high resolution Liquid Crystal on Silicon - Spatial Light Modulator (LCoS-SLM) which include an associated controller ASIC and LabVIEW based modulation software. Based on this LCoS-SLM, also called Education Kit (EDK), we provide a training platform which includes a series of optical theory and experiments to university students. This EDK not only provides a LabVIEW based operation software to produce Computer Generated Holograms (CGH) to generate some basic diffraction image or holographic image, but also provides simulation software to verity the experiment results simultaneously. However, we believe that a robust LCoSSLM, operation software, simulation software, training system, and training course can help students to study the fundamental optics, wave optics, and Fourier optics more easily. Based on these fundamental knowledges, they could develop their unique skills and create their new innovations on the optoelectronic application in the future.

Self-stabilizing optical clock pulse-train generator using SOA and saturable absorber for asynchronous optical packet processing.

PubMed

Nakahara, Tatsushi; Takahashi, Ryo

2013-05-06

We propose a novel, self-stabilizing optical clock pulse-train generator for processing preamble-free, asynchronous optical packets with variable lengths. The generator is based on an optical loop that includes a semiconductor optical amplifier (SOA) and a high-extinction spin-polarized saturable absorber (SA), with the loop being self-stabilized by balancing out the gain and absorption provided by the SOA and SA, respectively. The optical pulse train is generated by tapping out a small portion of a circulating seed pulse. The convergence of the generated pulse energy is enabled by the loop round-trip gain function that has a negative slope due to gain saturation in the SOA. The amplified spontaneous emission (ASE) of the SOA is effectively suppressed by the SA, and a backward optical pulse launched into the SOA enables overcoming the carrier-recovery speed mismatch between the SOA and SA. Without external control for the loop gain, a stable optical pulse train consisting of more than 50 pulses with low jitter is generated from a single 10-ps seed optical pulse even with a variation of 10 dB in the seed pulse intensity.

Case-based explanation of non-case-based learning methods.

PubMed Central

Caruana, R.; Kangarloo, H.; Dionisio, J. D.; Sinha, U.; Johnson, D.

1999-01-01

We show how to generate case-based explanations for non-case-based learning methods such as artificial neural nets or decision trees. The method uses the trained model (e.g., the neural net or the decision tree) as a distance metric to determine which cases in the training set are most similar to the case that needs to be explained. This approach is well suited to medical domains, where it is important to understand predictions made by complex machine learning models, and where training and clinical practice makes users adept at case interpretation. PMID:10566351

An innovative training program based on virtual reality and treadmill: effects on gait of persons with multiple sclerosis.

PubMed

Peruzzi, Agnese; Zarbo, Ignazio Roberto; Cereatti, Andrea; Della Croce, Ugo; Mirelman, Anat

2017-07-01

In this single blind randomized controlled trial, we examined the effect of a virtual reality-based training on gait of people with multiple sclerosis. Twenty-five individuals with multiple sclerosis with mild to moderate disability were randomly assigned to either the control group (n = 11) or the experimental group (n = 14). The subjects in the control group received treadmill training. Subjects in the experimental group received virtual reality based treadmill training. Clinical measures and gait parameters were evaluated. Subjects in both the groups significantly improved the walking endurance and speed, cadence and stride length, lower limb joint ranges of motion and powers, during single and dual task gait. Moreover, subjects in the experimental group also improved balance, as indicated by the results of the clinical motor tests (p < 0.05). Between-group comparisons revealed that the experimental group improved significantly more than control group in hip range of motion and hip generated power at terminal stance at post-training. Our results support the perceived benefits of training programs that incorporate virtual reality to improve gait measures in individuals with multiple sclerosis. Implication of rehabilitation Gait deficits are common in multiple sclerosis (85%) and worsen during dual task activities. Intensive and progressive treadmill training, with and without virtual reality, is effective on dual task gait in persons with multiple sclerosis. Virtual reality-based treadmill training requiring obstacle negotiation increases the range of motion and the power generated at the hip, consequently allowing longer stride length and, consequently, higher gait speed.

Computer-aided training sensorimotor cortex functions in humans before the upper limb transplantation using virtual reality and sensory feedback.

PubMed

Kurzynski, Marek; Jaskolska, Anna; Marusiak, Jaroslaw; Wolczowski, Andrzej; Bierut, Przemyslaw; Szumowski, Lukasz; Witkowski, Jerzy; Kisiel-Sajewicz, Katarzyna

2017-08-01

One of the biggest problems of upper limb transplantation is lack of certainty as to whether a patient will be able to control voluntary movements of transplanted hands. Based on findings of the recent research on brain cortex plasticity, a premise can be drawn that mental training supported with visual and sensory feedback can cause structural and functional reorganization of the sensorimotor cortex, which leads to recovery of function associated with the control of movements performed by the upper limbs. In this study, authors - based on the above observations - propose the computer-aided training (CAT) system, which generating visual and sensory stimuli, should enhance the effectiveness of mental training applied to humans before upper limb transplantation. The basis for the concept of computer-aided training system is a virtual hand whose reaching and grasping movements the trained patient can observe on the VR headset screen (visual feedback) and whose contact with virtual objects the patient can feel as a touch (sensory feedback). The computer training system is composed of three main components: (1) the system generating 3D virtual world in which the patient sees the virtual limb from the perspective as if it were his/her own hand; (2) sensory feedback transforming information about the interaction of the virtual hand with the grasped object into mechanical vibration; (3) the therapist's panel for controlling the training course. Results of the case study demonstrate that mental training supported with visual and sensory stimuli generated by the computer system leads to a beneficial change of the brain activity related to motor control of the reaching in the patient with bilateral upper limb congenital transverse deficiency. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Impact of Collegiate Aviation Student Learning Styles on Flight Performance: A Scenario-Based Training Approach

ERIC Educational Resources Information Center

Harriman, Stanley L.

2011-01-01

The introduction of the glass cockpit, as well as a whole new generation of high performance general aviation aircraft, highlights the need for a comprehensive overhaul of the traditional approach to training pilots. Collegiate aviation institutions that are interested in upgrading their training aircraft fleets will need to design new curricula…

Efficient generation of image chips for training deep learning algorithms

NASA Astrophysics Data System (ADS)

Han, Sanghui; Fafard, Alex; Kerekes, John; Gartley, Michael; Ientilucci, Emmett; Savakis, Andreas; Law, Charles; Parhan, Jason; Turek, Matt; Fieldhouse, Keith; Rovito, Todd

2017-05-01

Training deep convolutional networks for satellite or aerial image analysis often requires a large amount of training data. For a more robust algorithm, training data need to have variations not only in the background and target, but also radiometric variations in the image such as shadowing, illumination changes, atmospheric conditions, and imaging platforms with different collection geometry. Data augmentation is a commonly used approach to generating additional training data. However, this approach is often insufficient in accounting for real world changes in lighting, location or viewpoint outside of the collection geometry. Alternatively, image simulation can be an efficient way to augment training data that incorporates all these variations, such as changing backgrounds, that may be encountered in real data. The Digital Imaging and Remote Sensing Image Image Generation (DIRSIG) model is a tool that produces synthetic imagery using a suite of physics-based radiation propagation modules. DIRSIG can simulate images taken from different sensors with variation in collection geometry, spectral response, solar elevation and angle, atmospheric models, target, and background. Simulation of Urban Mobility (SUMO) is a multi-modal traffic simulation tool that explicitly models vehicles that move through a given road network. The output of the SUMO model was incorporated into DIRSIG to generate scenes with moving vehicles. The same approach was used when using helicopters as targets, but with slight modifications. Using the combination of DIRSIG and SUMO, we quickly generated many small images, with the target at the center with different backgrounds. The simulations generated images with vehicles and helicopters as targets, and corresponding images without targets. Using parallel computing, 120,000 training images were generated in about an hour. Some preliminary results show an improvement in the deep learning algorithm when real image training data are augmented with the simulated images, especially when obtaining sufficient real data was particularly challenging.

Automatic rule generation for high-level vision

NASA Technical Reports Server (NTRS)

Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

1992-01-01

A new fuzzy set based technique that was developed for decision making is discussed. It is a method to generate fuzzy decision rules automatically for image analysis. This paper proposes a method to generate rule-based approaches to solve problems such as autonomous navigation and image understanding automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Chemical name extraction based on automatic training data generation and rich feature set.

PubMed

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

Dual comb generation from a mode-locked fiber laser with orthogonally polarized interlaced pulses.

PubMed

Akosman, Ahmet E; Sander, Michelle Y

2017-08-07

Ultra-high precision dual-comb spectroscopy traditionally requires two mode-locked, fully stabilized lasers with complex feedback electronics. We present a novel mode-locked operation regime in a thulium-holmium co-doped fiber laser, a frequency-halved state with orthogonally polarized interlaced pulses, for dual comb generation from a single source. In a linear fiber laser cavity, an ultrafast pulse train composed of co-generated, equal intensity and orthogonally polarized consecutive pulses at half of the fundamental repetition rate is demonstrated based on vector solitons. Upon optical interference of the orthogonally polarized pulse trains, two stable microwave RF beat combs are formed, effectively down-converting the optical properties into the microwave regime. These co-generated, dual polarization interlaced pulse trains, from one all-fiber laser configuration with common mode suppression, thus provide an attractive compact source for dual-comb spectroscopy, optical metrology and polarization entanglement measurements.

Training the Next Generation of School Psychologists to Deliver Evidence Based Mental Health Practices: Current Challenges and Future Directions

ERIC Educational Resources Information Center

Shernoff, Elisa S.; Bearman, Sarah Kate; Kratochwill, Thomas R.

2017-01-01

School psychologists are uniquely positioned to support the delivery of evidence-based mental health practices (EBMHPs) to address the overwhelming mental health needs of children and youth. Graduate training programs can promote EBMHPs in schools by ensuring school psychologists enter the workplace prepared to deliver and support high-quality,…

Generative Adversarial Networks for Noise Reduction in Low-Dose CT.

PubMed

Wolterink, Jelmer M; Leiner, Tim; Viergever, Max A; Isgum, Ivana

2017-12-01

Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low-dose CT images and hence reduce noise. A generator CNN was trained to transform low-dose CT images into routine-dose CT images using voxelwise loss minimization. An adversarial discriminator CNN was simultaneously trained to distinguish the output of the generator from routine-dose CT images. The performance of this discriminator was used as an adversarial loss for the generator. Experiments were performed using CT images of an anthropomorphic phantom containing calcium inserts, as well as patient non-contrast-enhanced cardiac CT images. The phantom and patients were scanned at 20% and 100% routine clinical dose. Three training strategies were compared: the first used only voxelwise loss, the second combined voxelwise loss and adversarial loss, and the third used only adversarial loss. The results showed that training with only voxelwise loss resulted in the highest peak signal-to-noise ratio with respect to reference routine-dose images. However, CNNs trained with adversarial loss captured image statistics of routine-dose images better. Noise reduction improved quantification of low-density calcified inserts in phantom CT images and allowed coronary calcium scoring in low-dose patient CT images with high noise levels. Testing took less than 10 s per CT volume. CNN-based low-dose CT noise reduction in the image domain is feasible. Training with an adversarial network improves the CNNs ability to generate images with an appearance similar to that of reference routine-dose CT images.

Generation of bright attosecond x-ray pulse trains via Thomson scattering from laser-plasma accelerators.

PubMed

Luo, W; Yu, T P; Chen, M; Song, Y M; Zhu, Z C; Ma, Y Y; Zhuo, H B

2014-12-29

Generation of attosecond x-ray pulse attracts more and more attention within the advanced light source user community due to its potentially wide applications. Here we propose an all-optical scheme to generate bright, attosecond hard x-ray pulse trains by Thomson backscattering of similarly structured electron beams produced in a vacuum channel by a tightly focused laser pulse. Design parameters for a proof-of-concept experiment are presented and demonstrated by using a particle-in-cell code and a four-dimensional laser-Compton scattering simulation code to model both the laser-based electron acceleration and Thomson scattering processes. Trains of 200 attosecond duration hard x-ray pulses holding stable longitudinal spacing with photon energies approaching 50 keV and maximum achievable peak brightness up to 10²⁰ photons/s/mm²/mrad²/0.1%BW for each micro-bunch are observed. The suggested physical scheme for attosecond x-ray pulse trains generation may directly access the fastest time scales relevant to electron dynamics in atoms, molecules and materials.

Tunable High-Intensity Electron Bunch Train Production Based on Nonlinear Longitudinal Space Charge Oscillation

NASA Astrophysics Data System (ADS)

Zhang, Zhen; Yan, Lixin; Du, Yingchao; Zhou, Zheng; Su, Xiaolu; Zheng, Lianmin; Wang, Dong; Tian, Qili; Wang, Wei; Shi, Jiaru; Chen, Huaibi; Huang, Wenhui; Gai, Wei; Tang, Chuanxiang

2016-05-01

High-intensity trains of electron bunches with tunable picosecond spacing are produced and measured experimentally with the goal of generating terahertz (THz) radiation. By imposing an initial density modulation on a relativistic electron beam and controlling the charge density over the beam propagation, density spikes of several-hundred-ampere peak current in the temporal profile, which are several times higher than the initial amplitudes, have been observed for the first time. We also demonstrate that the periodic spacing of the bunch train can be varied continuously either by tuning launching phase of a radio-frequency gun or by tuning the compression of a downstream magnetic chicane. Narrow-band coherent THz radiation from the bunch train was also measured with μ J -level energies and tunable central frequency of the spectrum in the range of ˜0.5 to 1.6 THz. Our results pave the way towards generating mJ-level narrow-band coherent THz radiation and driving high-gradient wakefield-based acceleration.

Tunable High-Intensity Electron Bunch Train Production Based on Nonlinear Longitudinal Space Charge Oscillation

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

Zhang, Zhen; Yan, Lixin; Du, Yingchao

2016-05-05

High-intensity trains of electron bunches with tunable picosecond spacing are produced and measured experimentally with the goal of generating terahertz (THz) radiation. By imposing an initial density modulation on a relativistic electron beam and controlling the charge density over the beam propagation, density spikes of several-hundred-ampere peak current in the temporal profile, which are several times higher than the initial amplitudes, have been observed for the first time. We also demonstrate that the periodic spacing of the bunch train can be varied continuously either by tuning launching phase of a radiofrequency gun or by tuning the compression of a downstreammore » magnetic chicane. Narrow-band coherent THz radiation from the bunch train was also measured with μJ-level energies and tunable central frequency of the spectrum in the range of ~0.5 to 1.6 THz. Our results pave the way towards generating mJ-level narrow-band coherent THz radiation and driving high-gradient wakefield-based acceleration.« less

The comparative effect of individually-generated vs. collaboratively-generated computer-based concept mapping on science concept learning

NASA Astrophysics Data System (ADS)

Kwon, So Young

Using a quasi-experimental design, the researcher investigated the comparative effects of individually-generated and collaboratively-generated computer-based concept mapping on middle school science concept learning. Qualitative data were analyzed to explain quantitative findings. One hundred sixty-one students (74 boys and 87 girls) in eight, seventh grade science classes at a middle school in Southeast Texas completed the entire study. Using prior science performance scores to assure equivalence of student achievement across groups, the researcher assigned the teacher's classes to one of the three experimental groups. The independent variable, group, consisted of three levels: 40 students in a control group, 59 students trained to individually generate concept maps on computers, and 62 students trained to collaboratively generate concept maps on computers. The dependent variables were science concept learning as demonstrated by comprehension test scores, and quality of concept maps created by students in experimental groups as demonstrated by rubric scores. Students in the experimental groups received concept mapping training and used their newly acquired concept mapping skills to individually or collaboratively construct computer-based concept maps during study time. The control group, the individually-generated concept mapping group, and the collaboratively-generated concept mapping group had equivalent learning experiences for 50 minutes during five days, excepting that students in a control group worked independently without concept mapping activities, students in the individual group worked individually to construct concept maps, and students in the collaborative group worked collaboratively to construct concept maps during their study time. Both collaboratively and individually generated computer-based concept mapping had a positive effect on seventh grade middle school science concept learning but neither strategy was more effective than the other. However, the students who collaboratively generated concept maps created significantly higher quality concept maps than those who individually generated concept maps. The researcher concluded that the concept mapping software, Inspiration(TM), fostered construction of students' concept maps individually or collaboratively for science learning and helped students capture their evolving creative ideas and organize them for meaningful learning. Students in both the individual and the collaborative concept mapping groups had positive attitudes toward concept mapping using Inspiration(TM) software.

Interface Prostheses With Classifier-Feedback-Based User Training.

PubMed

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.

Development of a cloud-based Bioinformatics Training Platform.

PubMed

Revote, Jerico; Watson-Haigh, Nathan S; Quenette, Steve; Bethwaite, Blair; McGrath, Annette; Shang, Catherine A

2017-05-01

The Bioinformatics Training Platform (BTP) has been developed to provide access to the computational infrastructure required to deliver sophisticated hands-on bioinformatics training courses. The BTP is a cloud-based solution that is in active use for delivering next-generation sequencing training to Australian researchers at geographically dispersed locations. The BTP was built to provide an easy, accessible, consistent and cost-effective approach to delivering workshops at host universities and organizations with a high demand for bioinformatics training but lacking the dedicated bioinformatics training suites required. To support broad uptake of the BTP, the platform has been made compatible with multiple cloud infrastructures. The BTP is an open-source and open-access resource. To date, 20 training workshops have been delivered to over 700 trainees at over 10 venues across Australia using the BTP. © The Author 2016. Published by Oxford University Press.

Development of a cloud-based Bioinformatics Training Platform

PubMed Central

Revote, Jerico; Watson-Haigh, Nathan S.; Quenette, Steve; Bethwaite, Blair; McGrath, Annette

2017-01-01

Abstract The Bioinformatics Training Platform (BTP) has been developed to provide access to the computational infrastructure required to deliver sophisticated hands-on bioinformatics training courses. The BTP is a cloud-based solution that is in active use for delivering next-generation sequencing training to Australian researchers at geographically dispersed locations. The BTP was built to provide an easy, accessible, consistent and cost-effective approach to delivering workshops at host universities and organizations with a high demand for bioinformatics training but lacking the dedicated bioinformatics training suites required. To support broad uptake of the BTP, the platform has been made compatible with multiple cloud infrastructures. The BTP is an open-source and open-access resource. To date, 20 training workshops have been delivered to over 700 trainees at over 10 venues across Australia using the BTP. PMID:27084333

Learning to Drive a Wheelchair in Virtual Reality

ERIC Educational Resources Information Center

Inman, Dean P.; Loge, Ken; Cram, Aaron; Peterson, Missy

2011-01-01

This research project studied the effect that a technology-based training program, WheelchairNet, could contribute to the education of children with physical disabilities by providing a chance to practice driving virtual motorized wheelchairs safely within a computer-generated world. Programmers created three virtual worlds for training. Scenarios…

Sustaining Fidelity Following the Nationwide PMTO™ Implementation in Norway

PubMed Central

Forgatch, Marion S.; DeGarmo, David S.

2011-01-01

This report describes three studies from the nationwide Norwegian implementation of Parent Management Training – Oregon Model (PMTO™), an empirically supported treatment for families of children with behavior problems (Forgatch and Patterson 2010). Separate stages of the implementation were evaluated using a fidelity measure based on direct observation of intervention sessions. Study 1 assessed growth in fidelity observed early, mid, and late in the training of a group of practitioners. We hypothesized increased fidelity and decreased variability in practice. Study 2 evaluated method fidelity over the course of three generations of practitioners trained in PMTO. Generation 1 (G1) was trained by the PMTO developer/purveyors; Generation 2 (G2) was trained by selected G1 Norwegian trainers; and Generation 3 (G3) was trained by G1 and G2 trainers. We hypothesized decrease in fidelity with each generation. Study 3 tested the predictive validity of fidelity in a cross-cultural replication, hypothesizing that higher fidelity scores would correlate with improved parenting practices observed in parent-child interactions before and after treatment. In Study 1, trainees' performance improved and became more homogeneous as predicted. In Study 2, a small decline in fidelity followed the transfer from the purveyor trainers to Norwegian trainers in G2, but G3 scores were equivalent to those attained by G1. Thus, the hypothesis was not fully supported. Finally, the FIMP validity model replicated; PMTO fidelity significantly contributed to improvements in parenting practices from pre- to post-treatment. The data indicate that PMTO was transferred successfully to Norwegian implementation with sustained fidelity and cross-cultural generalization. PMID:21671090

Scalability of a Methodology for Generating Technical Trading Rules with GAPs Based on Risk-Return Adjustment and Incremental Training

NASA Astrophysics Data System (ADS)

de La Cal, E. A.; Fernández, E. M.; Quiroga, R.; Villar, J. R.; Sedano, J.

In previous works a methodology was defined, based on the design of a genetic algorithm GAP and an incremental training technique adapted to the learning of series of stock market values. The GAP technique consists in a fusion of GP and GA. The GAP algorithm implements the automatic search for crisp trading rules taking as objectives of the training both the optimization of the return obtained and the minimization of the assumed risk. Applying the proposed methodology, rules have been obtained for a period of eight years of the S&P500 index. The achieved adjustment of the relation return-risk has generated rules with returns very superior in the testing period to those obtained applying habitual methodologies and even clearly superior to Buy&Hold. This work probes that the proposed methodology is valid for different assets in a different market than previous work.

Environmental Assessment for Construction of a Multi-Story Dormitory at Tyndall Air Force Base, Florida

DTIC Science & Technology

2006-11-01

plants ); and recycling and reuse practices. Recyclable waste generated during construction wouJd be recycled according to the type of material ...the Air Force Air Education and Training Command 325th Fighter Wing Tyndall Air Force Base, Florida November 2006 Report Documentation...relies on highly trained , motivated unaccompanied enlisted men and women to support our increasingly technical air and space missions. The retention of

The Human Dimension of Closing the Training Gap for Fifth-Generation Fighters

NASA Technical Reports Server (NTRS)

Hoke, Jaclyn; Postnikov, Alex; Schnell, Thomas

2012-01-01

Based on a review of the recent technical literature there is little question that a serious training gap exists for fifth-generation fighters, primarily arising from the need to provide their own red-air. There are several methods for reducing this gap, including injecting virtual and constructive threats into the live cockpit. This live-virtual-constructive (LVC) training approach provides a cost effective means for addressing training needs but faces several challenges. Technical challenges include data links and information assurance. A more serious challenge may be the human factors dimension of representing virtual and constructive entities in the cockpit while ensuring safety-of-flight. This also needs to happen without increasing pilot workload. This paper discusses the methods Rockwell Collins and the University of Iowa's Operator Performance Lab use to assess pilot workload and training fidelity measures in an LVC training environment and the research we are conducting in safety-of-flight requirements of integrated LVC symbology.

ALOG: A spreadsheet-based program for generating artificial logs

Treesearch

Matthew F. Winn; Randolph H. Wynne; Philip A. Araman

2004-01-01

Log sawing simulation computer programs can be valuable tools for training sawyers as well as for testing different sawing patterns. Most available simulation programs rely on databases from which to draw logs and can be very costly and time-consuming to develop. ALOG (Artificial LOg Generator) is a Microsoft Excel®-based computer program that was developed to...

Novel Television-Based Cognitive Training Improves Working Memory and Executive Function

PubMed Central

Shatil, Evelyn; Mikulecká, Jaroslava; Bellotti, Francesco; Bureš, Vladimír

2014-01-01

The main study objective was to investigate the effect of interactive television-based cognitive training on cognitive performance of 119 healthy older adults, aged 60–87 years. Participants were randomly allocated to a cognitive training group or to an active control group in a single-blind controlled two-group design. Before and after training interactive television cognitive performance was assessed on well validated tests of fluid, higher-order ability, and system usability was evaluated. The participants in the cognitive training group completed a television-based cognitive training programme, while the participants in the active control group completed a TV-based programme of personally benefiting activities. Significant improvements were observed in well validated working memory and executive function tasks in the cognitive training but not in the control group. None of the groups showed statistically significant improvement in life satisfaction score. Participants' reports of “adequate” to “high” system usability testify to the successful development and implementation of the interactive television-based system and compliant cognitive training contents. The study demonstrates that cognitive training delivered by means of an interactive television system can generate genuine cognitive benefits in users and these are measurable using well-validated cognitive tests. Thus, older adults who cannot use or afford a computer can easily use digital interactive television to benefit from advanced software applications designed to train cognition. PMID:24992187

Training in neurology.

PubMed

Aminoff, Michael J

2008-05-13

The training of clinical neurologists is undergoing profound change. Increasing subspecialization within neurology, the widening separation of clinical neurology from other branches of internal medicine, limitations of exposure to training in internal medicine, mandated restrictions in working hours, and attempts to shorten the training period are likely to have adverse effects on the next generation of clinical neurologists. Despite the need for a broad base in general medicine, discussed here, the exposure of neurology trainees to general medical disorders is diminishing. An emphasis on an algorithmic approach to patient management rather than on educating residents to use their reasoning faculties when applying new techniques and knowledge to clinical practice may adversely affect patient care. Neurologists require broad-based training in neurology, internal medicine, and psychiatry, to ensure excellence in clinical practice. It is time to question again whether they are receiving the training that they need.

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

PubMed Central

Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

2017-01-01

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates (F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware. PMID:28208736

Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition.

PubMed

Munoz-Organero, Mario; Ruiz-Blazquez, Ramona

2017-02-08

Body-worn sensors in general and accelerometers in particular have been widely used in order to detect human movements and activities. The execution of each type of movement by each particular individual generates sequences of time series of sensed data from which specific movement related patterns can be assessed. Several machine learning algorithms have been used over windowed segments of sensed data in order to detect such patterns in activity recognition based on intermediate features (either hand-crafted or automatically learned from data). The underlying assumption is that the computed features will capture statistical differences that can properly classify different movements and activities after a training phase based on sensed data. In order to achieve high accuracy and recall rates (and guarantee the generalization of the system to new users), the training data have to contain enough information to characterize all possible ways of executing the activity or movement to be detected. This could imply large amounts of data and a complex and time-consuming training phase, which has been shown to be even more relevant when automatically learning the optimal features to be used. In this paper, we present a novel generative model that is able to generate sequences of time series for characterizing a particular movement based on the time elasticity properties of the sensed data. The model is used to train a stack of auto-encoders in order to learn the particular features able to detect human movements. The results of movement detection using a newly generated database with information on five users performing six different movements are presented. The generalization of results using an existing database is also presented in the paper. The results show that the proposed mechanism is able to obtain acceptable recognition rates ( F = 0.77) even in the case of using different people executing a different sequence of movements and using different hardware.

How Justice Can Affect Jury Training Abstract Words Promotes Generalization to Concrete Words in Patients with Aphasia

PubMed Central

Sandberg, Chaleece; Kiran, Swathi

2014-01-01

Developing language treatments that not only improve trained items but also promote generalization to untrained items is a major focus in aphasia research. This study is a replication and extension of previous work that found that training abstract words in a particular context-category promotes generalization to concrete words but not vice versa (Kiran, Sandberg, & Abbott, 2009). Twelve persons with aphasia (5 female) with varying types and degrees of severity participated in a generative naming treatment based on the complexity account of treatment efficacy (CATE; Thompson, Shapiro, Kiran, & Sobecks, 2003). All participants were trained to generate abstract words in a particular context-category by analyzing the semantic features of the target words. Two other context-categories were used as controls. Ten of the twelve participants improved on the trained abstract words in the trained context-category. Eight of the ten participants who responded to treatment also generalized to concrete words in the same context-category. These results suggest that this treatment is both efficacious and efficient. We discuss possible mechanisms of training and generalization effects. PMID:24805853

Concurrent exercise training: do opposites distract?

PubMed Central

Coffey, Vernon G.

2016-01-01

Abstract Specificity is a core principle of exercise training to promote the desired adaptations for maximising athletic performance. The principle of specificity of adaptation is underpinned by the volume, intensity, frequency and mode of contractile activity and is most evident when contrasting the divergent phenotypes that result after undertaking either prolonged endurance or resistance training. The molecular profiles that generate the adaptive response to different exercise modes have undergone intense scientific scrutiny. Given divergent exercise induces similar signalling and gene expression profiles in skeletal muscle of untrained or recreationally active individuals, what is currently unclear is how the specificity of the molecular response is modified by prior training history. The time course of adaptation and when ‘phenotype specificity’ occurs has important implications for exercise prescription. This context is essential when attempting to concomitantly develop resistance to fatigue (through endurance‐based exercise) and increased muscle mass (through resistance‐based exercise), typically termed ‘concurrent training’. Chronic training studies provide robust evidence that endurance exercise can attenuate muscle hypertrophy and strength but the mechanistic underpinning of this ‘interference’ effect with concurrent training is unknown. Moreover, despite the potential for several key regulators of muscle metabolism to explain an incompatibility in adaptation between endurance and resistance exercise, it now seems likely that multiple integrated, rather than isolated, effectors or processes generate the interference effect. Here we review studies of the molecular responses in skeletal muscle and evidence for the interference effect with concurrent training within the context of the specificity of training adaptation. PMID:27506998

Educating the Next Generation of Energy-Savvy Workforce

ERIC Educational Resources Information Center

Wu, Bin; Abad, Jorge

2014-01-01

This paper reports a problem-based learning model for the training of university students in the area of industrial energy efficiency, and discusses its context, contents, and the results from its implementation. The impact has been significant, with hundreds of university graduates trained and many of them now working in industry, leading their…

Optimizing Decision Preparedness by Adapting Scenario Complexity and Automating Scenario Generation

NASA Technical Reports Server (NTRS)

Dunne, Rob; Schatz, Sae; Flore, Stephen M.; Nicholson, Denise

2011-01-01

Klein's recognition-primed decision (RPD) framework proposes that experts make decisions by recognizing similarities between current decision situations and previous decision experiences. Unfortunately, military personnel arQ often presented with situations that they have not experienced before. Scenario-based training (S8T) can help mitigate this gap. However, SBT remains a challenging and inefficient training approach. To address these limitations, the authors present an innovative formulation of scenario complexity that contributes to the larger research goal of developing an automated scenario generation system. This system will enable trainees to effectively advance through a variety of increasingly complex decision situations and experiences. By adapting scenario complexities and automating generation, trainees will be provided with a greater variety of appropriately calibrated training events, thus broadening their repositories of experience. Preliminary results from empirical testing (N=24) of the proof-of-concept formula are presented, and future avenues of scenario complexity research are also discussed.

Survey of CIG Data Base Generation from Imagery.

DTIC Science & Technology

1980-09-01

world as measured by training transfer. There is no conclusive research as to therequired degree of realism or fidelity necessary to train. In order to...driving force behind emphasizing perceptual fidelity as opposed to realisn is the high cost of realism . Replication of all sensible attri- butes of the...and specification of visual simulation systems will con- tinue to je based on physical fidelity to the real world until those trade-offs on realism

Evaluating an education/training module to foster knowledge of cockpit weather technology.

PubMed

Cobbett, Erin A; Blickensderfer, Elizabeth L; Lanicci, John

2014-10-01

Previous research has indicated that general aviation (GA) pilots may use the sophisticated meteorological information available to them via a variety of Next-Generation Weather Radar (NEXRAD) based weather products in a manner that actually decreases flight safety. The current study examined an education/training method designed to enable GA pilots to use NEXRAD-based products effectively in convective weather situations. The training method was lecture combined with paper-based scenario exercises. A multivariate analysis of variance revealed that subjects in the training condition performed significantly better than did subjects in the control condition on several knowledge and attitude measures. Subjects in the training condition improved from a mean score of 66% to 80% on the radar-knowledge test and from 62% to 75% on the scenario-knowledge test. Although additional research is needed, these results demonstrated that pilots can benefit from a well-designed education/training program involving specific areas of aviation weather-related knowledge.

Vector Quantization Algorithm Based on Associative Memories

NASA Astrophysics Data System (ADS)

Guzmán, Enrique; Pogrebnyak, Oleksiy; Yáñez, Cornelio; Manrique, Pablo

This paper presents a vector quantization algorithm for image compression based on extended associative memories. The proposed algorithm is divided in two stages. First, an associative network is generated applying the learning phase of the extended associative memories between a codebook generated by the LBG algorithm and a training set. This associative network is named EAM-codebook and represents a new codebook which is used in the next stage. The EAM-codebook establishes a relation between training set and the LBG codebook. Second, the vector quantization process is performed by means of the recalling stage of EAM using as associative memory the EAM-codebook. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantages offered by the proposed algorithm is high processing speed and low demand of resources (system memory); results of image compression and quality are presented.

What’s working in working memory training? An educational perspective

PubMed Central

Redick, Thomas S.; Shipstead, Zach; Wiemers, Elizabeth A.; Melby-Lervåg, Monica; Hulme, Charles

2015-01-01

Working memory training programs have generated great interest, with claims that the training interventions can have profound beneficial effects on children’s academic and intellectual attainment. We describe the criteria by which to evaluate evidence for or against the benefit of working memory training. Despite the promising results of initial research studies, the current review of all of the available evidence of working memory training efficacy is less optimistic. Our conclusion is that working memory training produces limited benefits in terms of specific gains on short-term and working memory tasks that are very similar to the training programs, but no advantage for academic and achievement-based reading and arithmetic outcomes. PMID:26640352

Concurrent exercise training: do opposites distract?

PubMed

Coffey, Vernon G; Hawley, John A

2017-05-01

Specificity is a core principle of exercise training to promote the desired adaptations for maximising athletic performance. The principle of specificity of adaptation is underpinned by the volume, intensity, frequency and mode of contractile activity and is most evident when contrasting the divergent phenotypes that result after undertaking either prolonged endurance or resistance training. The molecular profiles that generate the adaptive response to different exercise modes have undergone intense scientific scrutiny. Given divergent exercise induces similar signalling and gene expression profiles in skeletal muscle of untrained or recreationally active individuals, what is currently unclear is how the specificity of the molecular response is modified by prior training history. The time course of adaptation and when 'phenotype specificity' occurs has important implications for exercise prescription. This context is essential when attempting to concomitantly develop resistance to fatigue (through endurance-based exercise) and increased muscle mass (through resistance-based exercise), typically termed 'concurrent training'. Chronic training studies provide robust evidence that endurance exercise can attenuate muscle hypertrophy and strength but the mechanistic underpinning of this 'interference' effect with concurrent training is unknown. Moreover, despite the potential for several key regulators of muscle metabolism to explain an incompatibility in adaptation between endurance and resistance exercise, it now seems likely that multiple integrated, rather than isolated, effectors or processes generate the interference effect. Here we review studies of the molecular responses in skeletal muscle and evidence for the interference effect with concurrent training within the context of the specificity of training adaptation. © 2016 The Authors. The Journal of Physiology © 2016 The Physiological Society.

Meditation awareness training for the treatment of fibromyalgia syndrome: A randomized controlled trial.

PubMed

Van Gordon, William; Shonin, Edo; Dunn, Thomas J; Garcia-Campayo, Javier; Griffiths, Mark D

2017-02-01

The purpose of this study was to conduct the first randomized controlled trial (RCT) to evaluate the effectiveness of a second-generation mindfulness-based intervention (SG-MBI) for treating fibromyalgia syndrome (FMS). Compared to first-generation mindfulness-based interventions, SG-MBIs are more acknowledging of the spiritual aspect of mindfulness. A RCT employing intent-to-treat analysis. Adults with FMS received an 8-week SG-MBI known as meditation awareness training (MAT; n = 74) or an active control intervention known as cognitive behaviour theory for groups (n = 74). Assessments were performed at pre-, post-, and 6-month follow-up phases. Meditation awareness training participants demonstrated significant and sustained improvements over control group participants in FMS symptomatology, pain perception, sleep quality, psychological distress, non-attachment (to self, symptoms, and environment), and civic engagement. A mediation analysis found that (1) civic engagement partially mediated treatment effects for all outcome variables, (2) non-attachment partially mediated treatment effects for psychological distress and sleep quality, and (3) non-attachment almost fully mediated treatment effects for FMS symptomatology and pain perception. Average daily time spent in meditation was found to be a significant predictor of changes in all outcome variables. Meditation awareness training may be a suitable treatment for adults with FMS and appears to ameliorate FMS symptomatology and pain perception by reducing attachment to self. Statement of contribution What is already known on this subject? Designing interventions to treat fibromyalgia syndrome (FMS) continues to be a challenge. There is growing interest into the applications of mindfulness-based interventions for treating FMS. Second-generation mindfulness-based interventions (SG-MBIs) are a key new direction in mindfulness research. What does this study add? Meditation awareness training - an SG-MBI - resulted in significant reductions in FMS symptomatology. SG-MBIs recognize the spiritual aspect of mindfulness and may have a role in the treatment of FMS. © 2016 The British Psychological Society.

Optimization design of wind turbine drive train based on Matlab genetic algorithm toolbox

NASA Astrophysics Data System (ADS)

Li, R. N.; Liu, X.; Liu, S. J.

2013-12-01

In order to ensure the high efficiency of the whole flexible drive train of the front-end speed adjusting wind turbine, the working principle of the main part of the drive train is analyzed. As critical parameters, rotating speed ratios of three planetary gear trains are selected as the research subject. The mathematical model of the torque converter speed ratio is established based on these three critical variable quantity, and the effect of key parameters on the efficiency of hydraulic mechanical transmission is analyzed. Based on the torque balance and the energy balance, refer to hydraulic mechanical transmission characteristics, the transmission efficiency expression of the whole drive train is established. The fitness function and constraint functions are established respectively based on the drive train transmission efficiency and the torque converter rotating speed ratio range. And the optimization calculation is carried out by using MATLAB genetic algorithm toolbox. The optimization method and results provide an optimization program for exact match of wind turbine rotor, gearbox, hydraulic mechanical transmission, hydraulic torque converter and synchronous generator, ensure that the drive train work with a high efficiency, and give a reference for the selection of the torque converter and hydraulic mechanical transmission.

Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images.

PubMed

Maier-Hein, Lena; Mersmann, Sven; Kondermann, Daniel; Bodenstedt, Sebastian; Sanchez, Alexandro; Stock, Christian; Kenngott, Hannes Gotz; Eisenmann, Mathias; Speidel, Stefanie

2014-01-01

Machine learning algorithms are gaining increasing interest in the context of computer-assisted interventions. One of the bottlenecks so far, however, has been the availability of training data, typically generated by medical experts with very limited resources. Crowdsourcing is a new trend that is based on outsourcing cognitive tasks to many anonymous untrained individuals from an online community. In this work, we investigate the potential of crowdsourcing for segmenting medical instruments in endoscopic image data. Our study suggests that (1) segmentations computed from annotations of multiple anonymous non-experts are comparable to those made by medical experts and (2) training data generated by the crowd is of the same quality as that annotated by medical experts. Given the speed of annotation, scalability and low costs, this implies that the scientific community might no longer need to rely on experts to generate reference or training data for certain applications. To trigger further research in endoscopic image processing, the data used in this study will be made publicly available.

Model-based strategy for cell culture seed train layout verified at lab scale.

PubMed

Kern, Simon; Platas-Barradas, Oscar; Pörtner, Ralf; Frahm, Björn

2016-08-01

Cell culture seed trains-the generation of a sufficient viable cell number for the inoculation of the production scale bioreactor, starting from incubator scale-are time- and cost-intensive. Accordingly, a seed train offers potential for optimization regarding its layout and the corresponding proceedings. A tool has been developed to determine the optimal points in time for cell passaging from one scale into the next and it has been applied to two different cell lines at lab scale, AGE1.HN AAT and CHO-K1. For evaluation, experimental seed train realization has been evaluated in comparison to its layout. In case of the AGE1.HN AAT cell line, the results have also been compared to the formerly manually designed seed train. The tool provides the same seed train layout based on the data of only two batches.

Container-code recognition system based on computer vision and deep neural networks

NASA Astrophysics Data System (ADS)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

X-train: teaching professionals remotely.

PubMed

Santerre, Charles R

2005-05-01

Increased popularity of the Internet, along with the development of new software applications have dramatically improved our ability to create and deliver online continuing education trainings to professionals in the areas of nutrition and food safety. In addition, these technological advances permit effective and affordable measurement of training outcomes, i.e., changes in knowledge, attitude, and behavior, that result from these educational efforts. Impact assessment of engagement programs is becoming increasing important for demonstrating the value of training activities to stakeholders. A novel software program, called X-Train, takes advantage of technological advances (databases, computer graphics, Web-based interfaces, and network speed) for delivering high-quality trainings to teachers and health care professionals. X-Train automatically collects outcome data, and generates and sends certificates of completion and communicates with participants through electronic messages. X-Train can be used as a collaborative tool whereby experts from various academic institutions are brought together to develop Web-based trainings. Finally, X-Train uses a unique approach that encourages cooperative extension specialists and educators to promote these educational opportunities within their state or county.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

PubMed

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Measurement of glucose concentration by image processing of thin film slides

NASA Astrophysics Data System (ADS)

Piramanayagam, Sankaranaryanan; Saber, Eli; Heavner, David

2012-02-01

Measurement of glucose concentration is important for diagnosis and treatment of diabetes mellitus and other medical conditions. This paper describes a novel image-processing based approach for measuring glucose concentration. A fluid drop (patient sample) is placed on a thin film slide. Glucose, present in the sample, reacts with reagents on the slide to produce a color dye. The color intensity of the dye formed varies with glucose at different concentration levels. Current methods use spectrophotometry to determine the glucose level of the sample. Our proposed algorithm uses an image of the slide, captured at a specific wavelength, to automatically determine glucose concentration. The algorithm consists of two phases: training and testing. Training datasets consist of images at different concentration levels. The dye-occupied image region is first segmented using a Hough based technique and then an intensity based feature is calculated from the segmented region. Subsequently, a mathematical model that describes a relationship between the generated feature values and the given concentrations is obtained. During testing, the dye region of a test slide image is segmented followed by feature extraction. These two initial steps are similar to those done in training. However, in the final step, the algorithm uses the model (feature vs. concentration) obtained from the training and feature generated from test image to predict the unknown concentration. The performance of the image-based analysis was compared with that of a standard glucose analyzer.

Entrepreneurial training for girls empowerment in Lesotho: A process evaluation of a model programme

PubMed Central

Berry, Mary O'Neill; Kuriansky, Judy; Lytle, Megan; Vistman, Bozhena; Mosisili, ‘Mathato S.; Hlothoane, Lieketso; Matlanyane, Mapeo; Mokobori, Thabang; Mosuhli, Silas; Pebane, Jane

2014-01-01

A Girls Empowerment Programme held in 2010 in Lesotho, Sub-Saharan Africa, focused on HIV/AIDS risk reduction and prevention, life skills and entrepreneurial training (income-generating activities). Entrepreneurial training was a crucial part of equipping the camp attendees with basic skills to help them develop sustainable livelihoods. Such skills and financial independence are essential to enable rural girls to complete their secondary schooling (in a fee-based educational system) and to pursue a career, as well as to further help them be less susceptible to transactional sex and its significant risks. The results of a brief process evaluation with some nested supporting data showed considerable improvement in the girls' knowledge about income-generating activities. In addition, almost half of the camp attendees participated in further entrepreneurial training and about half of these girls went on to develop small businesses. Replication of this model of camp training is recommended and being explored in other African countries. PMID:25505804

Entrepreneurial training for girls empowerment in Lesotho: A process evaluation of a model programme.

PubMed

Berry, Mary O'Neill; Kuriansky, Judy; Lytle, Megan; Vistman, Bozhena; Mosisili, 'Mathato S; Hlothoane, Lieketso; Matlanyane, Mapeo; Mokobori, Thabang; Mosuhli, Silas; Pebane, Jane

2013-12-01

A Girls Empowerment Programme held in 2010 in Lesotho, Sub-Saharan Africa, focused on HIV/AIDS risk reduction and prevention, life skills and entrepreneurial training (income-generating activities). Entrepreneurial training was a crucial part of equipping the camp attendees with basic skills to help them develop sustainable livelihoods. Such skills and financial independence are essential to enable rural girls to complete their secondary schooling (in a fee-based educational system) and to pursue a career, as well as to further help them be less susceptible to transactional sex and its significant risks. The results of a brief process evaluation with some nested supporting data showed considerable improvement in the girls' knowledge about income-generating activities. In addition, almost half of the camp attendees participated in further entrepreneurial training and about half of these girls went on to develop small businesses. Replication of this model of camp training is recommended and being explored in other African countries.

Income Generation and Money Management: Training Women as Entrepreneurs.

ERIC Educational Resources Information Center

Reed, Sheila

Based on a workshop in Gambia in 1989, this manual was developed to help Peace Corps workers to develop training techniques for teaching women to run businesses producing and selling local products and to manage money. Topics covered include the following: (1) the role of the facilitator in adult learning; (2) problems women face in controlling…

Expertise in musical improvisation and creativity: the mediation of idea evaluation.

PubMed

Kleinmintz, Oded M; Goldstein, Pavel; Mayseless, Naama; Abecasis, Donna; Shamay-Tsoory, Simone G

2014-01-01

The current study explored the influence of musical expertise, and specifically training in improvisation on creativity, using the framework of the twofold model, according to which creativity involves a process of idea generation and idea evaluation. Based on the hypothesis that a strict evaluation phase may have an inhibiting effect over the generation phase, we predicted that training in improvisation may have a "releasing effect" on the evaluation system, leading to greater creativity. To examine this hypothesis, we compared performance among three groups--musicians trained in improvisation, musicians not trained in improvisation, and non-musicians--on divergent thinking tasks and on their evaluation of creativity. The improvisation group scored higher on fluency and originality compared to the other two groups. Among the musicians, evaluation of creativity mediated how experience in improvisation was related to originality and fluency scores. It is concluded that deliberate practice of improvisation may have a "releasing effect" on creativity.

Neural Network and Response Surface Methodology for Rocket Engine Component Optimization

NASA Technical Reports Server (NTRS)

Vaidyanathan, Rajkumar; Papita, Nilay; Shyy, Wei; Tucker, P. Kevin; Griffin, Lisa W.; Haftka, Raphael; Fitz-Coy, Norman; McConnaughey, Helen (Technical Monitor)

2000-01-01

The goal of this work is to compare the performance of response surface methodology (RSM) and two types of neural networks (NN) to aid preliminary design of two rocket engine components. A data set of 45 training points and 20 test points obtained from a semi-empirical model based on three design variables is used for a shear coaxial injector element. Data for supersonic turbine design is based on six design variables, 76 training, data and 18 test data obtained from simplified aerodynamic analysis. Several RS and NN are first constructed using the training data. The test data are then employed to select the best RS or NN. Quadratic and cubic response surfaces. radial basis neural network (RBNN) and back-propagation neural network (BPNN) are compared. Two-layered RBNN are generated using two different training algorithms, namely solverbe and solverb. A two layered BPNN is generated with Tan-Sigmoid transfer function. Various issues related to the training of the neural networks are addressed including number of neurons, error goals, spread constants and the accuracy of different models in representing the design space. A search for the optimum design is carried out using a standard gradient-based optimization algorithm over the response surfaces represented by the polynomials and trained neural networks. Usually a cubic polynominal performs better than the quadratic polynomial but exceptions have been noticed. Among the NN choices, the RBNN designed using solverb yields more consistent performance for both engine components considered. The training of RBNN is easier as it requires linear regression. This coupled with the consistency in performance promise the possibility of it being used as an optimization strategy for engineering design problems.

Discriminative motif optimization based on perceptron training

PubMed Central

Patel, Ronak Y.; Stormo, Gary D.

2014-01-01

Motivation: Generating accurate transcription factor (TF) binding site motifs from data generated using the next-generation sequencing, especially ChIP-seq, is challenging. The challenge arises because a typical experiment reports a large number of sequences bound by a TF, and the length of each sequence is relatively long. Most traditional motif finders are slow in handling such enormous amount of data. To overcome this limitation, tools have been developed that compromise accuracy with speed by using heuristic discrete search strategies or limited optimization of identified seed motifs. However, such strategies may not fully use the information in input sequences to generate motifs. Such motifs often form good seeds and can be further improved with appropriate scoring functions and rapid optimization. Results: We report a tool named discriminative motif optimizer (DiMO). DiMO takes a seed motif along with a positive and a negative database and improves the motif based on a discriminative strategy. We use area under receiver-operating characteristic curve (AUC) as a measure of discriminating power of motifs and a strategy based on perceptron training that maximizes AUC rapidly in a discriminative manner. Using DiMO, on a large test set of 87 TFs from human, drosophila and yeast, we show that it is possible to significantly improve motifs identified by nine motif finders. The motifs are generated/optimized using training sets and evaluated on test sets. The AUC is improved for almost 90% of the TFs on test sets and the magnitude of increase is up to 39%. Availability and implementation: DiMO is available at http://stormo.wustl.edu/DiMO Contact: rpatel@genetics.wustl.edu, ronakypatel@gmail.com PMID:24369152

Improving the quality of transvaginal ultrasound scan by simulation training for general practice residents.

PubMed

Le Lous, M; De Chanaud, N; Bourret, A; Senat, M V; Colmant, C; Jaury, P; Tesnière, A; Tsatsaris, V

2017-01-01

Ultrasonography (US) is an essential tool for the diagnosis of acute gynecological conditions. General practice (GP) residents are involved in the first-line management of gynecologic emergencies. They are not familiar with US equipment. Initial training on simulators was conducted.The aim of this study was to evaluate the impact of simulation-based training on the quality of the sonographic images achieved by GP residents 2 months after the simulation training versus clinical training alone. Young GP residents assigned to emergency gynecology departments were invited to a one-day simulation-based US training session. A prospective controlled trial aiming to assess the impact of such training on TVS (transvaginal ultrasound scan) image quality was conducted. The first group included GP residents who attended the simulation training course. The second group included GP residents who did not attend the course. Written consent to participate was obtained from all participants. Images achieved 2 months after the training were scored using standardized quality criteria and compared in both groups. The stress generated by this examination was also assessed with a simple numeric scale. A total of 137 residents attended the simulation training, 26 consented to participate in the controlled trial. Sonographic image quality was significantly better in the simulation group for the sagittal view of the uterus (3.6 vs 2.7, p  = 0.01), for the longitudinal view of the right ovary (2.8 vs 1.4, p  = 0.027), and for the Morrison space (1.7 vs 0.4, p  = 0.034), but the difference was not significant for the left ovary (2.9 vs 1.7, p  = 0.189). The stress generated by TVS after 2 months was not different between the groups (6.0 vs 4.8, p  = 0.4). Simulation-based training improved the quality of pelvic US images in GP residents assessed after 2 months of experience in gynecology compared to clinical training alone.

Simulation-based training for cardiology procedures: Are we any further forward in evidencing real-world benefits?

PubMed

Harrison, Christopher M; Gosai, Jivendra N

2017-04-01

Simulation-based training as an educational tool for healthcare professionals continues to grow in sophistication, scope, and usage. There have been a number of studies demonstrating the utility of the technique, and it is gaining traction as part of the training curricula for the next generation of cardiologists. In this review, we focus on the recent literature for the efficacy of simulation for practical procedures specific to cardiology, focusing on transesophageal echocardiography, cardiac catheterization, coronary angioplasty, and electrophysiology. A number of studies demonstrated improved performance by those trained using SBT when compared to other methods, although evidence of this leading to an improvement in patient outcomes remains scarce. We discuss this evidence, and the implications for practice for training in cardiology. Copyright © 2017 Elsevier Inc. All rights reserved.

Planning Training Loads for the 400 M Hurdles in Three-Month Mesocycles using Artificial Neural Networks.

PubMed

Przednowek, Krzysztof; Iskra, Janusz; Wiktorowicz, Krzysztof; Krzeszowski, Tomasz; Maszczyk, Adam

2017-12-01

This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes' training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period.

Planning Training Loads for the 400 M Hurdles in Three-Month Mesocycles using Artificial Neural Networks

PubMed Central

Iskra, Janusz; Wiktorowicz, Krzysztof; Krzeszowski, Tomasz; Maszczyk, Adam

2017-01-01

Abstract This paper presents a novel approach to planning training loads in hurdling using artificial neural networks. The neural models performed the task of generating loads for athletes’ training for the 400 meters hurdles. All the models were calculated based on the training data of 21 Polish National Team hurdlers, aged 22.25 ± 1.96, competing between 1989 and 2012. The analysis included 144 training plans that represented different stages in the annual training cycle. The main contribution of this paper is to develop neural models for planning training loads for the entire career of a typical hurdler. In the models, 29 variables were used, where four characterized the runner and 25 described the training process. Two artificial neural networks were used: a multi-layer perceptron and a network with radial basis functions. To assess the quality of the models, the leave-one-out cross-validation method was used in which the Normalized Root Mean Squared Error was calculated. The analysis shows that the method generating the smallest error was the radial basis function network with nine neurons in the hidden layer. Most of the calculated training loads demonstrated a non-linear relationship across the entire competitive period. The resulting model can be used as a tool to assist a coach in planning training loads during a selected training period. PMID:29339998

Automating the training development process for mission flight operations

NASA Technical Reports Server (NTRS)

Scott, Carol J.

1994-01-01

Traditional methods of developing training do not effectively support the changing needs of operational users in a multimission environment. The Automated Training Development System (ATDS) provides advantages over conventional methods in quality, quantity, turnaround, database maintenance, and focus on individualized instruction. The Operations System Training Group at the JPL performed a six-month study to assess the potential of ATDS to automate curriculum development and to generate and maintain course materials. To begin the study, the group acquired readily available hardware and participated in a two-week training session to introduce the process. ATDS is a building activity that combines training's traditional information-gathering with a hierarchical method for interleaving the elements. The program can be described fairly simply. A comprehensive list of candidate tasks determines the content of the database; from that database, selected critical tasks dictate which competencies of skill and knowledge to include in course material for the target audience. The training developer adds pertinent planning information about each task to the database, then ATDS generates a tailored set of instructional material, based on the specific set of selection criteria. Course material consistently leads students to a prescribed level of competency.

Roof Type Selection Based on Patch-Based Classification Using Deep Learning for High Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Partovi, T.; Fraundorfer, F.; Azimi, S.; Marmanis, D.; Reinartz, P.

2017-05-01

3D building reconstruction from remote sensing image data from satellites is still an active research topic and very valuable for 3D city modelling. The roof model is the most important component to reconstruct the Level of Details 2 (LoD2) for a building in 3D modelling. While the general solution for roof modelling relies on the detailed cues (such as lines, corners and planes) extracted from a Digital Surface Model (DSM), the correct detection of the roof type and its modelling can fail due to low quality of the DSM generated by dense stereo matching. To reduce dependencies of roof modelling on DSMs, the pansharpened satellite images as a rich resource of information are used in addition. In this paper, two strategies are employed for roof type classification. In the first one, building roof types are classified in a state-of-the-art supervised pre-trained convolutional neural network (CNN) framework. In the second strategy, deep features from deep layers of different pre-trained CNN model are extracted and then an RBF kernel using SVM is employed to classify the building roof type. Based on roof complexity of the scene, a roof library including seven types of roofs is defined. A new semi-automatic method is proposed to generate training and test patches of each roof type in the library. Using the pre-trained CNN model does not only decrease the computation time for training significantly but also increases the classification accuracy.

Exploiting the potential of unlabeled endoscopic video data with self-supervised learning.

PubMed

Ross, Tobias; Zimmerer, David; Vemuri, Anant; Isensee, Fabian; Wiesenfarth, Manuel; Bodenstedt, Sebastian; Both, Fabian; Kessler, Philip; Wagner, Martin; Müller, Beat; Kenngott, Hannes; Speidel, Stefanie; Kopp-Schneider, Annette; Maier-Hein, Klaus; Maier-Hein, Lena

2018-06-01

Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field. The purpose of this paper was to investigate the concept of self-supervised learning to address this issue. Our approach is guided by the hypothesis that unlabeled video data can be used to learn a representation of the target domain that boosts the performance of state-of-the-art machine learning algorithms when used for pre-training. Core of the method is an auxiliary task based on raw endoscopic video data of the target domain that is used to initialize the convolutional neural network (CNN) for the target task. In this paper, we propose the re-colorization of medical images with a conditional generative adversarial network (cGAN)-based architecture as auxiliary task. A variant of the method involves a second pre-training step based on labeled data for the target task from a related domain. We validate both variants using medical instrument segmentation as target task. The proposed approach can be used to radically reduce the manual annotation effort involved in training CNNs. Compared to the baseline approach of generating annotated data from scratch, our method decreases exploratively the number of labeled images by up to 75% without sacrificing performance. Our method also outperforms alternative methods for CNN pre-training, such as pre-training on publicly available non-medical (COCO) or medical data (MICCAI EndoVis2017 challenge) using the target task (in this instance: segmentation). As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training.

Translating knowledge into practice: An exploratory study of dementia-specific training for community-based service providers.

PubMed

O'Sullivan, Grace; Hocking, Clare; McPherson, Kathryn

2017-08-01

Objective To develop, deliver, and evaluate dementia-specific training designed to inform service delivery by enhancing the knowledge of community-based service providers. Methods This exploratory qualitative study used an interdisciplinary, interuniversity team approach to develop and deliver dementia-specific training. Participants included management, care staff, and clients from three organizations funded to provide services in the community. Data on the acceptability, applicability, and perceived outcomes of the training were gathered through focus group discussions and individual interviews. Transcripts were analyzed to generate open codes which were clustered into themes and sub-themes addressing the content, delivery, and value of the training. Findings Staff valued up-to-date knowledge and "real stories" grounded in practice. Clients welcomed the strengths-based approach. Contractual obligations impact on the application of knowledge in practice. Implications The capacity to implement new knowledge may be limited by the legislative policies which frame service provision, to the detriment of service users.

MotionTherapy@Home - First results of a clinical study with a novel robotic device for automated locomotion therapy at home.

PubMed

Rupp, Rüdiger; Plewa, Harry; Schuld, Christian; Gerner, Hans Jürgen; Hofer, Eberhard P; Knestel, Markus

2011-02-01

In incomplete spinal cord injured subjects, task-oriented training regimes are applied for enhancement of neuroplasticity to improve gait capacity. However, a sufficient training intensity can only be achieved during the inpatient phase, which is getting shorter and shorter due to economic restrictions. In the clinical environment, complex and expensive robotic devices have been introduced to maintain the duration and the intensity of the training, but up to now only a few exist for continuation of automated locomotion training at home. For continuation of the automated locomotion training at home prototypes of the compact, pneumatically driven orthosis MoreGait have been realized, which generate the key afferent stimuli for activation of the spinal gait pattern generator. Artificial pneumatic muscles with excellent weight-to-force ratio and safety characteristics have been integrated as joint actuators. Additionally, a Stimulative Shoe for generation of the appropriate foot loading pattern has been developed without the need for verticalization of the user. The first results of the pilot study in eight chronic incomplete spinal cord injured subjects indicate that the home-based therapy is safe and feasible. The therapy related improvements of the walking capacity are in the range of locomotion robots used in clinical settings.

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

Diaconescu, Paula L.; Garcia, Evan

The objective of our research project was to study the reactivity of uranium complexes supported by ferrocene-based ligands. In addition, this research provides training of graduate students as the next generation of actinide scientists.

Know Thy Learner: User Characteristics Underlying Effective Videogame-Based Training

NASA Technical Reports Server (NTRS)

Orvis, Karin A.; Horn, Daniel B.; Belanich, James

2008-01-01

Some proponents of training games argue that younger adults (Soldiers) are part of the "digital"or "twitch" generation, having grown up uing computers and playing videogames (e.g.,Prensky,2001). The Entertainment Software Association (ESA) reports that 69%of American heads of households play computer and/or videogames. "65% of college students reported being regular or occasional game players" (Jones, 2003).

Physical environment virtualization for human activities recognition

NASA Astrophysics Data System (ADS)

Poshtkar, Azin; Elangovan, Vinayak; Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2015-05-01

Human activity recognition research relies heavily on extensive datasets to verify and validate performance of activity recognition algorithms. However, obtaining real datasets are expensive and highly time consuming. A physics-based virtual simulation can accelerate the development of context based human activity recognition algorithms and techniques by generating relevant training and testing videos simulating diverse operational scenarios. In this paper, we discuss in detail the requisite capabilities of a virtual environment to aid as a test bed for evaluating and enhancing activity recognition algorithms. To demonstrate the numerous advantages of virtual environment development, a newly developed virtual environment simulation modeling (VESM) environment is presented here to generate calibrated multisource imagery datasets suitable for development and testing of recognition algorithms for context-based human activities. The VESM environment serves as a versatile test bed to generate a vast amount of realistic data for training and testing of sensor processing algorithms. To demonstrate the effectiveness of VESM environment, we present various simulated scenarios and processed results to infer proper semantic annotations from the high fidelity imagery data for human-vehicle activity recognition under different operational contexts.

A Regions of Confidence Based Approach to Enhance Segmentation with Shape Priors.

PubMed

Appia, Vikram V; Ganapathy, Balaji; Abufadel, Amer; Yezzi, Anthony; Faber, Tracy

2010-01-18

We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in.

Theoretical and experimental study of vibration, generated by monorail trains

NASA Astrophysics Data System (ADS)

Rybak, Samuil A.; Makhortykh, Sergey A.; Kostarev, Stanislav A.

2002-11-01

Monorail transport as all other city transport vehicles is the source of high noise and vibration levels. It is less widespread than cars or underground transport but its influence in modern cities enhances. Now in Moscow the first monorail road with trains on tires is designed, therefore the problem of vibration and noise assessments and prediction of its impact on the residential region appears. To assess the levels of generated vibration a physical model of interaction in the system wagon-tire-road coating-viaduct-soil has been proposed and then numerically analyzed. The model is based on the known from publications facts of automobile transport vibration and our own practice concerning underground trains vibration generation. To verify computer simulation results and adjust model parameters the series of measurements of noise and vibration near experimental monorail road was carried out. In the report the results of calculations and measurements will be presented and some outcomes of possible acoustical ecologic situation near monorail roads will be proposed.

Gust wind tunnel study on ballast pick-up by high-speed trains

NASA Astrophysics Data System (ADS)

Navarro-Medina, F.; Sanz-Andres, A.; Perez-Grande, I.

2012-01-01

This paper describes the experimental setup, procedure, and results obtained, concerning the dynamics of a body lying on a floor, attached to a hinge, and exposed to an unsteady flow, which is a model of the initiation of rotational motion of ballast stones due to the wind generated by the passing of a high-speed train. The idea is to obtain experimental data to support the theoretical model developed in Sanz-Andres and Navarro-Medina (J Wind Eng Ind Aerodyn 98, 772-783, (2010), aimed at analyzing the initial phase of the ballast train-induced-wind erosion (BATIWE) phenomenon. The experimental setup is based on an open circuit, closed test section, low-speed wind tunnel, with a new sinusoidal gust generator mechanism concept, designed and built at the IDR/UPM. The tunnel's main characteristic is the ability to generate a flow with a uniform velocity profile and sinusoidal time fluctuation of the speed. Experimental results and theoretical model predictions are in good agreement.

A School-Based Mental Health Consultation Curriculum.

ERIC Educational Resources Information Center

Sandoval, Jonathan; Davis, John M.

1984-01-01

Presents one position on consultation that integrates a theoretical model, a process model, and a curriculum for training school-based mental health consultants. Elements of the proposed curriculum include: ethics, relationship building, maintaining rapport, defining problems, gathering data, sharing information, generating and supporting…

Text generation from Taiwanese Sign Language using a PST-based language model for augmentative communication.

PubMed

Wu, Chung-Hsien; Chiu, Yu-Hsien; Guo, Chi-Shiang

2004-12-01

This paper proposes a novel approach to the generation of Chinese sentences from ill-formed Taiwanese Sign Language (TSL) for people with hearing impairments. First, a sign icon-based virtual keyboard is constructed to provide a visualized interface to retrieve sign icons from a sign database. A proposed language model (LM), based on a predictive sentence template (PST) tree, integrates a statistical variable n-gram LM and linguistic constraints to deal with the translation problem from ill-formed sign sequences to grammatical written sentences. The PST tree trained by a corpus collected from the deaf schools was used to model the correspondence between signed and written Chinese. In addition, a set of phrase formation rules, based on trigger pair category, was derived for sentence pattern expansion. These approaches improved the efficiency of text generation and the accuracy of word prediction and, therefore, improved the input rate. For the assessment of practical communication aids, a reading-comprehension training program with ten profoundly deaf students was undertaken in a deaf school in Tainan, Taiwan. Evaluation results show that the literacy aptitude test and subjective satisfactory level are significantly improved.

Use of circulating-fluidized-bed combustors in compressed-air energy storage systems

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

Nakhamkin, M.; Patel, M.

1990-07-01

This report presents the result of a study conducted by Energy Storage and Power Consultants (ESPC), with the objective to develop and analyze compressed air energy storage (CAES) power plant concepts which utilize coal-fired circulating fluidized bed combustors (CFBC) for heating air during generating periods. The use of a coal-fired CFBC unit for indirect heating of the compressed air, in lieu of the current turbomachinery combustors, would eliminate the need for expensive premium fuels by a CAES facility. The CAES plant generation heat rate is approximately one-half of that for a conventional steam condensing power plant. Therefore, the required CFBCmore » heat generation capacity and capital costs would be lower per kW of power generation capacity. Three CAES/CFBC concepts were identified as the most promising, and were optimized using specifically developed computerized procedures. These concepts utilize various configurations of reheat turbomachinery trains specifically developed for CAES application as parts of the integrated CAES/CFBC plant concepts. The project team concluded that the optimized CAES/CFBC integrated plant concepts present a potentially attractive alternative to conventional steam generation power plants using CFBC or pulverized coal-fired boilers. A comparison of the results from the economic analysis performed on three concepts suggests that one of them (Concept 3) is the preferred concept. This concept has a two shaft turbomachinery train arrangement, and provides for load management functions by the compressor-electric motor train, and continuous base load operation of the turboexpander-electric generator train and the CFBC unit. 6 refs., 30 figs., 14 tabs.« less

A direct temporal domain approach for ultrafast optical signal processing and its implementation using planar lightwave circuits

NASA Astrophysics Data System (ADS)

Xia, Bing

Ultrafast optical signal processing, which shares the same fundamental principles of electrical signal processing, can realize numerous important functionalities required in both academic research and industry. Due to the extremely fast processing speed, all-optical signal processing and pulse shaping have been widely used in ultrafast telecommunication networks, photonically-assisted RFlmicro-meter waveform generation, microscopy, biophotonics, and studies on transient and nonlinear properties of atoms and molecules. In this thesis, we investigate two types of optical spectrally-periodic (SP) filters that can be fabricated on planar lightwave circuits (PLC) to perform pulse repetition rate multiplication (PRRM) and arbitrary optical waveform generation (AOWG). First, we present a direct temporal domain approach for PRRM using SP filters. We show that the repetition rate of an input pulse train can be multiplied by a factor N using an optical filter with a free spectral range that does not need to be constrained to an integer multiple of N. Furthermore, the amplitude of each individual output pulse can be manipulated separately to form an arbitrary envelope at the output by optimizing the impulse response of the filter. Next, we use lattice-form Mach-Zehnder interferometers (LF-MZI) to implement the temporal domain approach for PRRM. The simulation results show that PRRM with uniform profiles, binary-code profiles and triangular profiles can be achieved. Three silica based LF-MZIs are designed and fabricated, which incorporate multi-mode interference (MMI) couplers and phase shifters. The experimental results show that 40 GHz pulse trains with a uniform envelope pattern, a binary code pattern "1011" and a binary code pattern "1101" are generated from a 10 GHz input pulse train. Finally, we investigate 2D ring resonator arrays (RRA) for ultraf ast optical signal processing. We design 2D RRAs to generate a pair of pulse trains with different binary-code patterns simultaneously from a single pulse train at a low repetition rate. We also design 2D RRAs for AOWG using the modified direct temporal domain approach. To demonstrate the approach, we provide numerical examples to illustrate the generation of two very different waveforms (square waveform and triangular waveform) from the same hyperbolic secant input pulse train. This powerful technique based on SP filters can be very useful for ultrafast optical signal processing and pulse shaping.

Face recognition based on symmetrical virtual image and original training image

NASA Astrophysics Data System (ADS)

Ke, Jingcheng; Peng, Yali; Liu, Shigang; Li, Jun; Pei, Zhao

2018-02-01

In face representation-based classification methods, we are able to obtain high recognition rate if a face has enough available training samples. However, in practical applications, we only have limited training samples to use. In order to obtain enough training samples, many methods simultaneously use the original training samples and corresponding virtual samples to strengthen the ability of representing the test sample. One is directly using the original training samples and corresponding mirror samples to recognize the test sample. However, when the test sample is nearly symmetrical while the original training samples are not, the integration of the original training and mirror samples might not well represent the test samples. To tackle the above-mentioned problem, in this paper, we propose a novel method to obtain a kind of virtual samples which are generated by averaging the original training samples and corresponding mirror samples. Then, the original training samples and the virtual samples are integrated to recognize the test sample. Experimental results on five face databases show that the proposed method is able to partly overcome the challenges of the various poses, facial expressions and illuminations of original face image.

An intelligent training system for space shuttle flight controllers

NASA Technical Reports Server (NTRS)

Loftin, R. Bowen; Wang, Lui; Baffes, Paul; Hua, Grace

1988-01-01

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (DeplEx for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The DeplEx also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of DeplEx in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.

An intelligent training system for space shuttle flight controllers

NASA Technical Reports Server (NTRS)

Loftin, R. Bowen; Wang, Lui; Baffles, Paul; Hua, Grace

1988-01-01

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (Dep1Ex for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The Dep1Ex also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of Dep1Ex in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.

Establishment of Next-Generation Neurosurgery Research and Training Laboratory with Integrated Human Performance Monitoring.

PubMed

Bernardo, Antonio

2017-10-01

Quality of neurosurgical care and patient outcomes are inextricably linked to surgical and technical proficiency and a thorough working knowledge of microsurgical anatomy. Neurosurgical laboratory-based cadaveric training is essential for the development and refinement of technical skills before their use on a living patient. Recent biotechnological advances including 3-dimensional (3D) microscopy and endoscopy, 3D printing, virtual reality, surgical simulation, surgical robotics, and advanced neuroimaging have proved to reduce the learning curve, improve conceptual understanding of complex anatomy, and enhance visuospatial skills in neurosurgical training. Until recently, few means have allowed surgeons to obtain integrated surgical and technological training in an operating room setting. We report on a new model, currently in use at our institution, for technologically integrated surgical training and innovation using a next-generation microneurosurgery skull base laboratory designed to recreate the setting of a working operating room. Each workstation is equipped with a 3D surgical microscope, 3D endoscope, surgical drills, operating table with a Mayfield head holder, and a complete set of microsurgical tools. The laboratory also houses a neuronavigation system, a surgical robotic, a surgical planning system, 3D visualization, virtual reality, and computerized simulation for training of surgical procedures and visuospatial skills. In addition, the laboratory is equipped with neurophysiological monitoring equipment in order to conduct research into human factors in surgery and the respective roles of workload and fatigue on surgeons' performance. Copyright © 2017 Elsevier Inc. All rights reserved.

Does rational selection of training and test sets improve the outcome of QSAR modeling?

PubMed

Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

2012-10-22

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

First systematic plant proteomics workshop in Botany Department, University of Delhi: transferring proteomics knowledge to next-generation researchers and students.

PubMed

Deswal, Renu; Abat, Jasmeet Kaur; Sehrawat, Ankita; Gupta, Ravi; Kashyap, Prakriti; Sharma, Shruti; Sharma, Bhavana; Chaurasia, Satya Prakash; Chanu, Sougrakpam Yaiphabi; Masi, Antonio; Agrawal, Ganesh Kumar; Sarkar, Abhijit; Agrawal, Raj; Dunn, Michael J; Renaut, Jenny; Rakwal, Randeep

2014-07-01

International Plant Proteomics Organization (INPPO) outlined ten initiatives to promote plant proteomics in each and every country. With greater emphasis in developing countries, one of those was to "organize workshops at national and international levels to train manpower and exchange information". This third INPPO highlights covers the workshop organized for the very first time in a developing country, India, at the Department of Botany in University of Delhi on December 26-30, 2013 titled - "1(st) Plant Proteomics Workshop / Training Program" under the umbrella of INPPO India-Nepal chapter. Selected 20 participants received on-hand training mainly on gel-based proteomics approach along with manual booklet and parallel lectures on this and associated topics. In house, as well as invited experts drawn from other Universities and Institutes (national and international), delivered talks on different aspects of gel-based and gel-free proteomics. Importance of gel-free proteomics approach, translational proteomics, and INPPO roles were presented and interactively discussed by a group of three invited speakers Drs. Ganesh Kumar Agrawal (Nepal), Randeep Rakwal (Japan), and Antonio Masi (Italy). Given the output of this systematic workshop, it was proposed and thereafter decided to be organized every alternate year; the next workshop will be held in 2015. Furthermore, possibilities on providing advanced training to those students / researchers / teachers with basic knowledge in proteomics theory and experiments at national and international levels were discussed. INPPO is committed to generating next-generation trained manpower in proteomics, and it would only happen by the firm determination of scientists to come forward and do it. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education.

PubMed

Aikat, Jay; Carsey, Thomas M; Fecho, Karamarie; Jeffay, Kevin; Krishnamurthy, Ashok; Mucha, Peter J; Rajasekar, Arcot; Ahalt, Stanley C

2017-03-01

The era of "big data" has radically altered the way scientific research is conducted and new knowledge is discovered. Indeed, the scientific method is rapidly being complemented and even replaced in some fields by data-driven approaches to knowledge discovery. This paradigm shift is sometimes referred to as the "fourth paradigm" of data-intensive and data-enabled scientific discovery. Interdisciplinary research with a hard emphasis on translational outcomes is becoming the norm in all large-scale scientific endeavors. Yet, graduate education remains largely focused on individual achievement within a single scientific domain, with little training in team-based, interdisciplinary data-oriented approaches designed to translate scientific data into new solutions to today's critical challenges. In this article, we propose a new pedagogy for graduate education: data-centered learning for the domain-data scientist. Our approach is based on four tenets: (1) Graduate training must incorporate interdisciplinary training that couples the domain sciences with data science. (2) Graduate training must prepare students for work in data-enabled research teams. (3) Graduate training must include education in teaming and leadership skills for the data scientist. (4) Graduate training must provide experiential training through academic/industry practicums and internships. We emphasize that this approach is distinct from today's graduate training, which offers training in either data science or a domain science (e.g., biology, sociology, political science, economics, and medicine), but does not integrate the two within a single curriculum designed to prepare the next generation of domain-data scientists. We are in the process of implementing the proposed pedagogy through the development of a new graduate curriculum based on the above four tenets, and we describe herein our strategy, progress, and lessons learned. While our pedagogy was developed in the context of graduate education, the general approach of data-centered learning can and should be applied to students and professionals at any stage of their education, including at the K-12, undergraduate, graduate, and professional levels. We believe that the time is right to embed data-centered learning within our educational system and, thus, generate the talent required to fully harness the potential of big data.

A Satellite-Based Infrastructure Providing Broadband IP Services on Board High Speed Trains

NASA Astrophysics Data System (ADS)

Feltrin, Eros; Weller, Elisabeth

After the earlier technologies that offered satellite mobile services for civil and military applications, today’s specific antenna design, modulation techniques and most powerful new generation satellites also allow a good level of performance to be achieved on-board high speed modes of transport such as aircraft and trains. This paper reports the Eutelsat’s experience in the developing and deploying architecture based on a spread spectrum system in order to provide broadband connectivity on board of high speed trains. After introducing the adopted technologies, the architecture and the constraints, some results obtained from analysis, testing and measuring of the availability of the service are reported and commented upon.

Human factors in the Naval Air Systems Command: Computer based training

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

Seamster, T.L.; Snyder, C.E.; Terranova, M.

1988-01-01

Military standards applied to the private sector contracts have a substantial effect on the quality of Computer Based Training (CBT) systems procured for the Naval Air Systems Command. This study evaluated standards regulating the following areas in CBT development and procurement: interactive training systems, cognitive task analysis, and CBT hardware. The objective was to develop some high-level recommendations for evolving standards that will govern the next generation of CBT systems. One of the key recommendations is that there be an integration of the instructional systems development, the human factors engineering, and the software development standards. Recommendations were also made formore » task analysis and CBT hardware standards. (9 refs., 3 figs.)« less

Phantom-based interactive simulation system for dental treatment training.

PubMed

Sae-Kee, Bundit; Riener, Robert; Frey, Martin; Pröll, Thomas; Burgkart, Rainer

2004-01-01

In this paper, we propose a new interactive simulation system for dental treatment training. The system comprises a virtual reality environment and a force-torque measuring device to enhance the capabilities of a passive phantom of tooth anatomy in dental treatment training processes. The measuring device is connected to the phantom, and provides essential input data for generating the graphic animations of physical behaviors such as drilling and bleeding. The animation methods of those physical behaviors are also presented. This system is not only able to enhance interactivity and accessibility of the training system compared to conventional methods but it also provides possibilities of recording, evaluating, and verifying the training results.

Optical classification for quality and defect analysis of train brakes

NASA Astrophysics Data System (ADS)

Glock, Stefan; Hausmann, Stefan; Gerke, Sebastian; Warok, Alexander; Spiess, Peter; Witte, Stefan; Lohweg, Volker

2009-06-01

In this paper we present an optical measurement system approach for quality analysis of brakes which are used in high-speed trains. The brakes consist of the so called brake discs and pads. In a deceleration process the discs will be heated up to 500°C. The quality measure is based on the fact that the heated brake discs should not generate hot spots inside the brake material. Instead, the brake disc should be heated homogeneously by the deceleration. Therefore, it makes sense to analyze the number of hot spots and their relative gradients to create a quality measure for train brakes. In this contribution we present a new approach for a quality measurement system which is based on an image analysis and classification of infra-red based heat images. Brake images which are represented in pseudo-color are first transformed in a linear grayscale space by a hue-saturation-intensity (HSI) space. This transform is necessary for the following gradient analysis which is based on gray scale gradient filters. Furthermore, different features based on Haralick's measures are generated from the gray scale and gradient images. A following Fuzzy-Pattern-Classifier is used for the classification of good and bad brakes. It has to be pointed out that the classifier returns a score value for each brake which is between 0 and 100% good quality. This fact guarantees that not only good and bad bakes can be distinguished, but also their quality can be labeled. The results show that all critical thermal patterns of train brakes can be sensed and verified.

Artificial Neural Networks as an Architectural Design Tool-Generating New Detail Forms Based On the Roman Corinthian Order Capital

NASA Astrophysics Data System (ADS)

Radziszewski, Kacper

2017-10-01

The following paper presents the results of the research in the field of the machine learning, investigating the scope of application of the artificial neural networks algorithms as a tool in architectural design. The computational experiment was held using the backward propagation of errors method of training the artificial neural network, which was trained based on the geometry of the details of the Roman Corinthian order capital. During the experiment, as an input training data set, five local geometry parameters combined has given the best results: Theta, Pi, Rho in spherical coordinate system based on the capital volume centroid, followed by Z value of the Cartesian coordinate system and a distance from vertical planes created based on the capital symmetry. Additionally during the experiment, artificial neural network hidden layers optimal count and structure was found, giving results of the error below 0.2% for the mentioned before input parameters. Once successfully trained artificial network, was able to mimic the details composition on any other geometry type given. Despite of calculating the transformed geometry locally and separately for each of the thousands of surface points, system could create visually attractive and diverse, complex patterns. Designed tool, based on the supervised learning method of machine learning, gives possibility of generating new architectural forms- free of the designer’s imagination bounds. Implementing the infinitely broad computational methods of machine learning, or Artificial Intelligence in general, not only could accelerate and simplify the design process, but give an opportunity to explore never seen before, unpredictable forms or everyday architectural practice solutions.

ALOG user's manual: A Guide to using the spreadsheet-based artificial log generator

Treesearch

Matthew F. Winn; Philip A. Araman; Randolph H. Wynne

2012-01-01

Computer programs that simulate log sawing can be valuable training tools for sawyers, as well as a means oftesting different sawing patterns. Most available simulation programs rely on diagrammed-log databases, which canbe very costly and time consuming to develop. Artificial Log Generator (ALOG) is a user-friendly Microsoft® Excel®...

Infrared imagery acquisition process supporting simulation and real image training

NASA Astrophysics Data System (ADS)

O'Connor, John

2012-05-01

The increasing use of infrared sensors requires development of advanced infrared training and simulation tools to meet current Warfighter needs. In order to prepare the force, a challenge exists for training and simulation images to be both realistic and consistent with each other to be effective and avoid negative training. The US Army Night Vision and Electronic Sensors Directorate has corrected this deficiency by developing and implementing infrared image collection methods that meet the needs of both real image trainers and real-time simulations. The author presents innovative methods for collection of high-fidelity digital infrared images and the associated equipment and environmental standards. The collected images are the foundation for US Army, and USMC Recognition of Combat Vehicles (ROC-V) real image combat ID training and also support simulations including the Night Vision Image Generator and Synthetic Environment Core. The characteristics, consistency, and quality of these images have contributed to the success of these and other programs. To date, this method has been employed to generate signature sets for over 350 vehicles. The needs of future physics-based simulations will also be met by this data. NVESD's ROC-V image database will support the development of training and simulation capabilities as Warfighter needs evolve.

Designing and using computer simulations in medical education and training: an introduction.

PubMed

Friedl, Karl E; O'Neil, Harold F

2013-10-01

Computer-based technologies informed by the science of learning are becoming increasingly prevalent in education and training. For the Department of Defense (DoD), this presents a great potential advantage to the effective preparation of a new generation of technologically enabled service members. Military medicine has broad education and training challenges ranging from first aid and personal protective skills for every service member to specialized combat medic training; many of these challenges can be met with gaming and simulation technologies that this new generation has embraced. However, comprehensive use of medical games and simulation to augment expert mentorship is still limited to elite medical provider training programs, but can be expected to become broadly used in the training of first responders and allied health care providers. The purpose of this supplement is to review the use of computer games and simulation to teach and assess medical knowledge and skills. This review and other DoD research policy sources will form the basis for development of a research and development road map and guidelines for use of this technology in military medicine. Reprint & Copyright © 2013 Association of Military Surgeons of the U.S.

Higher Secondary Learners' Effectiveness towards Web Based Instruction (WBI) on Chemistry

ERIC Educational Resources Information Center

Sudha, A.; Amutha, S.

2015-01-01

Web-based training is becoming a phenomenon in education today because of its flexibility and convenience, it is vitally important to address those issues that adversely impact retention and success in this environment. To generate principles of effective asynchronous web-based materials specifically applicable for secondary level students based…

Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

NASA Astrophysics Data System (ADS)

Ma, Lei; Cheng, Liang; Li, Manchun; Liu, Yongxue; Ma, Xiaoxue

2015-04-01

Unmanned Aerial Vehicle (UAV) has been used increasingly for natural resource applications in recent years due to their greater availability and the miniaturization of sensors. In addition, Geographic Object-Based Image Analysis (GEOBIA) has received more attention as a novel paradigm for remote sensing earth observation data. However, GEOBIA generates some new problems compared with pixel-based methods. In this study, we developed a strategy for the semi-automatic optimization of object-based classification, which involves an area-based accuracy assessment that analyzes the relationship between scale and the training set size. We found that the Overall Accuracy (OA) increased as the training set ratio (proportion of the segmented objects used for training) increased when the Segmentation Scale Parameter (SSP) was fixed. The OA increased more slowly as the training set ratio became larger and a similar rule was obtained according to the pixel-based image analysis. The OA decreased as the SSP increased when the training set ratio was fixed. Consequently, the SSP should not be too large during classification using a small training set ratio. By contrast, a large training set ratio is required if classification is performed using a high SSP. In addition, we suggest that the optimal SSP for each class has a high positive correlation with the mean area obtained by manual interpretation, which can be summarized by a linear correlation equation. We expect that these results will be applicable to UAV imagery classification to determine the optimal SSP for each class.

Many Molecular Properties from One Kernel in Chemical Space

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

Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole

We introduce property-independent kernels for machine learning modeling of arbitrarily many molecular properties. The kernels encode molecular structures for training sets of varying size, as well as similarity measures sufficiently diffuse in chemical space to sample over all training molecules. Corresponding molecular reference properties provided, they enable the instantaneous generation of ML models which can systematically be improved through the addition of more data. This idea is exemplified for single kernel based modeling of internal energy, enthalpy, free energy, heat capacity, polarizability, electronic spread, zero-point vibrational energy, energies of frontier orbitals, HOMOLUMO gap, and the highest fundamental vibrational wavenumber. Modelsmore » of these properties are trained and tested using 112 kilo organic molecules of similar size. Resulting models are discussed as well as the kernels’ use for generating and using other property models.« less

Dynamic Behavior of Wind Turbine by a Mixed Flexible-Rigid Multi-Body Model

NASA Astrophysics Data System (ADS)

Wang, Jianhong; Qin, Datong; Ding, Yi

A mixed flexible-rigid multi-body model is presented to study the dynamic behavior of a horizontal axis wind turbine. The special attention is given to flexible body: flexible rotor is modeled by a newly developed blade finite element, support bearing elasticities, variations in the number of teeth in contact as well as contact tooth's elasticities are mainly flexible components in the power train. The couple conditions between different subsystems are established by constraint equations. The wind turbine model is generated by coupling models of rotor, power train and generator with constraint equations together. Based on this model, an eigenproblem analysis is carried out to show the mode shape of rotor and power train at a few natural frequencies. The dynamic responses and contact forces among gears under constant wind speed and fixed pitch angle are analyzed.

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

NASA Astrophysics Data System (ADS)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Expertise in Musical Improvisation and Creativity: The Mediation of Idea Evaluation

PubMed Central

Kleinmintz, Oded M.; Goldstein, Pavel; Mayseless, Naama; Abecasis, Donna; Shamay-Tsoory, Simone G.

2014-01-01

The current study explored the influence of musical expertise, and specifically training in improvisation on creativity, using the framework of the twofold model, according to which creativity involves a process of idea generation and idea evaluation. Based on the hypothesis that a strict evaluation phase may have an inhibiting effect over the generation phase, we predicted that training in improvisation may have a “releasing effect” on the evaluation system, leading to greater creativity. To examine this hypothesis, we compared performance among three groups - musicians trained in improvisation, musicians not trained in improvisation, and non-musicians - on divergent thinking tasks and on their evaluation of creativity. The improvisation group scored higher on fluency and originality compared to the other two groups. Among the musicians, evaluation of creativity mediated how experience in improvisation was related to originality and fluency scores. It is concluded that deliberate practice of improvisation may have a “releasing effect” on creativity. PMID:25010334

Realistic training scenario simulations and simulation techniques

DOEpatents

Dunlop, William H.; Koncher, Tawny R.; Luke, Stanley John; Sweeney, Jerry Joseph; White, Gregory K.

2017-12-05

In one embodiment, a system includes a signal generator operatively coupleable to one or more detectors; and a controller, the controller being both operably coupled to the signal generator and configured to cause the signal generator to: generate one or more signals each signal being representative of at least one emergency event; and communicate one or more of the generated signal(s) to a detector to which the signal generator is operably coupled. In another embodiment, a method includes: receiving data corresponding to one or more emergency events; generating at least one signal based on the data; and communicating the generated signal(s) to a detector.

A method for feature selection of APT samples based on entropy

NASA Astrophysics Data System (ADS)

Du, Zhenyu; Li, Yihong; Hu, Jinsong

2018-05-01

By studying the known APT attack events deeply, this paper propose a feature selection method of APT sample and a logic expression generation algorithm IOCG (Indicator of Compromise Generate). The algorithm can automatically generate machine readable IOCs (Indicator of Compromise), to solve the existing IOCs logical relationship is fixed, the number of logical items unchanged, large scale and cannot generate a sample of the limitations of the expression. At the same time, it can reduce the redundancy and useless APT sample processing time consumption, and improve the sharing rate of information analysis, and actively respond to complex and volatile APT attack situation. The samples were divided into experimental set and training set, and then the algorithm was used to generate the logical expression of the training set with the IOC_ Aware plug-in. The contrast expression itself was different from the detection result. The experimental results show that the algorithm is effective and can improve the detection effect.

Human Activity Behavior and Gesture Generation in Virtual Worlds for Long- Duration Space Missions. Chapter 8

NASA Technical Reports Server (NTRS)

Sierhuis, Maarten; Clancey, William J.; Damer, Bruce; Brodsky, Boris; vanHoff, Ron

2007-01-01

A virtual worlds presentation technique with embodied, intelligent agents is being developed as an instructional medium suitable to present in situ training on long term space flight. The system combines a behavioral element based on finite state automata, a behavior based reactive architecture also described as subsumption architecture, and a belief-desire-intention agent structure. These three features are being integrated to describe a Brahms virtual environment model of extravehicular crew activity which could become a basis for procedure training during extended space flight.

Multiple Point Statistics algorithm based on direct sampling and multi-resolution images

NASA Astrophysics Data System (ADS)

Julien, S.; Renard, P.; Chugunova, T.

2017-12-01

Multiple Point Statistics (MPS) has become popular for more than one decade in Earth Sciences, because these methods allow to generate random fields reproducing highly complex spatial features given in a conceptual model, the training image, while classical geostatistics techniques based on bi-point statistics (covariance or variogram) fail to generate realistic models. Among MPS methods, the direct sampling consists in borrowing patterns from the training image to populate a simulation grid. This latter is sequentially filled by visiting each of these nodes in a random order, and then the patterns, whose the number of nodes is fixed, become narrower during the simulation process, as the simulation grid is more densely informed. Hence, large scale structures are caught in the beginning of the simulation and small scale ones in the end. However, MPS may mix spatial characteristics distinguishable at different scales in the training image, and then loose the spatial arrangement of different structures. To overcome this limitation, we propose to perform MPS simulation using a decomposition of the training image in a set of images at multiple resolutions. Applying a Gaussian kernel onto the training image (convolution) results in a lower resolution image, and iterating this process, a pyramid of images depicting fewer details at each level is built, as it can be done in image processing for example to lighten the space storage of a photography. The direct sampling is then employed to simulate the lowest resolution level, and then to simulate each level, up to the finest resolution, conditioned to the level one rank coarser. This scheme helps reproduce the spatial structures at any scale of the training image and then generate more realistic models. We illustrate the method with aerial photographies (satellite images) and natural textures. Indeed, these kinds of images often display typical structures at different scales and are well-suited for MPS simulation techniques.

Reinforcement learning for a biped robot based on a CPG-actor-critic method.

PubMed

Nakamura, Yutaka; Mori, Takeshi; Sato, Masa-aki; Ishii, Shin

2007-08-01

Animals' rhythmic movements, such as locomotion, are considered to be controlled by neural circuits called central pattern generators (CPGs), which generate oscillatory signals. Motivated by this biological mechanism, studies have been conducted on the rhythmic movements controlled by CPG. As an autonomous learning framework for a CPG controller, we propose in this article a reinforcement learning method we call the "CPG-actor-critic" method. This method introduces a new architecture to the actor, and its training is roughly based on a stochastic policy gradient algorithm presented recently. We apply this method to an automatic acquisition problem of control for a biped robot. Computer simulations show that training of the CPG can be successfully performed by our method, thus allowing the biped robot to not only walk stably but also adapt to environmental changes.

Parametric motion control of robotic arms: A biologically based approach using neural networks

NASA Technical Reports Server (NTRS)

Bock, O.; D'Eleuterio, G. M. T.; Lipitkas, J.; Grodski, J. J.

1993-01-01

A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

Undesirable Choice Biases with Small Differences in the Spatial Structure of Chance Stimulus Sequences.

PubMed

Herrera, David; Treviño, Mario

2015-01-01

In two-alternative discrimination tasks, experimenters usually randomize the location of the rewarded stimulus so that systematic behavior with respect to irrelevant stimuli can only produce chance performance on the learning curves. One way to achieve this is to use random numbers derived from a discrete binomial distribution to create a 'full random training schedule' (FRS). When using FRS, however, sporadic but long laterally-biased training sequences occur by chance and such 'input biases' are thought to promote the generation of laterally-biased choices (i.e., 'output biases'). As an alternative, a 'Gellerman-like training schedule' (GLS) can be used. It removes most input biases by prohibiting the reward from appearing on the same location for more than three consecutive trials. The sequence of past rewards obtained from choosing a particular discriminative stimulus influences the probability of choosing that same stimulus on subsequent trials. Assuming that the long-term average ratio of choices matches the long-term average ratio of reinforcers, we hypothesized that a reduced amount of input biases in GLS compared to FRS should lead to a reduced production of output biases. We compared the choice patterns produced by a 'Rational Decision Maker' (RDM) in response to computer-generated FRS and GLS training sequences. To create a virtual RDM, we implemented an algorithm that generated choices based on past rewards. Our simulations revealed that, although the GLS presented fewer input biases than the FRS, the virtual RDM produced more output biases with GLS than with FRS under a variety of test conditions. Our results reveal that the statistical and temporal properties of training sequences interacted with the RDM to influence the production of output biases. Thus, discrete changes in the training paradigms did not translate linearly into modifications in the pattern of choices generated by a RDM. Virtual RDMs could be further employed to guide the selection of proper training schedules for perceptual decision-making studies.

Synthetic Training Data Generation for Activity Monitoring and Behavior Analysis

NASA Astrophysics Data System (ADS)

Monekosso, Dorothy; Remagnino, Paolo

This paper describes a data generator that produces synthetic data to simulate observations from an array of environment monitoring sensors. The overall goal of our work is to monitor the well-being of one occupant in a home. Sensors are embedded in a smart home to unobtrusively record environmental parameters. Based on the sensor observations, behavior analysis and modeling are performed. However behavior analysis and modeling require large data sets to be collected over long periods of time to achieve the level of accuracy expected. A data generator - was developed based on initial data i.e. data collected over periods lasting weeks to facilitate concurrent data collection and development of algorithms. The data generator is based on statistical inference techniques. Variation is introduced into the data using perturbation models.

Training the next generation of psychotraumatologists: COllaborative Network for Training and EXcellence in psychoTraumatology (CONTEXT)

PubMed Central

Vallières, Frédérique; Hyland, Philip; Murphy, Jamie; Hansen, Maj; Shevlin, Mark; Elklit, Ask; Ceannt, Ruth; Armour, Cherie; Wiedemann, Nana; Munk, Mette; Dinesen, Cecilie; O’Hare, Geraldine; Cunningham, Twylla; Askerod, Ditte; Spitz, Pernille; Blackwell, Noeline; McCarthy, Angela; O’Dowd, Leonie; Scott, Shirley; Reid, Tracey; Mokake, Andreas; Halpin, Rory; Perera, Camila; Gleeson, Christina; Frost, Rachel; Flanagan, Natalie; Aldamman, Kinan; Tamrakar, Trina; Louison Vang, Maria; Sherwood, Larissa; Travers, Áine; Haahr-Pedersen, Ida; Walshe, Catherine; McDonagh, Tracey; Bramsen, Rikke Holm

2018-01-01

ABSTRACT In this paper we present a description of the Horizon2020, Marie Skłodowska-Curie Action funded, research and training programme CONTEXT: COllaborative Network for Training and EXcellence in psychoTraumatology. The three objectives of the programme are put forward, each of which refers to a key component of the CONTEXT programme. First, we summarize the 12 individual research projects that will take place across three priority populations: (i) refugees and asylum seekers, (ii) first responders, and (iii) perpetrators and survivors of childhood and gender-based violence. Second, we detail the mentoring and training programme central to CONTEXT. Finally, we describe how the research, together with the training, will contribute towards better policy, guidelines, and practice within the field of psychotraumatology. PMID:29372015

Training the next generation of psychotraumatologists: COllaborative Network for Training and EXcellence in psychoTraumatology (CONTEXT).

PubMed

Vallières, Frédérique; Hyland, Philip; Murphy, Jamie; Hansen, Maj; Shevlin, Mark; Elklit, Ask; Ceannt, Ruth; Armour, Cherie; Wiedemann, Nana; Munk, Mette; Dinesen, Cecilie; O'Hare, Geraldine; Cunningham, Twylla; Askerod, Ditte; Spitz, Pernille; Blackwell, Noeline; McCarthy, Angela; O'Dowd, Leonie; Scott, Shirley; Reid, Tracey; Mokake, Andreas; Halpin, Rory; Perera, Camila; Gleeson, Christina; Frost, Rachel; Flanagan, Natalie; Aldamman, Kinan; Tamrakar, Trina; Louison Vang, Maria; Sherwood, Larissa; Travers, Áine; Haahr-Pedersen, Ida; Walshe, Catherine; McDonagh, Tracey; Bramsen, Rikke Holm

2018-01-01

In this paper we present a description of the Horizon2020, Marie Skłodowska-Curie Action funded, research and training programme CONTEXT: COllaborative Network for Training and EXcellence in psychoTraumatology. The three objectives of the programme are put forward, each of which refers to a key component of the CONTEXT programme. First, we summarize the 12 individual research projects that will take place across three priority populations: (i) refugees and asylum seekers, (ii) first responders, and (iii) perpetrators and survivors of childhood and gender-based violence. Second, we detail the mentoring and training programme central to CONTEXT. Finally, we describe how the research, together with the training, will contribute towards better policy, guidelines, and practice within the field of psychotraumatology.

A parallel decision tree-based method for user authentication based on keystroke patterns.

PubMed

Sheng, Yong; Phoha, Vir V; Rovnyak, Steven M

2005-08-01

We propose a Monte Carlo approach to attain sufficient training data, a splitting method to improve effectiveness, and a system composed of parallel decision trees (DTs) to authenticate users based on keystroke patterns. For each user, approximately 19 times as much simulated data was generated to complement the 387 vectors of raw data. The training set, including raw and simulated data, is split into four subsets. For each subset, wavelet transforms are performed to obtain a total of eight training subsets for each user. Eight DTs are thus trained using the eight subsets. A parallel DT is constructed for each user, which contains all eight DTs with a criterion for its output that it authenticates the user if at least three DTs do so; otherwise it rejects the user. Training and testing data were collected from 43 users who typed the exact same string of length 37 nine consecutive times to provide data for training purposes. The users typed the same string at various times over a period from November through December 2002 to provide test data. The average false reject rate was 9.62% and the average false accept rate was 0.88%.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research.

PubMed

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif

2016-03-11

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers-that we proposed earlier-improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

PubMed Central

Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-etriby, Sherif

2016-01-01

Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers—that we proposed earlier—improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction. PMID:26978368

The Role of Problem-Based Learning in Developing Creative Expertise

ERIC Educational Resources Information Center

Gallagher, Shelagh A.

2015-01-01

Contemporary real-world problems require creative solutions, necessitating the preparation of a new generation of creative experts capable of finding original solutions to ill-structured problems. Although much school-based training in creativity focuses on discrete skills, real-world creativity results from a multidimensional interaction between…

Automatic rule generation for high-level vision

NASA Technical Reports Server (NTRS)

Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

1992-01-01

Many high-level vision systems use rule-based approaches to solving problems such as autonomous navigation and image understanding. The rules are usually elaborated by experts. However, this procedure may be rather tedious. In this paper, we propose a method to generate such rules automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Generating description with multi-feature fusion and saliency maps of image

NASA Astrophysics Data System (ADS)

Liu, Lisha; Ding, Yuxuan; Tian, Chunna; Yuan, Bo

2018-04-01

Generating description for an image can be regard as visual understanding. It is across artificial intelligence, machine learning, natural language processing and many other areas. In this paper, we present a model that generates description for images based on RNN (recurrent neural network) with object attention and multi-feature of images. The deep recurrent neural networks have excellent performance in machine translation, so we use it to generate natural sentence description for images. The proposed method uses single CNN (convolution neural network) that is trained on ImageNet to extract image features. But we think it can not adequately contain the content in images, it may only focus on the object area of image. So we add scene information to image feature using CNN which is trained on Places205. Experiments show that model with multi-feature extracted by two CNNs perform better than which with a single feature. In addition, we make saliency weights on images to emphasize the salient objects in images. We evaluate our model on MSCOCO based on public metrics, and the results show that our model performs better than several state-of-the-art methods.

Research and analysis of head-directed area-of-interest visual system concepts

NASA Technical Reports Server (NTRS)

Sinacori, J. B.

1983-01-01

An analysis and survey with conjecture supporting a preliminary data base design is presented. The data base is intended for use in a Computer Image Generator visual subsystem for a rotorcraft flight simulator that is used for rotorcraft systems development, not training. The approach taken was to attempt to identify the visual perception strategies used during terrain flight, survey environmental and image generation factors, and meld these into a preliminary data base design. This design is directed at Data Base developers, and hopefully will stimulate and aid their efforts to evolve such a Base that will support simulation of terrain flight operations.

Enhancing Learning Outcomes with an Interactive Knowledge-Based Learning Environment Providing Narrative Feedback

ERIC Educational Resources Information Center

Stranieri, Andrew; Yearwood, John

2008-01-01

This paper describes a narrative-based interactive learning environment which aims to elucidate reasoning using interactive scenarios that may be used in training novices in decision-making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a…

TH-CD-202-07: A Methodology for Generating Numerical Phantoms for Radiation Therapy Using Geometric Attribute Distribution Models

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

Dolly, S; Chen, H; Mutic, S

Purpose: A persistent challenge for the quality assessment of radiation therapy treatments (e.g. contouring accuracy) is the absence of the known, ground truth for patient data. Moreover, assessment results are often patient-dependent. Computer simulation studies utilizing numerical phantoms can be performed for quality assessment with a known ground truth. However, previously reported numerical phantoms do not include the statistical properties of inter-patient variations, as their models are based on only one patient. In addition, these models do not incorporate tumor data. In this study, a methodology was developed for generating numerical phantoms which encapsulate the statistical variations of patients withinmore » radiation therapy, including tumors. Methods: Based on previous work in contouring assessment, geometric attribute distribution (GAD) models were employed to model both the deterministic and stochastic properties of individual organs via principle component analysis. Using pre-existing radiation therapy contour data, the GAD models are trained to model the shape and centroid distributions of each organ. Then, organs with different shapes and positions can be generated by assigning statistically sound weights to the GAD model parameters. Organ contour data from 20 retrospective prostate patient cases were manually extracted and utilized to train the GAD models. As a demonstration, computer-simulated CT images of generated numerical phantoms were calculated and assessed subjectively and objectively for realism. Results: A cohort of numerical phantoms of the male human pelvis was generated. CT images were deemed realistic both subjectively and objectively in terms of image noise power spectrum. Conclusion: A methodology has been developed to generate realistic numerical anthropomorphic phantoms using pre-existing radiation therapy data. The GAD models guarantee that generated organs span the statistical distribution of observed radiation therapy patients, according to the training dataset. The methodology enables radiation therapy treatment assessment with multi-modality imaging and a known ground truth, and without patient-dependent bias.« less

BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS.

PubMed

Hoff, Katharina J; Lange, Simone; Lomsadze, Alexandre; Borodovsky, Mark; Stanke, Mario

2016-03-01

Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation.

PubMed

Wang, Jun; Deng, Zhaohong; Luo, Xiaoqing; Jiang, Yizhang; Wang, Shitong

2016-06-01

Training feedforward neural networks (FNNs) is one of the most critical issues in FNNs studies. However, most FNNs training methods cannot be directly applied for very large datasets because they have high computational and space complexity. In order to tackle this problem, the CCMEB (Center-Constrained Minimum Enclosing Ball) problem in hidden feature space of FNN is discussed and a novel learning algorithm called HFSR-GCVM (hidden-feature-space regression using generalized core vector machine) is developed accordingly. In HFSR-GCVM, a novel learning criterion using L2-norm penalty-based ε-insensitive function is formulated and the parameters in the hidden nodes are generated randomly independent of the training sets. Moreover, the learning of parameters in its output layer is proved equivalent to a special CCMEB problem in FNN hidden feature space. As most CCMEB approximation based machine learning algorithms, the proposed HFSR-GCVM training algorithm has the following merits: The maximal training time of the HFSR-GCVM training is linear with the size of training datasets and the maximal space consumption is independent of the size of training datasets. The experiments on regression tasks confirm the above conclusions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mental Imagery-Based Training to Modify Mood and Cognitive Bias in Adolescents: Effects of Valence and Perspective.

PubMed

Burnett Heyes, S; Pictet, A; Mitchell, H; Raeder, S M; Lau, J Y F; Holmes, E A; Blackwell, S E

2017-01-01

Mental imagery has a powerful impact on emotion and cognitive processing in adults, and is implicated in emotional disorders. Research suggests the perspective adopted in mental imagery modulates its emotional impact. However, little is known about the impact of mental imagery in adolescence, despite adolescence being the key time for the onset of emotional dysfunction. We administered computerised positive versus mixed valence picture-word mental imagery training to male adolescent participants (N = 60, aged 11-16 years) across separate field and observer perspective sessions. Positive mood increased more following positive than mixed imagery; pleasantness ratings of ambiguous pictures increased following positive versus mixed imagery generated from field but not observer perspective; negative interpretation bias on a novel scrambled sentences task was smaller following positive than mixed imagery particularly when imagery was generated from field perspective. These findings suggest positive mental imagery generation alters mood and cognition in male adolescents, with the latter moderated by imagery perspective. Identifying key components of such training, such as imagery perspective, extends understanding of the relationship between mental imagery, mood, and cognition in adolescence.

Ponderomotive Generation and Detection of Attosecond Free-Electron Pulse Trains

NASA Astrophysics Data System (ADS)

Kozák, M.; Schönenberger, N.; Hommelhoff, P.

2018-03-01

Atomic motion dynamics during structural changes or chemical reactions have been visualized by pico- and femtosecond pulsed electron beams via ultrafast electron diffraction and microscopy. Imaging the even faster dynamics of electrons in atoms, molecules, and solids requires electron pulses with subfemtosecond durations. We demonstrate here the all-optical generation of trains of attosecond free-electron pulses. The concept is based on the periodic energy modulation of a pulsed electron beam via an inelastic interaction, with the ponderomotive potential of an optical traveling wave generated by two femtosecond laser pulses at different frequencies in vacuum. The subsequent dispersive propagation leads to a compression of the electrons and the formation of ultrashort pulses. The longitudinal phase space evolution of the electrons after compression is mapped by a second phase-locked interaction. The comparison of measured and calculated spectrograms reveals the attosecond temporal structure of the compressed electron pulse trains with individual pulse durations of less than 300 as. This technique can be utilized for tailoring and initial characterization of suboptical-cycle free-electron pulses at high repetition rates for stroboscopic time-resolved experiments with subfemtosecond time resolution.

Integrating conventional and inverse representation for face recognition.

PubMed

Xu, Yong; Li, Xuelong; Yang, Jian; Lai, Zhihui; Zhang, David

2014-10-01

Representation-based classification methods are all constructed on the basis of the conventional representation, which first expresses the test sample as a linear combination of the training samples and then exploits the deviation between the test sample and the expression result of every class to perform classification. However, this deviation does not always well reflect the difference between the test sample and each class. With this paper, we propose a novel representation-based classification method for face recognition. This method integrates conventional and the inverse representation-based classification for better recognizing the face. It first produces conventional representation of the test sample, i.e., uses a linear combination of the training samples to represent the test sample. Then it obtains the inverse representation, i.e., provides an approximation representation of each training sample of a subject by exploiting the test sample and training samples of the other subjects. Finally, the proposed method exploits the conventional and inverse representation to generate two kinds of scores of the test sample with respect to each class and combines them to recognize the face. The paper shows the theoretical foundation and rationale of the proposed method. Moreover, this paper for the first time shows that a basic nature of the human face, i.e., the symmetry of the face can be exploited to generate new training and test samples. As these new samples really reflect some possible appearance of the face, the use of them will enable us to obtain higher accuracy. The experiments show that the proposed conventional and inverse representation-based linear regression classification (CIRLRC), an improvement to linear regression classification (LRC), can obtain very high accuracy and greatly outperforms the naive LRC and other state-of-the-art conventional representation based face recognition methods. The accuracy of CIRLRC can be 10% greater than that of LRC.

Embedded system based on PWM control of hydrogen generator with SEPIC converter

NASA Astrophysics Data System (ADS)

Fall, Cheikh; Setiawan, Eko; Habibi, Muhammad Afnan; Hodaka, Ichijo

2017-09-01

The objective of this paper is to design and to produce a micro electrical plant system based on fuel cell for teaching material-embedded systems in technical vocational training center. Based on this, the student can experience generating hydrogen by fuel cells, controlling the rate of hydrogen generation by the duty ration of single-ended primary-inductor converter(SEPIC), drawing the curve rate of hydrogen to duty ratio, generating electrical power by using hydrogen, and calculating the fuel cell efficiency when it is used as electrical energy generator. This project is of great importance insofar as students will need to acquire several skills to be able to realize it such as continuous DC DC conversion and the scientific concept behind the converter, the regulation of systems with integral proportional controllers, the installation of photovoltaic cells, the use of high-tech sensors, microcontroller programming, object-oriented programming, mastery of the fuel cell syste

Raster Scan Computer Image Generation (CIG) System Based On Refresh Memory

NASA Astrophysics Data System (ADS)

Dichter, W.; Doris, K.; Conkling, C.

1982-06-01

A full color, Computer Image Generation (CIG) raster visual system has been developed which provides a high level of training sophistication by utilizing advanced semiconductor technology and innovative hardware and firmware techniques. Double buffered refresh memory and efficient algorithms eliminate the problem of conventional raster line ordering by allowing the generated image to be stored in a random fashion. Modular design techniques and simplified architecture provide significant advantages in reduced system cost, standardization of parts, and high reliability. The major system components are a general purpose computer to perform interfacing and data base functions; a geometric processor to define the instantaneous scene image; a display generator to convert the image to a video signal; an illumination control unit which provides final image processing; and a CRT monitor for display of the completed image. Additional optional enhancements include texture generators, increased edge and occultation capability, curved surface shading, and data base extensions.

Muscle function in aged women in response to a water-based exercises program and progressive resistance training.

PubMed

Bento, Paulo Cesar Barauce; Rodacki, André Luiz Felix

2015-11-01

The purpose of the present study was to determine the effects of a water-based exercise program on muscle function compared with regular high-intensity resistance training. Older women (n = 87) were recruited from the local community. The inclusion criteria were, to be aged 60 years or older, able to walk and able to carry out daily living activities independently. Participants were randomly assigned to one of the following groups: water-based exercises (WBG), resistance training (RTG) or control (CG). The experimental groups carried out 12 weeks of an excise program performed on water or on land. The dynamic strength, the isometric peak, and rate of torque development for the lower limbs were assessed before and after interventions. The water-based program provided a similar improvement in dynamic strength in comparison with resistance training. The isometric peak torque increased around the hip and ankle joints in the water-based group, and around the knee joint in the resistance-training group (P < 0.05). The rate of torque development increased only in the water-based group around the hip extensors muscles (P < 0.05). Water-based programs constitute an attractive alternative to promote relevant strength gains using moderate loads and fast speed movements, which were also effective to improve the capacity to generate fast torques. © 2014 Japan Geriatrics Society.

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction.

PubMed

Ravì, Daniele; Szczotka, Agnieszka Barbara; Shakir, Dzhoshkun Ismail; Pereira, Stephen P; Vercauteren, Tom

2018-06-01

Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive image quality assessment that takes into account different quality scores, including a Mean Opinion Score (MOS). Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images.

Retrospective analysis of dental implants placed and restored by advanced prosthodontic residents.

PubMed

Barias, Pamela A; Lee, Damian J; Yuan, Judy Chia-Chun; Sukotjo, Cortino; Campbell, Stephen D; Knoernschild, Kent L

2013-02-01

The purposes of this retrospective clinical review were to: (1) describe the demographics of implant patients, types of implant treatment and implant-supported prostheses in an Advanced Education in Prosthodontic Program, (2) evaluate the survival rate of dental implants placed by prosthodontic residents from 2006 to 2008, and (3) analyze the relationship between resident year of training and implant survival rate. All patients who received dental implants placed by prosthodontic residents from January 2006 to October of 2008 in the Advanced Prosthodontic Program at the University of Illinois at Chicago College of Dentistry were selected for this study. Age, gender, implant diameter, length, implant locations, surgical and restorative detail, and year of prosthodontic residency training were collected and analyzed. Life-table and Kaplan-Meier survival analyses were performed based on implants overall, locations, year of training, and use of a computer-generated surgical guide. A Logrank statistic was performed between implant survival and year of prosthodontic residency training, location, and use of computer-generated surgical guide (α= 0.05). Three hundred and six implants were placed, and of these, seven failed. Life-table and Kaplan-Meier analyses computed a cumulative survival rate (CSR) of 97% for overall implants and implants placed with a computer-generated surgical guide. No statistical difference was found in implant survival rates as a function of year of training (P= 0.85). Dental implants placed by prosthodontic residents had a CSR comparable to previously published studies by other specialties. The year of prosthodontic residency training and implant failure rate did not have any significant relationship. © 2012 by the American College of Prosthodontists.

ezTag: tagging biomedical concepts via interactive learning.

PubMed

Kwon, Dongseop; Kim, Sun; Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

2018-05-18

Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org.

Generation of Synthetic Spike Trains with Defined Pairwise Correlations

PubMed Central

Niebur, Ernst

2008-01-01

Recent technological advances as well as progress in theoretical understanding of neural systems have created a need for synthetic spike trains with controlled mean rate and pairwise cross-correlation. This report introduces and analyzes a novel algorithm for the generation of discretized spike trains with arbitrary mean rates and controlled cross correlation. Pairs of spike trains with any pairwise correlation can be generated, and higher-order correlations are compatible with common synaptic input. Relations between allowable mean rates and correlations within a population are discussed. The algorithm is highly efficient, its complexity increasing linearly with the number of spike trains generated and therefore inversely with the number of cross-correlated pairs. PMID:17521277

Superconducting wind turbine generators

NASA Astrophysics Data System (ADS)

Abrahamsen, A. B.; Mijatovic, N.; Seiler, E.; Zirngibl, T.; Træholt, C.; Nørgård, P. B.; Pedersen, N. F.; Andersen, N. H.; Østergård, J.

2010-03-01

We have examined the potential of 10 MW superconducting direct drive generators to enter the European offshore wind power market and estimated that the production of about 1200 superconducting turbines until 2030 would correspond to 10% of the EU offshore market. The expected properties of future offshore turbines of 8 and 10 MW have been determined from an up-scaling of an existing 5 MW turbine and the necessary properties of the superconducting drive train are discussed. We have found that the absence of the gear box is the main benefit and the reduced weight and size is secondary. However, the main challenge of the superconducting direct drive technology is to prove that the reliability is superior to the alternative drive trains based on gearboxes or permanent magnets. A strategy of successive testing of superconducting direct drive trains in real wind turbines of 10 kW, 100 kW, 1 MW and 10 MW is suggested to secure the accumulation of reliability experience. Finally, the quantities of high temperature superconducting tape needed for a 10 kW and an extreme high field 10 MW generator are found to be 7.5 km and 1500 km, respectively. A more realistic estimate is 200-300 km of tape per 10 MW generator and it is concluded that the present production capacity of coated conductors must be increased by a factor of 36 by 2020, resulting in a ten times lower price of the tape in order to reach a realistic price level for the superconducting drive train.

The National Institute of Environmental Health Sciences Superfund Research Program: a model for multidisciplinary training of the next generation of environmental health scientists.

PubMed

Carlin, Danielle J; Henry, Heather; Heacock, Michelle; Trottier, Brittany; Drew, Christina H; Suk, William A

2018-03-28

The National Institute of Environmental Health Sciences (NIEHS) Superfund Research Program (SRP) funds university-based, multidisciplinary research on human health and environmental science and engineering with the central goals to understand how hazardous substances contribute to disease and how to prevent exposures to these environmental chemicals. This multi-disciplinary approach allows early career scientists (e.g. graduate students and postdoctoral researchers) to gain experience in problem-based, solution-oriented research and to conduct research in a highly collaborative environment. Training the next generation of environmental health scientists has been an important part of the SRP since its inception. In addition to basic research, the SRP has grown to include support of broader training experiences such as those in research translation and community engagement activities that provide opportunities to give new scientists many of the skills they will need to be successful in their field of research. Looking to the future, the SRP will continue to evolve its training component by tracking and analyzing outcomes from its trainees by using tools such as the NIEHS CareerTrac database system, by increasing opportunities for trainees interested in research that goes beyond US boundaries, and in the areas of bioinformatics and data integration. These opportunities will give them the skills needed to be competitive and successful no matter which employment sector they choose to enter after they have completed their training experience.

The Effects of Long-term Abacus Training on Topological Properties of Brain Functional Networks.

PubMed

Weng, Jian; Xie, Ye; Wang, Chunjie; Chen, Feiyan

2017-08-18

Previous studies in the field of abacus-based mental calculation (AMC) training have shown that this training has the potential to enhance a wide variety of cognitive abilities. It can also generate specific changes in brain structure and function. However, there is lack of studies investigating the impact of AMC training on the characteristics of brain networks. In this study, utilizing graph-based network analysis, we compared topological properties of brain functional networks between an AMC group and a matched control group. Relative to the control group, the AMC group exhibited higher nodal degrees in bilateral calcarine sulcus and increased local efficiency in bilateral superior occipital gyrus and right cuneus. The AMC group also showed higher nodal local efficiency in right fusiform gyrus, which was associated with better math ability. However, no relationship was significant in the control group. These findings provide evidence that long-term AMC training may improve information processing efficiency in visual-spatial related regions, which extend our understanding of training plasticity at the brain network level.

Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

PubMed

Mostafa, Hesham

2017-08-01

Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

The Web-based CanMEDS Resident Learning Portfolio Project (WEBCAM): how we got started.

PubMed

Glen, Peter; Balaa, Fady; Momoli, Franco; Martin, Louise; Found, Dorothy; Arnaout, Angel

2016-12-01

The CanMEDS framework is ubiquitous in Canadian postgraduate medical education; however, training programs do not have a universal method of assessing competence. We set out to develop a novel portfolio that allowed trainees to generate a longitudinal record of their training and development within the framework. The portfolio provided an objective means for the residency program director to document and evaluate resident progress within the CanMEDS roles.

Immersive virtual reality platform for medical training: a "killer-application".

PubMed

2000-01-01

The Medical Readiness Trainer (MRT) integrates fully immersive Virtual Reality (VR), highly advanced medical simulation technologies, and medical data to enable unprecedented medical education and training. The flexibility offered by the MRT environment serves as a practical teaching tool today and in the near future the will serve as an ideal vehicle for facilitating the transition to the next level of medical practice, i.e., telepresence and next generation Internet-based collaborative learning.

Automated Scenario Generation: Toward Tailored and Optimized Military Training in Virtual Environments

DTIC Science & Technology

2012-01-01

us.army.mil ABSTRACT Scenario-based training exemplifies the learning-by-doing approach to human performance improvement. In this paper , we enumerate...through a narrative, mission, quest, or scenario. In this paper we argue for a combinatorial optimization search approach to selecting and ordering...the role of an expert for the purposes of practicing skills and knowledge in realistic situations in a learning-by-doing approach to performance

DeepMoon: Convolutional neural network trainer to identify moon craters

NASA Astrophysics Data System (ADS)

Silburt, Ari; Zhu, Chenchong; Ali-Dib, Mohamad; Menou, Kristen; Jackson, Alan

2018-05-01

DeepMoon trains a convolutional neural net using data derived from a global digital elevation map (DEM) and catalog of craters to recognize craters on the Moon. The TensorFlow-based pipeline code is divided into three parts. The first generates a set images of the Moon randomly cropped from the DEM, with corresponding crater positions and radii. The second trains a convnet using this data, and the third validates the convnet's predictions.

Air Force Civil Engineer, Volume 9, Number 1, Spring 2001

DTIC Science & Technology

2001-01-01

generated some important lessons learned. The Gulf War was a wakeup call for contingency training. When it began, many in CE had never trained on bare...square foot, corrosion control facility at Charleston Air Force Base, S.C. Construction is scheduled for comple- tion in early 2002. The facility is...Rhein Main Transition Program. This program, scheduled for completion in 2005, transfers operational capability from Rhein Main AB to Spangdahlem and

Centralized, capacity-building training of Lichtenstein hernioplasty in Brazil.

PubMed

Moore, Alexandra M; Datta, Néha; Wagner, Justin P; Schroeder, Alexander D; Reinpold, Wolfgang; Franciss, Maurice Y; Silva, Rodrigo A; Chen, David C; Filipi, Charles J; Roll, Sergio

2017-02-01

In Brazil, access to healthcare varies widely by community. Options for repair of surgically correctable conditions, such as inguinal hernias, are limited. A training program was instituted to expand access to Lichtenstein hernioplasty. Between September, 2014 and September, 2015, 3 orders of training series took place in São Paulo, Brazil. Participating surgeons received training and assessments from expert trainers using the Operative Performance Rating Scale (OPRS). Those who completed training successfully were invited to become trainers. OPRS scores were compared between training series. Outcomes were documented up to 6 months post-training. The 3 orders of training series resulted in 45 surgeons trained and 213 hernias repaired. Eleven trainees subsequently became trainers. Mean post-training OPRS scores were 4.4 (scale of 5) and did not vary significantly between training series. The overall complication rate was 4.7%, with no hernia recurrences or reoperations at 6 months. Competency-based training generates a regional network of surgeons proficient in Lichtenstein hernioplasty. Each training session progressively expands patient access to high quality operations in underserved communities in Brazil. Copyright © 2016 Elsevier Inc. All rights reserved.

Domain Regeneration for Cross-Database Micro-Expression Recognition

NASA Astrophysics Data System (ADS)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

The Development and Implementation of a Competency-Based Curriculum for Training in Global Health Research

PubMed Central

Ton, Thanh G. N.; Gladding, Sophia P.; Zunt, Joseph R.; John, Chandy; Nerurkar, Vivek R.; Moyer, Cheryl A.; Hobbs, Nicole; McCoy, Molly; Kolars, Joseph C.

2015-01-01

The Fogarty International Center (FIC) Global Health Fellows Program provides trainees with the opportunity to develop research skills through a mentored research experience, increase their content expertise, and better understand trends in global health research, funding organizations, and pathways to generate support. The Northern Pacific Global Health Fellows Research and Training Consortium, which hosts one of the FIC Global Health Programs, sought to enhance research training by developing, implementing, and evaluating a competency-based curriculum that uses a modular, asynchronous, web-based format. The curriculum has 8 core competencies, 36 learning objectives, and 58 assignments. Nineteen trainees completed their 11-month fellowship, engaged in the curriculum, and provided pre- and post-fellowship self-assessments. Self-assessed scores significantly improved for all competencies. Trainees identified the curriculum as one of the strengths of the program. This competency-based curriculum represents a first step toward creating a framework of global health research competencies on which further efforts could be based. PMID:25371189

Optimizing Preseason Training Loads in Australian Football.

PubMed

Carey, David L; Crow, Justin; Ong, Kok-Leong; Blanch, Peter; Morris, Meg E; Dascombe, Ben J; Crossley, Kay M

2018-02-01

To investigate whether preseason training plans for Australian football can be computer generated using current training-load guidelines to optimize injury-risk reduction and performance improvement. A constrained optimization problem was defined for daily total and sprint distance, using the preseason schedule of an elite Australian football team as a template. Maximizing total training volume and maximizing Banister-model-projected performance were both considered optimization objectives. Cumulative workload and acute:chronic workload-ratio constraints were placed on training programs to reflect current guidelines on relative and absolute training loads for injury-risk reduction. Optimization software was then used to generate preseason training plans. The optimization framework was able to generate training plans that satisfied relative and absolute workload constraints. Increasing the off-season chronic training loads enabled the optimization algorithm to prescribe higher amounts of "safe" training and attain higher projected performance levels. Simulations showed that using a Banister-model objective led to plans that included a taper in training load prior to competition to minimize fatigue and maximize projected performance. In contrast, when the objective was to maximize total training volume, more frequent training was prescribed to accumulate as much load as possible. Feasible training plans that maximize projected performance and satisfy injury-risk constraints can be automatically generated by an optimization problem for Australian football. The optimization methods allow for individualized training-plan design and the ability to adapt to changing training objectives and different training-load metrics.

Installation Restoration Program Records Search for Davis-Monthan Air Force Base, Arizona.

DTIC Science & Technology

1982-08-01

inspection labs, and corrosion -2- control shops. These industrial operations generate varying quantities of waste oils , fuels , *solvents, and cleaners. The...standard procedures for the disposition of the majority of the waste oils , fuels , solvents, and cleaners has been (1) fire department training...and corrosion control shops. These industrial operations generate varying quantities of waste oils , fuels , solvents, and cleaners. The total quantity

Improved Volitional Recall of Motor-Imagery-Related Brain Activation Patterns Using Real-Time Functional MRI-Based Neurofeedback.

PubMed

Bagarinao, Epifanio; Yoshida, Akihiro; Ueno, Mika; Terabe, Kazunori; Kato, Shohei; Isoda, Haruo; Nakai, Toshiharu

2018-01-01

Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain-computer/brain-machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training. The participants' performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns.

Neural networks for structural design - An integrated system implementation

NASA Technical Reports Server (NTRS)

Berke, Laszlo; Hafez, Wassim; Pao, Yoh-Han

1992-01-01

The development of powerful automated procedures to aid the creative designer is becoming increasingly critical for complex design tasks. In the work described here Artificial Neural Nets are applied to acquire structural analysis and optimization domain expertise. Based on initial instructions from the user an automated procedure generates random instances of structural analysis and/or optimization 'experiences' that cover a desired domain. It extracts training patterns from the created instances, constructs and trains an appropriate network architecture and checks the accuracy of net predictions. The final product is a trained neural net that can estimate analysis and/or optimization results instantaneously.

A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

PubMed Central

Ye, Qing; Pan, Hao; Liu, Changhua

2015-01-01

This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

NASA Astrophysics Data System (ADS)

Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

2016-03-01

The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

Design of Neural Networks for Fast Convergence and Accuracy: Dynamics and Control

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Sparks, Dean W., Jr.

1997-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Design of neural networks for fast convergence and accuracy: dynamics and control.

PubMed

Maghami, P G; Sparks, D R

2000-01-01

A procedure for the design and training of artificial neural networks, used for rapid and efficient controls and dynamics design and analysis for flexible space systems, has been developed. Artificial neural networks are employed, such that once properly trained, they provide a means of evaluating the impact of design changes rapidly. Specifically, two-layer feedforward neural networks are designed to approximate the functional relationship between the component/spacecraft design changes and measures of its performance or nonlinear dynamics of the system/components. A training algorithm, based on statistical sampling theory, is presented, which guarantees that the trained networks provide a designer-specified degree of accuracy in mapping the functional relationship. Within each iteration of this statistical-based algorithm, a sequential design algorithm is used for the design and training of the feedforward network to provide rapid convergence to the network goals. Here, at each sequence a new network is trained to minimize the error of previous network. The proposed method should work for applications wherein an arbitrary large source of training data can be generated. Two numerical examples are performed on a spacecraft application in order to demonstrate the feasibility of the proposed approach.

Adapting Training to Meet the Preferred Learning Styles of Different Generations

ERIC Educational Resources Information Center

Urick, Michael

2017-01-01

This article considers how training professionals can respond to differences in training preferences between generational groups. It adopts two methods. First, it surveys the existing research and finds generally that preferences for training approaches can differ between groups and specifically that younger employees are perceived to leverage…

Smoke detection

DOEpatents

Warmack, Robert J. Bruce; Wolf, Dennis A.; Frank, Steven Shane

2016-09-06

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

Smoke detection

DOEpatents

Warmack, Robert J. Bruce; Wolf, Dennis A.; Frank, Steven Shane

2015-10-27

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDA training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.

Competency based training in robotic surgery: benchmark scores for virtual reality robotic simulation.

PubMed

Raison, Nicholas; Ahmed, Kamran; Fossati, Nicola; Buffi, Nicolò; Mottrie, Alexandre; Dasgupta, Prokar; Van Der Poel, Henk

2017-05-01

To develop benchmark scores of competency for use within a competency based virtual reality (VR) robotic training curriculum. This longitudinal, observational study analysed results from nine European Association of Urology hands-on-training courses in VR simulation. In all, 223 participants ranging from novice to expert robotic surgeons completed 1565 exercises. Competency was set at 75% of the mean expert score. Benchmark scores for all general performance metrics generated by the simulator were calculated. Assessment exercises were selected by expert consensus and through learning-curve analysis. Three basic skill and two advanced skill exercises were identified. Benchmark scores based on expert performance offered viable targets for novice and intermediate trainees in robotic surgery. Novice participants met the competency standards for most basic skill exercises; however, advanced exercises were significantly more challenging. Intermediate participants performed better across the seven metrics but still did not achieve the benchmark standard in the more difficult exercises. Benchmark scores derived from expert performances offer relevant and challenging scores for trainees to achieve during VR simulation training. Objective feedback allows both participants and trainers to monitor educational progress and ensures that training remains effective. Furthermore, the well-defined goals set through benchmarking offer clear targets for trainees and enable training to move to a more efficient competency based curriculum. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

Integrating research into clinical internship training bridging the science/practice gap in pediatric psychology.

PubMed

McQuaid, Elizabeth L; Spirito, Anthony

2012-03-01

Existing literature highlights a critical gap between science and practice in clinical psychology. The internship year is a "capstone experience"; training in methods of scientific evaluation should be integrated with the development of advanced clinical competencies. We provide a rationale for continued exposure to research during the clinical internship year, including, (a) critical examination and integration of the literature regarding evidence-based treatment and assessment, (b) participation in faculty-based and independent research, and (c) orientation to the science and strategy of grantsmanship. Participation in research provides exposure to new empirical models and can foster the development of applied research questions. Orientation to grantsmanship can yield an initial sense of the "business of science." Internship provides an important opportunity to examine the challenges to integrating the clinical evidence base into professional practice; for that reason, providing research exposure on internship is an important strategy in training the next generation of pediatric psychologists.

Weighted Discriminative Dictionary Learning based on Low-rank Representation

NASA Astrophysics Data System (ADS)

Chang, Heyou; Zheng, Hao

2017-01-01

Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods.

Infrared small target detection based on Danger Theory

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Yang, Xiao

2009-11-01

To solve the problem that traditional method can't detect the small objects whose local SNR is less than 2 in IR images, a Danger Theory-based model to detect infrared small target is presented in this paper. First, on the analog with immunology, the definition is given, in this paper, to such terms as dangerous signal, antigens, APC, antibodies. Besides, matching rule between antigen and antibody is improved. Prior to training the detection model and detecting the targets, the IR images are processed utilizing adaptive smooth filter to decrease the stochastic noise. Then at the training process, deleting rule, generating rule, crossover rule and the mutation rule are established after a large number of experiments in order to realize immediate convergence and obtain good antibodies. The Danger Theory-based model is built after the training process, and this model can detect the target whose local SNR is only 1.5.

Consensus development of core competencies in intensive and critical care medicine training in China.

PubMed

Hu, Xiaoyun; Xi, Xiuming; Ma, Penglin; Qiu, Haibo; Yu, Kaijiang; Tang, Yaoqing; Qian, Chuanyun; Fang, Qiang; Wang, Yushan; Yu, Xiangyou; Xu, Yuan; Du, Bin

2016-10-16

The aim of this study is to develop consensus on core competencies required for postgraduate training in intensive care medicine. We used a combination of a modified Delphi method and a nominal group technique to create and modify the list of core competencies to ensure maximum consensus. Ideas were generated modified from Competency Based Training in Intensive Care Medicine in Europe collaboration (CoBaTrICE) core competencies. An online survey invited healthcare professionals, educators, and trainees to rate and comment on these competencies. The output from the online survey was edited and then reviewed by a nominal group of 13 intensive care professionals to identify each competence for importance. The resulting list was then recirculated in the nominal group for iterative rating. The online survey yielded a list of 199 competencies for nominal group reviewing. After five rounds of rating, 129 competencies entered the final set defined as core competencies. We have generated a set of core competencies using a consensus technique which can serve as an indicator for training program development.

Knowledge-based reasoning in the Paladin tactical decision generation system

NASA Technical Reports Server (NTRS)

Chappell, Alan R.

1993-01-01

A real-time tactical decision generation system for air combat engagements, Paladin, has been developed. A pilot's job in air combat includes tasks that are largely symbolic. These symbolic tasks are generally performed through the application of experience and training (i.e. knowledge) gathered over years of flying a fighter aircraft. Two such tasks, situation assessment and throttle control, are identified and broken out in Paladin to be handled by specialized knowledge based systems. Knowledge pertaining to these tasks is encoded into rule-bases to provide the foundation for decisions. Paladin uses a custom built inference engine and a partitioned rule-base structure to give these symbolic results in real-time. This paper provides an overview of knowledge-based reasoning systems as a subset of rule-based systems. The knowledge used by Paladin in generating results as well as the system design for real-time execution is discussed.

The effect of Web-based Braden Scale training on the reliability and precision of Braden Scale pressure ulcer risk assessments.

PubMed

Magnan, Morris A; Maklebust, Joann

2008-01-01

To evaluate the effect of Web-based Braden Scale training on the reliability and precision of pressure ulcer risk assessments made by registered nurses (RN) working in acute care settings. Pretest-posttest, 2-group, quasi-experimental design. Five hundred Braden Scale risk assessments were made on 102 acute care patients deemed to be at various levels of risk for pressure ulceration. Assessments were made by RNs working in acute care hospitals at 3 different medical centers where the Braden Scale was in regular daily use (2 medical centers) or new to the setting (1 medical center). The Braden Scale for Predicting Pressure Sore Risk was used to guide pressure ulcer risk assessments. A Web-based version of the Detroit Medical Center Braden Scale Computerized Training Module was used to teach nurses correct use of the Braden Scale and selection of risk-based pressure ulcer prevention interventions. In the aggregate, RN generated reliable Braden Scale pressure ulcer risk assessments 65% of the time after training. The effect of Web-based Braden Scale training on reliability and precision of assessments varied according to familiarity with the scale. With training, new users of the scale made reliable assessments 84% of the time and significantly improved precision of their assessments. The reliability and precision of Braden Scale risk assessments made by its regular users was unaffected by training. Technology-assisted Braden Scale training improved both reliability and precision of risk assessments made by new users of the scale, but had virtually no effect on the reliability or precision of risk assessments made by regular users of the instrument. Further research is needed to determine best approaches for improving reliability and precision of Braden Scale assessments made by its regular users.

Integrating knowledge and control into hypermedia-based training environments: Experiments with HyperCLIPS

NASA Technical Reports Server (NTRS)

Hill, Randall W., Jr.

1990-01-01

The issues of knowledge representation and control in hypermedia-based training environments are discussed. The main objective is to integrate the flexible presentation capability of hypermedia with a knowledge-based approach to lesson discourse management. The instructional goals and their associated concepts are represented in a knowledge representation structure called a 'concept network'. Its functional usages are many: it is used to control the navigation through a presentation space, generate tests for student evaluation, and model the student. This architecture was implemented in HyperCLIPS, a hybrid system that creates a bridge between HyperCard, a popular hypertext-like system used for building user interfaces to data bases and other applications, and CLIPS, a highly portable government-owned expert system shell.

Creating a medical dictionary using word alignment: the influence of sources and resources.

PubMed

Nyström, Mikael; Merkel, Magnus; Petersson, Håkan; Ahlfeldt, Hans

2007-11-23

Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10.

Creating a medical dictionary using word alignment: The influence of sources and resources

PubMed Central

Nyström, Mikael; Merkel, Magnus; Petersson, Håkan; Åhlfeldt, Hans

2007-01-01

Background Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. Methods We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. Results The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base forms. Conclusion More resources give better results in the automatic word alignment, but some resources only give small improvements. The most important type of resource is training and the most general resources were generated from ICD-10. PMID:18036221

Variation of Extreme and Fatigue Design Loads on the Main Bearing of a Front Mounted Direct Drive System

NASA Astrophysics Data System (ADS)

Abrahamsen, Asger Bech; Natarajan, Anand

2016-09-01

The drivetrain of a 10 MW wind turbine has been designed as a direct drive transmission with a superconducting generator mounted in front of the hub and connected to the main frame through a King-pin stiff assembly by DNV-GL. The aeroelastic design loads of such an arrangement are evaluated based on the thrust and bending moments at the main bearing, both for ultimate design and in fatigue. It is found that the initial superconductor generator weight of 363 tons must be reduced by 25% in order not to result in higher extreme loads on main and yaw bearing than the reference10 MW geared reference drive train. A weight reduction of 50% is needed in order to maintain main bearing fatigue damage equivalent to the reference drive train. Thus a target mass of front mounted superconducting direct drive generators is found to be between 183-272 tons.

Classification of urine sediment based on convolution neural network

NASA Astrophysics Data System (ADS)

Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian

2018-04-01

By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.

Computer-assisted generation of individual training concepts for advanced education in manufacturing metrology

NASA Astrophysics Data System (ADS)

Werner, Teresa; Weckenmann, Albert

2010-05-01

Due to increasing requirements on the accuracy and reproducibility of measurement results together with a rapid development of novel technologies for the execution of measurements, there is a high demand for adequately qualified metrologists. Accordingly, a variety of training offers are provided by machine manufacturers, universities and other institutions. Yet, for an interested learner it is very difficult to define an optimal training schedule for his/her individual demands. Therefore, a computer-based assistance tool is developed to support a demand-responsive scheduling of training. Based on the difference between the actual and intended competence profile and under consideration of amending requirements, an optimally customized qualification concept is derived. For this, available training offers are categorized according to different dimensions: regarding contents of the course, but also intended target groups, focus of the imparted competences, implemented methods of learning and teaching, expected constraints for learning and necessary preknowledge. After completing a course, the achieved competences and the transferability of gathered knowledge are evaluated. Based on the results, recommendations for amending measures of learning are provided. Thus, a customized qualification for manufacturing metrology is facilitated, adapted to the specific needs and constraints of each individual learner.

AVESTAR Center for Operational Excellence of Electricity Generation Plants

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

Zitney, Stephen

2012-08-29

To address industry challenges in attaining operational excellence for electricity generation plants, the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) has launched a world-class facility for Advanced Virtual Energy Simulation Training and Research (AVESTARTM). This presentation will highlight the AVESTARTM Center simulators, facilities, and comprehensive training, education, and research programs focused on the operation and control of high-efficiency, near-zero-emission electricity generation plants. The AVESTAR Center brings together state-of-the-art, real-time, high-fidelity dynamic simulators with full-scope operator training systems (OTSs) and 3D virtual immersive training systems (ITSs) into an integrated energy plant and control room environment. AVESTAR’s initial offeringmore » combines--for the first time--a “gasification with CO2 capture” process simulator with a “combined-cycle” power simulator together in a single OTS/ITS solution for an integrated gasification combined cycle (IGCC) power plant with carbon dioxide (CO2) capture. IGCC systems are an attractive technology option for power generation, especially when capturing and storing CO2 is necessary to satisfy emission targets. The AVESTAR training program offers a variety of courses that merge classroom learning, simulator-based OTS learning in a control-room operations environment, and immersive learning in the interactive 3D virtual plant environment or ITS. All of the courses introduce trainees to base-load plant operation, control, startups, and shutdowns. Advanced courses require participants to become familiar with coordinated control, fuel switching, power-demand load shedding, and load following, as well as to problem solve equipment and process malfunctions. Designed to ensure work force development, training is offered for control room and plant field operators, as well as engineers and managers. Such comprehensive simulator-based instruction allows for realistic training without compromising worker, equipment, and environmental safety. It also better prepares operators and engineers to manage the plant closer to economic constraints while minimizing or avoiding the impact of any potentially harmful, wasteful, or inefficient events. The AVESTAR Center is also used to augment graduate and undergraduate engineering education in the areas of process simulation, dynamics, control, and safety. Students and researchers gain hands-on simulator-based training experience and learn how the commercial-scale power plants respond dynamically to changes in manipulated inputs, such as coal feed flow rate and power demand. Students also analyze how the regulatory control system impacts power plant performance and stability. In addition, students practice start-up, shutdown, and malfunction scenarios. The 3D virtual ITSs are used for plant familiarization, walk-through, equipment animations, and safety scenarios. To further leverage the AVESTAR facilities and simulators, NETL and its university partners are pursuing an innovative and collaborative R&D program. In the area of process control, AVESTAR researchers are developing enhanced strategies for regulatory control and coordinated plant-wide control, including gasifier and gas turbine lead, as well as advanced process control using model predictive control (MPC) techniques. Other AVESTAR R&D focus areas include high-fidelity equipment modeling using partial differential equations, dynamic reduced order modeling, optimal sensor placement, 3D virtual plant simulation, and modern grid. NETL and its partners plan to continue building the AVESTAR portfolio of dynamic simulators, immersive training systems, and advanced research capabilities to satisfy industry’s growing need for training and experience with the operation and control of clean energy plants. Future dynamic simulators under development include natural gas combined cycle (NGCC) and supercritical pulverized coal (SCPC) plants with post-combustion CO2 capture. These dynamic simulators are targeted for use in establishing a Virtual Carbon Capture Center (VCCC), similar in concept to the DOE’s National Carbon Capture Center for slipstream testing. The VCCC will enable developers of CO2 capture technologies to integrate, test, and optimize the operation of their dynamic capture models within the context of baseline power plant dynamic models. The objective is to provide hands-on, simulator-based “learn-by-operating” test platforms to accelerate the scale-up and deployment of CO2 capture technologies. Future AVESTAR plans also include pursuing R&D on the dynamics, operation, and control of integrated electricity generation and storage systems for the modern grid era. Special emphasis will be given to combining load-following energy plants with renewable and distributed generating supplies and fast-ramping energy storage systems to provide near constant baseload power.« less

Postgraduate education for Chinese medicine practitioners: a Hong Kong perspective

PubMed Central

Chung, Vincent CH; Law, Michelle PM; Wong, Samuel YS; Mercer, Stewart W; Griffiths, Sian M

2009-01-01

Background Despite Hong Kong government's official commitment to the development of traditional Chinese medicine (TCM) over the last ten years, there appears to have been limited progress in public sector initiated career development and postgraduate training (PGT) for public university trained TCM practitioners. Instead, the private TCM sector is expected to play a major role in nurturing the next generation of TCM practitioners. In the present study we evaluated TCM graduates' perspectives on their career prospects and their views regarding PGT. Method Three focus group discussions with 19 local TCM graduates who had worked full time in a clinical setting for fewer than 5 years. Results Graduates were generally uncertain about how to develop their career pathways in Hong Kong with few postgraduate development opportunities; because of this some were planning to leave the profession altogether. Despite their expressed needs, they were dissatisfied with the current quality of local PGT and suggested various ways for improvement including supervised practice-based learning, competency-based training, and accreditation of training with trainee involvement in design and evaluation. In addition they identified educational needs beyond TCM, in particular a better understanding of western medicine and team working so that primary care provision might be more integrated in the future. Conclusion TCM graduates in Hong Kong feel let down by the lack of public PGT opportunities which is hindering career development. To develop a new generation of TCM practitioners with the capacity to provide quality and comprehensive care, a stronger role for the government, including sufficient public funding, in promoting TCM graduates' careers and training development is suggested. Recent British and Australian experiences in prevocational western medicine training reform may serve as a source of references when relevant program for TCM graduates is planned in the future. PMID:19228379

Investment Strategies for Improving Fifth-Generation Fighter Training

DTIC Science & Technology

2011-01-01

The pod is part of the fifth-generation P5 Combat Training System/Tactical Combat Training System designed by Cubic Corporation. (See Shamim , 2007...http://www.rand.org/pubs/monograph_reports/MR1286/ Shamim , Asif, “F-35 Lightning II News: Cubic Lands Contract for F-35 ACMI Training System,” F-16.net

Towards Autonomous Agriculture: Automatic Ground Detection Using Trinocular Stereovision

PubMed Central

Reina, Giulio; Milella, Annalisa

2012-01-01

Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.

Automated Plantation Mapping in Indonesia Using Remote Sensing Data

NASA Astrophysics Data System (ADS)

Karpatne, A.; Jia, X.; Khandelwal, A.; Kumar, V.

2017-12-01

Plantation mapping is critical for understanding and addressing deforestation, a key driver of climate change and ecosystem degradation. Unfortunately, most plantation maps are limited to small areas for specific years because they rely on visual inspection of imagery. In this work, we propose a data-driven approach which automatically generates yearly plantation maps for large regions using MODIS multi-spectral data. While traditional machine learning algorithms face manifold challenges in this task, e.g. imperfect training labels, spatio-temporal data heterogeneity, noisy and high-dimensional data, lack of evaluation data, etc., we introduce a novel deep learning-based framework that combines existing imperfect plantation products as training labels and models the spatio-temporal relationships of land covers. We also explores the post-processing steps based on Hidden Markov Model that further improve the detection accuracy. Then we conduct extensive evaluation of the generated plantation maps. Specifically, by randomly sampling and comparing with high-resolution Digital Globe imagery, we demonstrate that the generated plantation maps achieve both high precision and high recall. When compared with existing plantation mapping products, our detection can avoid both false positives and false negatives. Finally, we utilize the generated plantation maps in analyzing the relationship between forest fires and growth of plantations, which assists in better understanding the cause of deforestation in Indonesia.

Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution

PubMed Central

Espinal, Andres; Rostro-Gonzalez, Horacio; Carpio, Martin; Guerra-Hernandez, Erick I.; Ornelas-Rodriguez, Manuel; Sotelo-Figueroa, Marco

2016-01-01

This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented. PMID:27516737

49 CFR 661.11 - Rolling stock procurements.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Devices; (21) Car Axle Counters; (22) Communication Based Train Control (CBTC). (u) Communication... components of a bus. This list is not all-inclusive. Car body shells, egines, transmissions, front axle... assemblies, air compressor and pneumatic systems, generator/alternator and electrical systems, steering...

49 CFR 661.11 - Rolling stock procurements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Devices; (21) Car Axle Counters; (22) Communication Based Train Control (CBTC). (u) Communication... components of a bus. This list is not all-inclusive. Car body shells, egines, transmissions, front axle... assemblies, air compressor and pneumatic systems, generator/alternator and electrical systems, steering...

Individualized grid-enabled mammographic training system

NASA Astrophysics Data System (ADS)

Yap, M. H.; Gale, A. G.

2009-02-01

The PERFORMS self-assessment scheme measures individuals skills in identifying key mammographic features on sets of known cases. One aspect of this is that it allows radiologists' skills to be trained, based on their data from this scheme. Consequently, a new strategy is introduced to provide revision training based on mammographic features that the radiologist has had difficulty with in these sets. To do this requires a lot of random cases to provide dynamic, unique, and up-to-date training modules for each individual. We propose GIMI (Generic Infrastructure in Medical Informatics) middleware as the solution to harvest cases from distributed grid servers. The GIMI middleware enables existing and legacy data to support healthcare delivery, research, and training. It is technology-agnostic, data-agnostic, and has a security policy. The trainee examines each case, indicating the location of regions of interest, and completes an evaluation form, to determine mammographic feature labelling, diagnosis, and decisions. For feedback, the trainee can choose to have immediate feedback after examining each case or batch feedback after examining a number of cases. All the trainees' result are recorded in a database which also contains their trainee profile. A full report can be prepared for the trainee after they have completed their training. This project demonstrates the practicality of a grid-based individualised training strategy and the efficacy in generating dynamic training modules within the coverage/outreach of the GIMI middleware. The advantages and limitations of the approach are discussed together with future plans.

Robust kernel collaborative representation for face recognition

NASA Astrophysics Data System (ADS)

Huang, Wei; Wang, Xiaohui; Ma, Yanbo; Jiang, Yuzheng; Zhu, Yinghui; Jin, Zhong

2015-05-01

One of the greatest challenges of representation-based face recognition is that the training samples are usually insufficient. In other words, the training set usually does not include enough samples to show varieties of high-dimensional face images caused by illuminations, facial expressions, and postures. When the test sample is significantly different from the training samples of the same subject, the recognition performance will be sharply reduced. We propose a robust kernel collaborative representation based on virtual samples for face recognition. We think that the virtual training set conveys some reasonable and possible variations of the original training samples. Hence, we design a new object function to more closely match the representation coefficients generated from the original and virtual training sets. In order to further improve the robustness, we implement the corresponding representation-based face recognition in kernel space. It is noteworthy that any kind of virtual training samples can be used in our method. We use noised face images to obtain virtual face samples. The noise can be approximately viewed as a reflection of the varieties of illuminations, facial expressions, and postures. Our work is a simple and feasible way to obtain virtual face samples to impose Gaussian noise (and other types of noise) specifically to the original training samples to obtain possible variations of the original samples. Experimental results on the FERET, Georgia Tech, and ORL face databases show that the proposed method is more robust than two state-of-the-art face recognition methods, such as CRC and Kernel CRC.

Effects of Model-Based and Memory-Based Processing on Speed and Accuracy of Grammar String Generation

ERIC Educational Resources Information Center

Domangue, Thomas J.; Mathews, Robert C.; Sun, Ron; Roussel, Lewis G.; Guidry, Claire E.

2004-01-01

Learners are able to use 2 different types of knowledge to perform a skill. One type is a conscious mental model, and the other is based on memories of instances. The authors conducted 3 experiments that manipulated training conditions designed to affect the availability of 1 or both types of knowledge about an artificial grammar. Participants…

Innovating Training through Immersive Environments: Generation Y, Exploratory Learning, and Serious Games

NASA Technical Reports Server (NTRS)

Gendron, Gerald

2012-01-01

Over the next decade, those entering Service and Joint Staff positions within the military will come from a different generation than the current leadership. They will come from Generation Y and have differing preferences for learning. Immersive learning environments like serious games and virtual world initiatives can complement traditional training methods to provide a better overall training program for staffs. Generation Y members desire learning methods which are relevant and interactive, regardless of whether they are delivered over the internet or in person. This paper focuses on a project undertaken to assess alternative training methods to teach special operations staffs. It provides a summary of the needs analysis used to consider alternatives and to better posture the Department of Defense for future training development.

Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging.

PubMed

Liu, Fang; Jang, Hyungseok; Kijowski, Richard; Bradshaw, Tyler; McMillan, Alan B

2018-02-01

Purpose To develop and evaluate the feasibility of deep learning approaches for magnetic resonance (MR) imaging-based attenuation correction (AC) (termed deep MRAC) in brain positron emission tomography (PET)/MR imaging. Materials and Methods A PET/MR imaging AC pipeline was built by using a deep learning approach to generate pseudo computed tomographic (CT) scans from MR images. A deep convolutional auto-encoder network was trained to identify air, bone, and soft tissue in volumetric head MR images coregistered to CT data for training. A set of 30 retrospective three-dimensional T1-weighted head images was used to train the model, which was then evaluated in 10 patients by comparing the generated pseudo CT scan to an acquired CT scan. A prospective study was carried out for utilizing simultaneous PET/MR imaging for five subjects by using the proposed approach. Analysis of covariance and paired-sample t tests were used for statistical analysis to compare PET reconstruction error with deep MRAC and two existing MR imaging-based AC approaches with CT-based AC. Results Deep MRAC provides an accurate pseudo CT scan with a mean Dice coefficient of 0.971 ± 0.005 for air, 0.936 ± 0.011 for soft tissue, and 0.803 ± 0.021 for bone. Furthermore, deep MRAC provides good PET results, with average errors of less than 1% in most brain regions. Significantly lower PET reconstruction errors were realized with deep MRAC (-0.7% ± 1.1) compared with Dixon-based soft-tissue and air segmentation (-5.8% ± 3.1) and anatomic CT-based template registration (-4.8% ± 2.2). Conclusion The authors developed an automated approach that allows generation of discrete-valued pseudo CT scans (soft tissue, bone, and air) from a single high-spatial-resolution diagnostic-quality three-dimensional MR image and evaluated it in brain PET/MR imaging. This deep learning approach for MR imaging-based AC provided reduced PET reconstruction error relative to a CT-based standard within the brain compared with current MR imaging-based AC approaches. © RSNA, 2017 Online supplemental material is available for this article.

Possibilities of the three-dimensional virtual environment tridimensional Second Life® for training in radiology.

PubMed

Lorenzo Álvarez, R; Pavía Molina, J; Sendra Portero, F

2018-03-20

Three-dimensional virtual environments enable very realistic ludic, social, cultural, and educational activities to be carried out online. Second Life ® is one of the most well-known virtual environments, in which numerous training activities have been developed for healthcare professionals, although none about radiology. The aim of this article is to present the technical resources and educational activities that Second Life ® offers for training in radiology based on our experience since 2011 with diverse training activities for undergraduate and postgraduate students. Second Life ® is useful for carrying out radiology training activities online through remote access in an attractive scenario, especially for current generations of students and residents. More than 800 participants have reported in individual satisfaction surveys that their experiences with this approach have been interesting and useful for their training in radiology. Copyright © 2018 SERAM. Publicado por Elsevier España, S.L.U. All rights reserved.

Anxiety and Threat-Related Attention: Cognitive-Motivational Framework and Treatment.

PubMed

Mogg, Karin; Bradley, Brendan P

2018-03-01

Research in experimental psychopathology and cognitive theories of anxiety highlight threat-related attention biases (ABs) and underpin the development of a computer-delivered treatment for anxiety disorders: attention-bias modification (ABM) training. Variable effects of ABM training on anxiety and ABs generate conflicting research recommendations, novel ABM training procedures, and theoretical controversy. This article summarises an updated cognitive-motivational framework, integrating proposals from cognitive models of anxiety and attention, as well as evidence of ABs. Interactions between motivational salience-driven and goal-directed influences on multiple cognitive processes (e.g., stimulus evaluation, inhibition, switching, orienting) underlie anxiety and the variable manifestations of ABs (orienting towards and away from threat; threat-distractor interference). This theoretical analysis also considers ABM training as cognitive skill training, describes a conceptual framework for evaluating/developing novel ABM training procedures, and complements network-based research on reciprocal anxiety-cognition relationships. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Photovoltaic-Model-Based Solar Irradiance Estimators: Performance Comparison and Application to Maximum Power Forecasting

NASA Astrophysics Data System (ADS)

Scolari, Enrica; Sossan, Fabrizio; Paolone, Mario

2018-01-01

Due to the increasing proportion of distributed photovoltaic (PV) production in the generation mix, the knowledge of the PV generation capacity has become a key factor. In this work, we propose to compute the PV plant maximum power starting from the indirectly-estimated irradiance. Three estimators are compared in terms of i) ability to compute the PV plant maximum power, ii) bandwidth and iii) robustness against measurements noise. The approaches rely on measurements of the DC voltage, current, and cell temperature and on a model of the PV array. We show that the considered methods can accurately reconstruct the PV maximum generation even during curtailment periods, i.e. when the measured PV power is not representative of the maximum potential of the PV array. Performance evaluation is carried out by using a dedicated experimental setup on a 14.3 kWp rooftop PV installation. Results also proved that the analyzed methods can outperform pyranometer-based estimations, with a less complex sensing system. We show how the obtained PV maximum power values can be applied to train time series-based solar maximum power forecasting techniques. This is beneficial when the measured power values, commonly used as training, are not representative of the maximum PV potential.

Similarity-based multi-model ensemble approach for 1-15-day advance prediction of monsoon rainfall over India

NASA Astrophysics Data System (ADS)

Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati

2018-04-01

The southwest (SW) monsoon season (June, July, August and September) is the major period of rainfall over the Indian region. The present study focuses on the development of a new multi-model ensemble approach based on the similarity criterion (SMME) for the prediction of SW monsoon rainfall in the extended range. This approach is based on the assumption that training with the similar type of conditions may provide the better forecasts in spite of the sequential training which is being used in the conventional MME approaches. In this approach, the training dataset has been selected by matching the present day condition to the archived dataset and days with the most similar conditions were identified and used for training the model. The coefficients thus generated were used for the rainfall prediction. The precipitation forecasts from four general circulation models (GCMs), viz. European Centre for Medium-Range Weather Forecasts (ECMWF), United Kingdom Meteorological Office (UKMO), National Centre for Environment Prediction (NCEP) and China Meteorological Administration (CMA) have been used for developing the SMME forecasts. The forecasts of 1-5, 6-10 and 11-15 days were generated using the newly developed approach for each pentad of June-September during the years 2008-2013 and the skill of the model was analysed using verification scores, viz. equitable skill score (ETS), mean absolute error (MAE), Pearson's correlation coefficient and Nash-Sutcliffe model efficiency index. Statistical analysis of SMME forecasts shows superior forecast skill compared to the conventional MME and the individual models for all the pentads, viz. 1-5, 6-10 and 11-15 days.

The Shuttle Mission Simulator computer generated imagery

NASA Technical Reports Server (NTRS)

Henderson, T. H.

1984-01-01

Equipment available in the primary training facility for the Space Transportation System (STS) flight crews includes the Fixed Base Simulator, the Motion Base Simulator, the Spacelab Simulator, and the Guidance and Navigation Simulator. The Shuttle Mission Simulator (SMS) consists of the Fixed Base Simulator and the Motion Base Simulator. The SMS utilizes four visual Computer Generated Image (CGI) systems. The Motion Base Simulator has a forward crew station with six-degrees of freedom motion simulation. Operation of the Spacelab Simulator is planned for the spring of 1983. The Guidance and Navigation Simulator went into operation in 1982. Aspects of orbital visual simulation are discussed, taking into account the earth scene, payload simulation, the generation and display of 1079 stars, the simulation of sun glare, and Reaction Control System jet firing plumes. Attention is also given to landing site visual simulation, and night launch and landing simulation.

Method of converting an existing vehicle powertrain to a hybrid powertrain system

DOEpatents

Reed, Jr., Richard G.; Boberg, Evan S.; Lawrie, Robert E.; Castaing, Francois J.

2001-12-25

A method of converting an existing vehicle powertrain including a manual transmission to a hybrid powertrain system with an automated powertrain transmission. The first step in the method of attaching a gear train housing to a housing of said manual transmission, said gear train housing receiving as end of drive shaft of said transmission and rotatably supporting a gear train assembly. Secondly, mounting an electric motor/generator to said gear train housing and attaching a motor/generator drive shaft of said electric motor/generator to said gear train assembly. Lastly, connecting an electro-mechanical clutch actuator to a friction clutch mechanism of said manual transmission.

Overload training inhibits phagocytosis and ROS generation of peritoneal macrophages: role of IGF-1 and MGF.

PubMed

Xiao, Weihua; Chen, Peijie; Wang, Ru; Dong, Jingmei

2013-01-01

We tested the hypothesis that overload training inhibits the phagocytosis and the reactive oxygen species (ROS) generation of peritoneal macrophages (Mϕs), and that insulin-like growth factor-1(IGF-1) and mechano-growth factor (MGF) produced by macrophages may contribute to this process. Rats were randomized to two groups, sedentary control group (n = 10) and overload training group (n = 10). The rats of overload training group were subjected to 11 weeks of experimental training protocol. Blood sample was used to determine the content of hemoglobin, testosterone, and corticosterone. The phagocytosis and the ROS generation of Mϕs were measured by the uptake of neutral red and the flow cytometry, respectively. IGF-1 and MGF mRNA levels in Mϕs were determined by real-time PCR. In addition, we evaluated the effects of IGF-1 and MGF peptide on phagocytosis and ROS generation of Mϕs in vitro. The data showed that overload training significantly decreased the body weight (19.3 %, P < 0.01), the hemoglobin (13.5 %, P < 0.01), the testosterone (55.3 %, P < 0.01) and the corticosterone (40.6 %, P < 0.01) in blood. Moreover, overload training significantly decreased the phagocytosis (27 %, P < 0.05) and the ROS generation (35 %, P < 0.01) of Mϕs. IGF-1 and MGF mRNA levels in Mϕs from overload training group increased significantly compared with the control group (21-fold and 92-fold, respectively; P < 0.01). In vitro experiments showed that IGF-1 had no significant effect on the phagocytosis and the ROS generation of Mϕs. Unlike IGF-1, MGF peptide impaired the phagocytosis of Mϕs in dose-independent manner. In addition, MGF peptide of some concentrations (i.e., 1, 10, 50, 100 ng/ml) significantly inhibited the ROS generation of Mϕs. These results suggest that overload training inhibits the phagocytosis and the ROS generation of peritoneal macrophages, and that MGF produced by macrophages may play a key role in this process. This may represent a novel mechanism of immune suppression induced by overload training.

Generative adversarial networks for brain lesion detection

NASA Astrophysics Data System (ADS)

Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

2017-02-01

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Observation of coherently enhanced tunable narrow-band terahertz transition radiation from a relativistic sub-picosecond electron bunch train

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

Piot, P.; Maxwell, T. J.; Accelerator Physics Center, Fermi National Accelerator Laboratory, Batavia, Illinois 60510

2011-06-27

We experimentally demonstrate the production of narrow-band ({delta}f/f{approx_equal}20% at f{approx_equal}0.5THz) transition radiation with tunable frequency over [0.37, 0.86] THz. The radiation is produced as a train of sub-picosecond relativistic electron bunches transits at the vacuum-aluminum interface of an aluminum converter screen. The bunch train is generated via a transverse-to-longitudinal phase space exchange technique. We also show a possible application of modulated beams to extend the dynamical range of a popular bunch length diagnostic technique based on the spectral analysis of coherent radiation.

Training in the prevention of cervical cancer: advantages of e-learning

PubMed Central

Company, Assumpta; Montserrat, Mireia; Bosch, Francesc X; de Sanjosé, Silvia

2015-01-01

Cervical cancer remains the second most common cancer for women worldwide and is the cancer priority in most low- and middle-income countries (LMIC). The development of vaccines against the human papilloma virus (HPV) and the impact of technology both for the detection of HPV and cervical cancer represent milestones and new opportunities in prevention. New internet-based technologies are generating mass access to training programmes. This article presents the methodology for developing an online training programme for the prevention of cervical cancer as well as the results obtained during the four year period wherein the same programme was delivered in Latin America. PMID:26557878

Training in the prevention of cervical cancer: advantages of e-learning.

PubMed

Company, Assumpta; Montserrat, Mireia; Bosch, Francesc X; de Sanjosé, Silvia

2015-01-01

Cervical cancer remains the second most common cancer for women worldwide and is the cancer priority in most low- and middle-income countries (LMIC). The development of vaccines against the human papilloma virus (HPV) and the impact of technology both for the detection of HPV and cervical cancer represent milestones and new opportunities in prevention. New internet-based technologies are generating mass access to training programmes. This article presents the methodology for developing an online training programme for the prevention of cervical cancer as well as the results obtained during the four year period wherein the same programme was delivered in Latin America.

Sample Selection for Training Cascade Detectors.

PubMed

Vállez, Noelia; Deniz, Oscar; Bueno, Gloria

2015-01-01

Automatic detection systems usually require large and representative training datasets in order to obtain good detection and false positive rates. Training datasets are such that the positive set has few samples and/or the negative set should represent anything except the object of interest. In this respect, the negative set typically contains orders of magnitude more images than the positive set. However, imbalanced training databases lead to biased classifiers. In this paper, we focus our attention on a negative sample selection method to properly balance the training data for cascade detectors. The method is based on the selection of the most informative false positive samples generated in one stage to feed the next stage. The results show that the proposed cascade detector with sample selection obtains on average better partial AUC and smaller standard deviation than the other compared cascade detectors.

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

Warmack, Robert J. Bruce; Wolf, Dennis A.; Frank, Steven Shane

Various apparatus and methods for smoke detection are disclosed. In one embodiment, a method of training a classifier for a smoke detector comprises inputting sensor data from a plurality of tests into a processor. The sensor data is processed to generate derived signal data corresponding to the test data for respective tests. The derived signal data is assigned into categories comprising at least one fire group and at least one non-fire group. Linear discriminant analysis (LDA) training is performed by the processor. The derived signal data and the assigned categories for the derived signal data are inputs to the LDAmore » training. The output of the LDA training is stored in a computer readable medium, such as in a smoke detector that uses LDA to determine, based on the training, whether present conditions indicate the existence of a fire.« less

Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning.

PubMed

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

2017-01-01

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

Changes in lower limb muscle function and muscle mass following exercise-based interventions in patients with chronic obstructive pulmonary disease: A review of the English-language literature.

PubMed

De Brandt, Jana; Spruit, Martijn A; Hansen, Dominique; Franssen, Frits Me; Derave, Wim; Sillen, Maurice Jh; Burtin, Chris

2018-05-01

Chronic obstructive pulmonary disease (COPD) patients often experience lower limb muscle dysfunction and wasting. Exercise-based training has potential to improve muscle function and mass, but literature on this topic is extensive and heterogeneous including numerous interventions and outcome measures. This review uses a detailed systematic approach to investigate the effect of this wide range of exercise-based interventions on muscle function and mass. PUBMED and PEDro databases were searched. In all, 70 studies ( n = 2504 COPD patients) that implemented an exercise-based intervention and reported muscle strength, endurance, or mass in clinically stable COPD patients were critically appraised. Aerobic and/or resistance training, high-intensity interval training, electrical or magnetic muscle stimulation, whole-body vibration, and water-based training were investigated. Muscle strength increased in 78%, muscle endurance in 92%, and muscle mass in 88% of the cases where that specific outcome was measured. Despite large heterogeneity in exercise-based interventions and outcome measures used, most exercise-based trials showed improvements in muscle strength, endurance, and mass in COPD patients. Which intervention(s) is (are) best for which subgroup of patients remains currently unknown. Furthermore, this literature review identifies gaps in the current knowledge and generates recommendations for future research to enhance our knowledge on exercise-based interventions in COPD patients.

Changes in lower limb muscle function and muscle mass following exercise-based interventions in patients with chronic obstructive pulmonary disease: A review of the English-language literature

PubMed Central

De Brandt, Jana; Spruit, Martijn A; Hansen, Dominique; Franssen, Frits ME; Derave, Wim; Sillen, Maurice JH; Burtin, Chris

2017-01-01

Chronic obstructive pulmonary disease (COPD) patients often experience lower limb muscle dysfunction and wasting. Exercise-based training has potential to improve muscle function and mass, but literature on this topic is extensive and heterogeneous including numerous interventions and outcome measures. This review uses a detailed systematic approach to investigate the effect of this wide range of exercise-based interventions on muscle function and mass. PUBMED and PEDro databases were searched. In all, 70 studies (n = 2504 COPD patients) that implemented an exercise-based intervention and reported muscle strength, endurance, or mass in clinically stable COPD patients were critically appraised. Aerobic and/or resistance training, high-intensity interval training, electrical or magnetic muscle stimulation, whole-body vibration, and water-based training were investigated. Muscle strength increased in 78%, muscle endurance in 92%, and muscle mass in 88% of the cases where that specific outcome was measured. Despite large heterogeneity in exercise-based interventions and outcome measures used, most exercise-based trials showed improvements in muscle strength, endurance, and mass in COPD patients. Which intervention(s) is (are) best for which subgroup of patients remains currently unknown. Furthermore, this literature review identifies gaps in the current knowledge and generates recommendations for future research to enhance our knowledge on exercise-based interventions in COPD patients. PMID:28580854

The Statistical Meaning of Kurtosis and Its New Application to Identification of Persons Based on Seismic Signals

PubMed Central

Liang, Zhiqiang; Wei, Jianming; Zhao, Junyu; Liu, Haitao; Li, Baoqing; Shen, Jie; Zheng, Chunlei

2008-01-01

This paper presents a new algorithm making use of kurtosis, which is a statistical parameter, to distinguish the seismic signal generated by a person's footsteps from other signals. It is adaptive to any environment and needs no machine study or training. As persons or other targets moving on the ground generate continuous signals in the form of seismic waves, we can separate different targets based on the seismic waves they generate. The parameter of kurtosis is sensitive to impulsive signals, so it's much more sensitive to the signal generated by person footsteps than other signals generated by vehicles, winds, noise, etc. The parameter of kurtosis is usually employed in the financial analysis, but rarely used in other fields. In this paper, we make use of kurtosis to distinguish person from other targets based on its different sensitivity to different signals. Simulation and application results show that this algorithm is very effective in distinguishing person from other targets. PMID:27873804

Anomaly detection for machine learning redshifts applied to SDSS galaxies

NASA Astrophysics Data System (ADS)

Hoyle, Ben; Rau, Markus Michael; Paech, Kerstin; Bonnett, Christopher; Seitz, Stella; Weller, Jochen

2015-10-01

We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million `clean' SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 `anomalous' galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed `anomaly-removed' sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

Evaluation of three techniques for classifying urban land cover patterns using LANDSAT MSS data. [New Orleans, Louisiana

NASA Technical Reports Server (NTRS)

Baumann, P. R. (Principal Investigator)

1979-01-01

Three computer quantitative techniques for determining urban land cover patterns are evaluated. The techniques examined deal with the selection of training samples by an automated process, the overlaying of two scenes from different seasons of the year, and the use of individual pixels as training points. Evaluation is based on the number and type of land cover classes generated and the marks obtained from an accuracy test. New Orleans, Louisiana and its environs form the study area.

Millwright Apprenticeship. Related Training Modules. 11.1-11.2 Generators.

ERIC Educational Resources Information Center

Lane Community Coll., Eugene, OR.

This packet, part of the instructional materials for the Oregon apprenticeship program for millwright training, contains two modules covering generators. The modules provide information on the following topics: types and construction of generators and generator operation. Each module consists of a goal, performance indicators, student study guide,…

Can surgical simulation be used to train detection and classification of neural networks?

PubMed

Zisimopoulos, Odysseas; Flouty, Evangello; Stacey, Mark; Muscroft, Sam; Giataganas, Petros; Nehme, Jean; Chow, Andre; Stoyanov, Danail

2017-10-01

Computer-assisted interventions (CAI) aim to increase the effectiveness, precision and repeatability of procedures to improve surgical outcomes. The presence and motion of surgical tools is a key information input for CAI surgical phase recognition algorithms. Vision-based tool detection and recognition approaches are an attractive solution and can be designed to take advantage of the powerful deep learning paradigm that is rapidly advancing image recognition and classification. The challenge for such algorithms is the availability and quality of labelled data used for training. In this Letter, surgical simulation is used to train tool detection and segmentation based on deep convolutional neural networks and generative adversarial networks. The authors experiment with two network architectures for image segmentation in tool classes commonly encountered during cataract surgery. A commercially-available simulator is used to create a simulated cataract dataset for training models prior to performing transfer learning on real surgical data. To the best of authors' knowledge, this is the first attempt to train deep learning models for surgical instrument detection on simulated data while demonstrating promising results to generalise on real data. Results indicate that simulated data does have some potential for training advanced classification methods for CAI systems.

School of pharmacy-based medication therapy management program: development and initial experience.

PubMed

Lam, Annie; Odegard, Peggy Soule; Gardner, Jacqueline

2012-01-01

To describe a school of pharmacy-community pharmacy collaborative model for medication therapy management (MTM) service and training. University of Washington (UW) School of Pharmacy (Seattle), from July to December 2008. MTM services and training. A campus-based MTM pharmacy was established for teaching, practice, and collaboration with community pharmacies to provide comprehensive medication reviews (CMRs) and MTM training. Number of collaborating pharmacies, number of patients contacted, number of CMRs conducted, and estimated cost avoidance (ECA). UW Pharmacy Cares was licensed as a Class A pharmacy (nondispensing) and signed "business associate" agreements with six community pharmacies. During July to December 2008, 10 faculty pharmacists completed training and 5 provided CMR services to 17 patients (5 telephonic and 12 face-to-face interviews). A total of 67 claims (17 CMRs and 50 CMR-generated claims) were submitted for reimbursement of $1,642 ($96.58/CMR case). Total ECA was $54,250, averaging $3,191.19 per patient. Seven student pharmacists gained CMR interview training. Interest in collaboration by community pharmacies was lower than expected; however, the campus-community practice model addressed unmet patient care needs, reduced outstanding MTM CMR case loads, increased ECA, and facilitated faculty development and training of student pharmacists.

Simulation and training of ultrasound supported anaesthesia: a low-cost approach

NASA Astrophysics Data System (ADS)

Schaaf, T.; Lamontain, M.; Hilpert, J.; Schilling, F.; Tolxdorff, T.

2010-03-01

The use of ultrasound imaging technology during techniques of peripheral nerve blockade offers several clinical benefits. Here we report on a new method to educate residents in ultrasound-guided regional anesthesia. The daily challenge for the anesthesiologists is the 3D angle-depending handling of the stimulation needle and the ultrasound probe while watching the 2D ultrasound image on the monitor. Purpose: Our approach describes how a computer-aided simulation and training set for ultrasound-guided regional anesthesia could be built based on wireless low-cost devices and an interactive simulation of a 2D ultrasound image. For training purposes the injection needle and the ultrasound probe are replaced by wireless Bluetooth-connected 3D tracking devices, which are embedded in WII-mote controllers (Nintendo-Brand). In correlation to the tracked 3D positions of the needle and transducer models the visibility and position of the needle should be simulated in the 2D generated ultrasound image. Conclusion: In future, this tracking and visualization software module could be integrated in a more complex training set, where complex injection paths could be trained based on a 3D segmented model and the training results could be part of a curricular e-learning module.

Single particle train ordering in microchannel based on inertial and vortex effects

NASA Astrophysics Data System (ADS)

Fan, Liang-Liang; Yan, Qing; Zhe, Jiang; Zhao, Liang

2018-06-01

A new microfluidic device for microparticle focusing and ordering in a single particle train is reported. The particle focusing and ordering are based on inertial and vortex effects in a microchannel with a series of suddenly contracted and widely expanded structures on one side. In the suddenly contracted regions, particles located near the contracted structures are subjected to a strong wall-effect lift force and momentum-change-induced inertial force due to the highly curved trajectory, migrating to the straight wall. A horizontal vortex is generated downstream of the contracted structure, which prevents the particle from getting close to the wall. In the widely expanded regions, the streamline is curved and no vortex is generated. The shear-gradient lift force and the momentum-change-induced inertial force are dominant for particle lateral migration, driving particles towards the wall of the expanded structures. Eventually, particles are focused and ordered in a single particle train by the combination effects of the inertial forces and the vortex. In comparison with other single-stream particle focusing methods, this device requires no sheath flow, is easy for fabrication and operation, and can work over a wide range of Reynolds numbers from 19.1–142.9. The highly ordered particle chain could be potentially utilized in a variety of lab-chip applications, including micro-flow cytometer, imaging and droplet-based cell entrapment.

A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning.

PubMed

Li, Haiou; Lyu, Qiang; Cheng, Jianlin

2016-12-01

Protein structure prediction is an important problem in computational biology, and is widely applied to various biomedical problems such as protein function study, protein design, and drug design. In this work, we developed a novel deep learning approach based on a deeply stacked denoising autoencoder for protein structure reconstruction. We applied our approach to a template-based protein structure prediction using only the 3D structural coordinates of homologous template proteins as input. The templates were identified for a target protein by a PSI-BLAST search. 3DRobot (a program that automatically generates diverse and well-packed protein structure decoys) was used to generate initial decoy models for the target from the templates. A stacked denoising autoencoder was trained on the decoys to obtain a deep learning model for the target protein. The trained deep model was then used to reconstruct the final structural model for the target sequence. With target proteins that have highly similar template proteins as benchmarks, the GDT-TS score of the predicted structures is greater than 0.7, suggesting that the deep autoencoder is a promising method for protein structure reconstruction.

A training program for nurse scientists to promote intervention translation.

PubMed

Santacroce, Sheila Judge; Leeman, Jennifer; Song, Mi-Kyung

To reduce the burden of chronic illness, prevention and management interventions must be efficacious, adopted and implemented with fidelity, and reach those at greatest risk. Yet, many research-tested interventions are slow to translate into practice. This paper describes how The University of North Carolina at Chapel Hill School of Nursing's NINR-funded institutional pre- and postdoctoral research-training program is addressing the imperative to speed knowledge translation across the research cycle. The training emphasizes six research methods ("catalysts") to speed translation: stakeholder engagement, patient-centered outcomes, intervention optimization and sequential multiple randomized trials (SMART), pragmatic trials, mixed methods approaches, and dissemination and implementation science strategies. Catalysts are integrated into required coursework, biweekly scientific and integrative seminars, and experiential research training. Trainee and program success is evaluated based on benchmarks applicable to all PhD program students, supplemented by indicators specific to the catalysts. Trainees must also demonstrate proficiency in at least two of the six catalysts in their scholarly products. Proficiency is assessed through their works in progress presentations and peer reviews at T32 integrative seminars. While maintaining the emphasis on theory-based interventions, we have integrated six catalysts into our ongoing research training to expedite the dynamic process of intervention development, testing, dissemination and implementation. Through a variety of training activities, our research training focused on theory-based interventions and the six catalysts will generate future nurse scientists who speed translation of theory-based interventions into practice to maximize health outcomes for patients, families, communities and populations affected by chronic illness. Copyright © 2017 Elsevier Inc. All rights reserved.

Broadening the interface bandwidth in simulation based training

NASA Technical Reports Server (NTRS)

Somers, Larry E.

1989-01-01

Currently most computer based simulations rely exclusively on computer generated graphics to create the simulation. When training is involved, the method almost exclusively used to display information to the learner is text displayed on the cathode ray tube. MICROEXPERT Systems is concentrating on broadening the communications bandwidth between the computer and user by employing a novel approach to video image storage combined with sound and voice output. An expert system is used to combine and control the presentation of analog video, sound, and voice output with computer based graphics and text. Researchers are currently involved in the development of several graphics based user interfaces for NASA, the U.S. Army, and the U.S. Navy. Here, the focus is on the human factors considerations, software modules, and hardware components being used to develop these interfaces.

High-resolution broadband terahertz spectroscopy via electronic heterodyne detection of photonically generated terahertz frequency comb.

PubMed

Pavelyev, D G; Skryl, A S; Bakunov, M I

2014-10-01

We report an alternative approach to the terahertz frequency-comb spectroscopy (TFCS) based on nonlinear mixing of a photonically generated terahertz pulse train with a continuous wave signal from an electronic synthesizer. A superlattice is used as a nonlinear mixer. Unlike the standard TFCS technique, this approach does not require a complex double-laser system but retains the advantages of TFCS-high spectral resolution and wide bandwidth.

Co-Labeling for Multi-View Weakly Labeled Learning.

PubMed

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi-view datasets clearly demonstrate that our proposed co-labeling approach achieves state-of-the-art performance for various multi-view weakly labeled learning problems including multi-view SSL, multi-view MIL and multi-view ROD.

MR-based synthetic CT generation using a deep convolutional neural network method.

PubMed

Han, Xiao

2017-04-01

Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images. The proposed method builds upon recent developments of deep learning and convolutional neural networks in the computer vision literature. The proposed DCNN model has 27 convolutional layers interleaved with pooling and unpooling layers and 35 million free parameters, which can be trained to learn a direct end-to-end mapping from MR images to their corresponding CTs. Training such a large model on our limited data is made possible through the principle of transfer learning and by initializing model weights from a pretrained model. Eighteen brain tumor patients with both CT and T1-weighted MR images are used as experimental data and a sixfold cross-validation study is performed. Each sCT generated is compared against the real CT image of the same patient on a voxel-by-voxel basis. Comparison is also made with respect to an atlas-based approach that involves deformable atlas registration and patch-based atlas fusion. The proposed DCNN method produced a mean absolute error (MAE) below 85 HU for 13 of the 18 test subjects. The overall average MAE was 84.8 ± 17.3 HU for all subjects, which was found to be significantly better than the average MAE of 94.5 ± 17.8 HU for the atlas-based method. The DCNN method also provided significantly better accuracy when being evaluated using two other metrics: the mean squared error (188.6 ± 33.7 versus 198.3 ± 33.0) and the Pearson correlation coefficient(0.906 ± 0.03 versus 0.896 ± 0.03). Although training a DCNN model can be slow, training only need be done once. Applying a trained model to generate a complete sCT volume for each new patient MR image only took 9 s, which was much faster than the atlas-based approach. A DCNN model method was developed, and shown to be able to produce highly accurate sCT estimations from conventional, single-sequence MR images in near real time. Quantitative results also showed that the proposed method competed favorably with an atlas-based method, in terms of both accuracy and computation speed at test time. Further validation on dose computation accuracy and on a larger patient cohort is warranted. Extensions of the method are also possible to further improve accuracy or to handle multi-sequence MR images. © 2017 American Association of Physicists in Medicine.

A Comparison of Two Control Display Unit Concepts on Flight Management System Training

NASA Technical Reports Server (NTRS)

Abbott, Terence S.

1997-01-01

One of the biggest challenges for a pilot in the transition to a 'glass' cockpit is understanding the flight management system (FMS). Because of both the complex nature of the FMS and the pilot-FMS interface, a large portion of transition training is devoted to the FMS. The current study examined the impact of the primary pilot-FMS interface, the control display unit (CDU), on FMS training. Based on the hypothesis that the interface design could have a significant impact on training, an FMS simulation with two separate interfaces was developed. One interface was similar to a current-generation design, and the other was a multiwindows CDU based on graphical user interface techniques. For both application and evaluation reasons, constraints were applied to the graphical CDU design to maintain as much similarity as possible with the conventional CDU. This preliminary experiment was conducted to evaluate the interface effects on training. Sixteen pilots with no FMS experience were used in a between-subjects test. A time-compressed, airline-type FMS training environment was simulated. The subjects were trained to a fixed-time criterion, and performance was measured in a final, full-mission simulation context. This paper describes the technical approach, simulation implementation, and experimental results of this effort.

Training needs for toxicity testing in the 21st century: a survey-informed analysis.

PubMed

Lapenna, Silvia; Gabbert, Silke; Worth, Andrew

2012-12-01

Current training needs on the use of alternative methods in predictive toxicology, including new approaches based on mode-of-action (MoA) and adverse outcome pathway (AOP) concepts, are expected to evolve rapidly. In order to gain insight into stakeholder preferences for training, the European Commission's Joint Research Centre (JRC) conducted a single-question survey with twelve experts in regulatory agencies, industry, national research organisations, NGOs and consultancies. Stakeholder responses were evaluated by means of theory-based qualitative data analysis. Overall, a set of training topics were identified that relate both to general background information and to guidance for applying alternative testing methods. In particular, for the use of in silico methods, stakeholders emphasised the need for training on data integration and evaluation, in order to increase confidence in applying these methods for regulatory purposes. Although the survey does not claim to offer an exhaustive overview of the training requirements, its findings support the conclusion that the development of well-targeted and tailor-made training opportunities that inform about the usefulness of alternative methods, in particular those that offer practical experience in the application of in silico methods, deserves more attention. This should be complemented by transparent information and guidance on the interpretation of the results generated by these methods and software tools. 2012 FRAME.

Automation, robotics, and inflight training for manned Mars missions

NASA Technical Reports Server (NTRS)

Holt, Alan C.

1986-01-01

The automation, robotics, and inflight training requirements of manned Mars missions will be supported by similar capabilities developed for the space station program. Evolutionary space station onboard training facilities will allow the crewmembers to minimize the amount of training received on the ground by providing extensive onboard access to system and experiment malfunction procedures, maintenance procedures, repair procedures, and associated video sequences. Considerable on-the-job training will also be conducted for space station management, mobile remote manipulator operations, proximity operations with the Orbital Maneuvering Vehicle (and later the Orbit Transfer Vehicle), and telerobotics and mobile robots. A similar approach could be used for manned Mars mission training with significant additions such as high fidelity image generation and simulation systems such as holographic projection systems for Mars landing, ascent, and rendezvous training. In addition, a substantial increase in the use of automation and robotics for hazardous and tedious tasks would be expected for Mars mission. Mobile robots may be used to assist in the assembly, test and checkout of the Mars spacecraft, in the handling of nuclear components and hazardous chemical propellent transfer operations, in major spacecraft repair tasks which might be needed (repair of a micrometeroid penetration, for example), in the construction of a Mars base, and for routine maintenance of the base when unmanned.

Surgical motion characterization in simulated needle insertion procedures

NASA Astrophysics Data System (ADS)

Holden, Matthew S.; Ungi, Tamas; Sargent, Derek; McGraw, Robert C.; Fichtinger, Gabor

2012-02-01

PURPOSE: Evaluation of surgical performance in image-guided needle insertions is of emerging interest, to both promote patient safety and improve the efficiency and effectiveness of training. The purpose of this study was to determine if a Markov model-based algorithm can more accurately segment a needle-based surgical procedure into its five constituent tasks than a simple threshold-based algorithm. METHODS: Simulated needle trajectories were generated with known ground truth segmentation by a synthetic procedural data generator, with random noise added to each degree of freedom of motion. The respective learning algorithms were trained, and then tested on different procedures to determine task segmentation accuracy. In the threshold-based algorithm, a change in tasks was detected when the needle crossed a position/velocity threshold. In the Markov model-based algorithm, task segmentation was performed by identifying the sequence of Markov models most likely to have produced the series of observations. RESULTS: For amplitudes of translational noise greater than 0.01mm, the Markov model-based algorithm was significantly more accurate in task segmentation than the threshold-based algorithm (82.3% vs. 49.9%, p<0.001 for amplitude 10.0mm). For amplitudes less than 0.01mm, the two algorithms produced insignificantly different results. CONCLUSION: Task segmentation of simulated needle insertion procedures was improved by using a Markov model-based algorithm as opposed to a threshold-based algorithm for procedures involving translational noise.

Quantitative analysis of single- vs. multiple-set programs in resistance training.

PubMed

Wolfe, Brian L; LeMura, Linda M; Cole, Phillip J

2004-02-01

The purpose of this study was to examine the existing research on single-set vs. multiple-set resistance training programs. Using the meta-analytic approach, we included studies that met the following criteria in our analysis: (a) at least 6 subjects per group; (b) subject groups consisting of single-set vs. multiple-set resistance training programs; (c) pretest and posttest strength measures; (d) training programs of 6 weeks or more; (e) apparently "healthy" individuals free from orthopedic limitations; and (f) published studies in English-language journals only. Sixteen studies generated 103 effect sizes (ESs) based on a total of 621 subjects, ranging in age from 15-71 years. Across all designs, intervention strategies, and categories, the pretest to posttest ES in muscular strength was (chi = 1.4 +/- 1.4; 95% confidence interval, 0.41-3.8; p < 0.001). The results of 2 x 2 analysis of variance revealed simple main effects for age, training status (trained vs. untrained), and research design (p < 0.001). No significant main effects were found for sex, program duration, and set end point. Significant interactions were found for training status and program duration (6-16 weeks vs. 17-40 weeks) and number of sets performed (single vs. multiple). The data indicated that trained individuals performing multiple sets generated significantly greater increases in strength (p < 0.001). For programs with an extended duration, multiple sets were superior to single sets (p < 0.05). This quantitative review indicates that single-set programs for an initial short training period in untrained individuals result in similar strength gains as multiple-set programs. However, as progression occurs and higher gains are desired, multiple-set programs are more effective.

Improving Perceptual Skills with 3-Dimensional Animations.

ERIC Educational Resources Information Center

Johns, Janet Faye; Brander, Julianne Marie

1998-01-01

Describes three-dimensional computer aided design (CAD) models for every component in a representative mechanical system; the CAD models made it easy to generate 3-D animations that are ideal for teaching perceptual skills in multimedia computer-based technical training. Fifteen illustrations are provided. (AEF)

Altitude control in honeybees: joint vision-based learning and guidance.

PubMed

Portelli, Geoffrey; Serres, Julien R; Ruffier, Franck

2017-08-23

Studies on insects' visual guidance systems have shed little light on how learning contributes to insects' altitude control system. In this study, honeybees were trained to fly along a double-roofed tunnel after entering it near either the ceiling or the floor of the tunnel. The honeybees trained to hug the ceiling therefore encountered a sudden change in the tunnel configuration midways: i.e. a "dorsal ditch". Thus, the trained honeybees met a sudden increase in the distance to the ceiling, corresponding to a sudden strong change in the visual cues available in their dorsal field of view. Honeybees reacted by rising quickly and hugging the new, higher ceiling, keeping a similar forward speed, distance to the ceiling and dorsal optic flow to those observed during the training step; whereas bees trained to follow the floor kept on following the floor regardless of the change in the ceiling height. When trained honeybees entered the tunnel via the other entry (the lower or upper entry) to that used during the training step, they quickly changed their altitude and hugged the surface they had previously learned to follow. These findings clearly show that trained honeybees control their altitude based on visual cues memorized during training. The memorized visual cues generated by the surfaces followed form a complex optic flow pattern: trained honeybees may attempt to match the visual cues they perceive with this memorized optic flow pattern by controlling their altitude.

Cognitive/emotional models for human behavior representation in 3D avatar simulations

NASA Astrophysics Data System (ADS)

Peterson, James K.

2004-08-01

Simplified models of human cognition and emotional response are presented which are based on models of auditory/ visual polymodal fusion. At the core of these models is a computational model of Area 37 of the temporal cortex which is based on new isocortex models presented recently by Grossberg. These models are trained using carefully chosen auditory (musical sequences), visual (paintings) and higher level abstract (meta level) data obtained from studies of how optimization strategies are chosen in response to outside managerial inputs. The software modules developed are then used as inputs to character generation codes in standard 3D virtual world simulations. The auditory and visual training data also enable the development of simple music and painting composition generators which significantly enhance one's ability to validate the cognitive model. The cognitive models are handled as interacting software agents implemented as CORBA objects to allow the use of multiple language coding choices (C++, Java, Python etc) and efficient use of legacy code.

Next generation industrial biotechnology based on extremophilic bacteria.

PubMed

Chen, Guo-Qiang; Jiang, Xiao-Ran

2018-04-01

Industrial biotechnology aims to produce bulk chemicals including polymeric materials and biofuels based on bioprocessing sustainable agriculture products such as starch, fatty acids and/or cellulose. However, traditional bioprocesses require bioreactors made of stainless steel, complicated sterilization, difficult and expensive separation procedures as well as well-trained engineers that are able to conduct bioprocessing under sterile conditions, reducing the competitiveness of the bio-products. Amid the continuous low petroleum price, next generation industrial biotechnology (NGIB) allows bioprocessing to be conducted under unsterile (open) conditions using ceramic, cement or plastic bioreactors in a continuous way, it should be an energy, water and substrate saving technology with convenient operation procedure. NGIB also requires less capital investment and reduces demand on highly trained engineers. The foundation for the simplified NGIB is microorganisms that resist contaminations by other microbes, one of the examples is rapid growing halophilic bacteria inoculated under high salt concentration and alkali pH. They have been engineered to produce multiple products in various scales. Copyright © 2017 Elsevier Ltd. All rights reserved.

Trains of electron micro-bunches in plasma wake-field acceleration

NASA Astrophysics Data System (ADS)

Lécz, Zsolt; Andreev, Alexander; Konoplev, Ivan; Seryi, Andrei; Smith, Jonathan

2018-07-01

Plasma-based charged particle accelerators have been intensively investigated in the past three decades due to their capability to open up new horizons in accelerator science and particle physics yielding electric field accelerating gradient more than three orders of magnitudes higher than in conventional devices. At the current stage the most advanced and reliable mechanism for accelerating electrons is based on the propagation of an intense laser pulse or a relativistic electron beam in a low density gaseous target. In this paper we concentrate on the electron beam-driven plasma wake-field acceleration and demonstrate using 3D PiC simulations that a train of electron micro-bunches with ∼10 fs period can be generated behind the driving beam propagating in a density down-ramp. We will discuss the conditions and properties of the micro-bunches generated aiming at understanding and study of multi-bunch mechanism of injection. It is show that the periodicity and duration of micro-bunches can be controlled by adjusting the plasma density gradient and driving beam charge.

Generating region proposals for histopathological whole slide image retrieval.

PubMed

Ma, Yibing; Jiang, Zhiguo; Zhang, Haopeng; Xie, Fengying; Zheng, Yushan; Shi, Huaqiang; Zhao, Yu; Shi, Jun

2018-06-01

Content-based image retrieval is an effective method for histopathological image analysis. However, given a database of huge whole slide images (WSIs), acquiring appropriate region-of-interests (ROIs) for training is significant and difficult. Moreover, histopathological images can only be annotated by pathologists, resulting in the lack of labeling information. Therefore, it is an important and challenging task to generate ROIs from WSI and retrieve image with few labels. This paper presents a novel unsupervised region proposing method for histopathological WSI based on Selective Search. Specifically, the WSI is over-segmented into regions which are hierarchically merged until the WSI becomes a single region. Nucleus-oriented similarity measures for region mergence and Nucleus-Cytoplasm color space for histopathological image are specially defined to generate accurate region proposals. Additionally, we propose a new semi-supervised hashing method for image retrieval. The semantic features of images are extracted with Latent Dirichlet Allocation and transformed into binary hashing codes with Supervised Hashing. The methods are tested on a large-scale multi-class database of breast histopathological WSIs. The results demonstrate that for one WSI, our region proposing method can generate 7.3 thousand contoured regions which fit well with 95.8% of the ROIs annotated by pathologists. The proposed hashing method can retrieve a query image among 136 thousand images in 0.29 s and reach precision of 91% with only 10% of images labeled. The unsupervised region proposing method can generate regions as predictions of lesions in histopathological WSI. The region proposals can also serve as the training samples to train machine-learning models for image retrieval. The proposed hashing method can achieve fast and precise image retrieval with small amount of labels. Furthermore, the proposed methods can be potentially applied in online computer-aided-diagnosis systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Developing an Intelligent Computer-Aided Trainer

NASA Technical Reports Server (NTRS)

Hua, Grace

1990-01-01

The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system was developed as a prototype for intelligent tutoring systems with the intention of seeing PD/ICAT evolve and produce a general ICAT architecture and development environment that can be adapted by a wide variety of training tasks. The proposed architecture is composed of a user interface, a domain expert, a training session manager, a trainee model and a training scenario generator. The PD/ICAT prototype was developed in the LISP environment. Although it has been well received by its peers and users, it could not be delivered toe its end users for practical use because of specific hardware and software constraints. To facilitate delivery of PD/ICAT to its users and to prepare for a more widely accepted development and delivery environment for future ICAT applications, we have ported this training system to a UNIX workstation and adopted use of a conventional language, C, and a C-based rule-based language, CLIPS. A rapid conversion of the PD/ICAT expert system to CLIPS was possible because the knowledge was basically represented as a forward chaining rule base. The resulting CLIPS rule base has been tested successfully in other ICATs as well. Therefore, the porting effort has proven to be a positive step toward our ultimate goal of building a general purpose ICAT development environment.

Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

PubMed

Gao, Hao; Zhang, Yawei; Ren, Lei; Yin, Fang-Fang

2018-01-01

This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state-of-art method, that is, PICCS, using both simulation and experimental data with the on-board cone-beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components. © 2017 American Association of Physicists in Medicine.

Implementing scientific evidence to improve the quality of Child Protection

PubMed Central

Cowley, Laura; Tempest, Vanessa; Maguire, Sabine; Mann, Mala; Naughton, Aideen; Wain, Laura; Kemp, Alison

2013-01-01

In contrast to other areas of medical practice, there was a lack of a clear, concise and accessible synthesis of scientific literature to aid the recognition and investigation of suspected child abuse, and no national training program or evidence based guidelines for clinicians. The project's aim was to identify the current scientific evidence for the recognition and investigation of suspected child abuse and neglect and to disseminate and introduce this into clinical practice. Since 2003 a comprehensive program of Systematic Reviews of all aspects of physical abuse, emotional abuse, and neglect of children, has been developed. Based on NHS Centre for Reviews and Dissemination standards, methodology was devised and reviewers trained. Dissemination was via peer reviewed publications, a series of leaflets highlighting key points in a Question and Answer format, and a website. To date, 21 systematic reviews have been completed, generating 28 peer reviewed publications, and six leaflets around each theme (eg fractures, bruising). More than 250,000 have been distributed to date. Our website generates more than 10,000 hits monthly. It hosts primary reviews that are updated annually, links to all included studies, publications, and detailed methodology. The reviews have directly informed five national clinical guidelines, and the first evidence based training in Child Maltreatment. Child abuse is every health practitioner's responsibility, and it is vital that the decisions made are evidence based, as it is expected in all other fields of medicine. Although challenging, this project demonstrates that it is possible to conduct high quality systematic reviews in this field. For the first time a clear concise synthesis of up to date scientific evidence is available to all practitioners in a range of accessible formats. This has underpinned high quality national guidance and training programs. It ensures all professionals have the appropriate knowledge base in this difficult and challenging field. PMID:26734183

Treatment of category generation and retrieval in aphasia: Effect of typicality of category items.

PubMed Central

Kiran, Swathi; Sandberg, Chaleece; Sebastian, Rajani

2011-01-01

Purpose: Kiran and colleagues (Kiran, 2007, 2008; Kiran & Johnson, 2008; Kiran & Thompson, 2003) have previously suggested that training atypical examples within a semantic category is a more efficient treatment approach to facilitating generalization within the category than training typical examples. The present study extended our previous work examining the notion of semantic complexity within goal-derived (ad-hoc) categories in individuals with aphasia. Methods: Six individuals with fluent aphasia (range = 39-84 years) and varying degrees of naming deficits and semantic impairments were involved. Thirty typical and atypical items each from two categories were selected after an extensive stimulus norming task. Generative naming for the two categories was tested during baseline and treatment. Results: As predicted, training atypical examples in the category resulted in generalization to untrained typical examples in five out the five patient-treatment conditions. In contrast, training typical examples (which was in examined three conditions) produced mixed results. One patient showed generalization to untrained atypical examples, whereas two patients did not show generalization to untrained atypical examples. Conclusions: Results of the present study supplement our existing data on the effect of a semantically based treatment for lexical retrieval by manipulating the typicality of category exemplars. PMID:21173393

Experimental Investigation of Aerodynamic Noise Generated by a Train-Car Gap

NASA Astrophysics Data System (ADS)

Mizushima, Fumio; Takakura, Hiroyuki; Kurita, Takeshi; Kato, Chisachi; Iida, Akiyoshi

To investigate the mechanism of noise generation by a train-car gap, which is one of a major source of noise in Shinkansen trains, experiments were carried out in a wind tunnel using a 1/5-scale model train. We measured velocity profiles of the boundary layer that approaches the gap and confirmed that the boundary layer is turbulent. We also measured the power spectrum of noise and surface pressure fluctuations around the train-car gap. Peak noise and broadband noise were observed. It is found that strong peak noise is generated when the vortex shedding frequency corresponds to the acoustic resonance frequency determined by the geometrical shape of the gap, and that broadband noise is generated at the downstream edge of the gap where vortexes collide. It is estimated that the convection velocity of the vortices in the gap is approximately 45% of the uniform flow velocity.

MOSAIK: a hash-based algorithm for accurate next-generation sequencing short-read mapping.

PubMed

Lee, Wan-Ping; Stromberg, Michael P; Ward, Alistair; Stewart, Chip; Garrison, Erik P; Marth, Gabor T

2014-01-01

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery. All variant discovery benefits from an accurate description of the read placement confidence. To this end, MOSAIK uses a neural-network based training scheme to provide well-calibrated mapping quality scores, demonstrated by a correlation coefficient between MOSAIK assigned and actual mapping qualities greater than 0.98. In order to ensure that studies of any genome are supported, a training pipeline is provided to ensure optimal mapping quality scores for the genome under investigation. MOSAIK is multi-threaded, open source, and incorporated into our command and pipeline launcher system GKNO (http://gkno.me).

MOSAIK: A Hash-Based Algorithm for Accurate Next-Generation Sequencing Short-Read Mapping

PubMed Central

Lee, Wan-Ping; Stromberg, Michael P.; Ward, Alistair; Stewart, Chip; Garrison, Erik P.; Marth, Gabor T.

2014-01-01

MOSAIK is a stable, sensitive and open-source program for mapping second and third-generation sequencing reads to a reference genome. Uniquely among current mapping tools, MOSAIK can align reads generated by all the major sequencing technologies, including Illumina, Applied Biosystems SOLiD, Roche 454, Ion Torrent and Pacific BioSciences SMRT. Indeed, MOSAIK was the only aligner to provide consistent mappings for all the generated data (sequencing technologies, low-coverage and exome) in the 1000 Genomes Project. To provide highly accurate alignments, MOSAIK employs a hash clustering strategy coupled with the Smith-Waterman algorithm. This method is well-suited to capture mismatches as well as short insertions and deletions. To support the growing interest in larger structural variant (SV) discovery, MOSAIK provides explicit support for handling known-sequence SVs, e.g. mobile element insertions (MEIs) as well as generating outputs tailored to aid in SV discovery. All variant discovery benefits from an accurate description of the read placement confidence. To this end, MOSAIK uses a neural-network based training scheme to provide well-calibrated mapping quality scores, demonstrated by a correlation coefficient between MOSAIK assigned and actual mapping qualities greater than 0.98. In order to ensure that studies of any genome are supported, a training pipeline is provided to ensure optimal mapping quality scores for the genome under investigation. MOSAIK is multi-threaded, open source, and incorporated into our command and pipeline launcher system GKNO (http://gkno.me). PMID:24599324

System Diagnostic Builder - A rule generation tool for expert systems that do intelligent data evaluation. [applied to Shuttle Mission Simulator

NASA Technical Reports Server (NTRS)

Nieten, Joseph; Burke, Roger

1993-01-01

Consideration is given to the System Diagnostic Builder (SDB), an automated knowledge acquisition tool using state-of-the-art AI technologies. The SDB employs an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert. Thus, data are captured from the subject system, classified, and used to drive the rule generation process. These rule bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The knowledge bases captured from the Shuttle Mission Simulator can be used as black box simulations by the Intelligent Computer Aided Training devices. The SDB can also be used to construct knowledge bases for the process control industry, such as chemical production or oil and gas production.

Integrating Research Into Clinical Internship Training Bridging the Science/Practice Gap in Pediatric Psychology

PubMed Central

Spirito, Anthony

2012-01-01

Existing literature highlights a critical gap between science and practice in clinical psychology. The internship year is a “capstone experience”; training in methods of scientific evaluation should be integrated with the development of advanced clinical competencies. We provide a rationale for continued exposure to research during the clinical internship year, including, (a) critical examination and integration of the literature regarding evidence-based treatment and assessment, (b) participation in faculty-based and independent research, and (c) orientation to the science and strategy of grantsmanship. Participation in research provides exposure to new empirical models and can foster the development of applied research questions. Orientation to grantsmanship can yield an initial sense of the “business of science.” Internship provides an important opportunity to examine the challenges to integrating the clinical evidence base into professional practice; for that reason, providing research exposure on internship is an important strategy in training the next generation of pediatric psychologists. PMID:22286345

Portable Brain-Computer Interface for the Intensive Care Unit Patient Communication Using Subject-Dependent SSVEP Identification.

PubMed

Dehzangi, Omid; Farooq, Muhamed

2018-01-01

A major predicament for Intensive Care Unit (ICU) patients is inconsistent and ineffective communication means. Patients rated most communication sessions as difficult and unsuccessful. This, in turn, can cause distress, unrecognized pain, anxiety, and fear. As such, we designed a portable BCI system for ICU communications (BCI4ICU) optimized to operate effectively in an ICU environment. The system utilizes a wearable EEG cap coupled with an Android app designed on a mobile device that serves as visual stimuli and data processing module. Furthermore, to overcome the challenges that BCI systems face today in real-world scenarios, we propose a novel subject-specific Gaussian Mixture Model- (GMM-) based training and adaptation algorithm. First, we incorporate subject-specific information in the training phase of the SSVEP identification model using GMM-based training and adaptation. We evaluate subject-specific models against other subjects. Subsequently, from the GMM discriminative scores, we generate the transformed vectors, which are passed to our predictive model. Finally, the adapted mixture mean scores of the subject-specific GMMs are utilized to generate the high-dimensional supervectors. Our experimental results demonstrate that the proposed system achieved 98.7% average identification accuracy, which is promising in order to provide effective and consistent communication for patients in the intensive care.

A game-based platform for crowd-sourcing biomedical image diagnosis and standardized remote training and education of diagnosticians

NASA Astrophysics Data System (ADS)

Feng, Steve; Woo, Minjae; Chandramouli, Krithika; Ozcan, Aydogan

2015-03-01

Over the past decade, crowd-sourcing complex image analysis tasks to a human crowd has emerged as an alternative to energy-inefficient and difficult-to-implement computational approaches. Following this trend, we have developed a mathematical framework for statistically combining human crowd-sourcing of biomedical image analysis and diagnosis through games. Using a web-based smart game (BioGames), we demonstrated this platform's effectiveness for telediagnosis of malaria from microscopic images of individual red blood cells (RBCs). After public release in early 2012 (http://biogames.ee.ucla.edu), more than 3000 gamers (experts and non-experts) used this BioGames platform to diagnose over 2800 distinct RBC images, marking them as positive (infected) or negative (non-infected). Furthermore, we asked expert diagnosticians to tag the same set of cells with labels of positive, negative, or questionable (insufficient information for a reliable diagnosis) and statistically combined their decisions to generate a gold standard malaria image library. Our framework utilized minimally trained gamers' diagnoses to generate a set of statistical labels with an accuracy that is within 98% of our gold standard image library, demonstrating the "wisdom of the crowd". Using the same image library, we have recently launched a web-based malaria training and educational game allowing diagnosticians to compare their performance with their peers. After diagnosing a set of ~500 cells per game, diagnosticians can compare their quantified scores against a leaderboard and view their misdiagnosed cells. Using this platform, we aim to expand our gold standard library with new RBC images and provide a quantified digital tool for measuring and improving diagnostician training globally.

Effective Training for Millennial Students

ERIC Educational Resources Information Center

Werth, Eric P.; Werth, Loredana

2011-01-01

A generational shift is occurring in training environments worldwide, a shift that promises to bring with it a dramatic and long-lasting impact. Just as years ago, those of the Baby Boomer generation passed the torch to Generation X, today the process is starting anew with Generation X and those who have come to be known as the Millennials.…

Towards Participation and Equality: The UN's International Labour Organization.

ERIC Educational Resources Information Center

Konig, A.

1990-01-01

The role of the International Labour Organization (ILO) in vocational rehabilitation and employment for people with disabilities is examined. The ILO's recent emphasis on community-based training and employment programs, social reintegration of disabled citizens through self-employment and union-generating activities, and special programs for…

Generating realistic environments for cyber operations development, testing, and training

NASA Astrophysics Data System (ADS)

Berk, Vincent H.; Gregorio-de Souza, Ian; Murphy, John P.

2012-06-01

Training eective cyber operatives requires realistic network environments that incorporate the structural and social complexities representative of the real world. Network trac generators facilitate repeatable experiments for the development, training and testing of cyber operations. However, current network trac generators, ranging from simple load testers to complex frameworks, fail to capture the realism inherent in actual environments. In order to improve the realism of network trac generated by these systems, it is necessary to quantitatively measure the level of realism in generated trac with respect to the environment being mimicked. We categorize realism measures into statistical, content, and behavioral measurements, and propose various metrics that can be applied at each level to indicate how eectively the generated trac mimics the real world.

Less is more: Sampling chemical space with active learning

NASA Astrophysics Data System (ADS)

Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas; Isayev, Olexandr; Roitberg, Adrian E.

2018-06-01

The development of accurate and transferable machine learning (ML) potentials for predicting molecular energetics is a challenging task. The process of data generation to train such ML potentials is a task neither well understood nor researched in detail. In this work, we present a fully automated approach for the generation of datasets with the intent of training universal ML potentials. It is based on the concept of active learning (AL) via Query by Committee (QBC), which uses the disagreement between an ensemble of ML potentials to infer the reliability of the ensemble's prediction. QBC allows the presented AL algorithm to automatically sample regions of chemical space where the ML potential fails to accurately predict the potential energy. AL improves the overall fitness of ANAKIN-ME (ANI) deep learning potentials in rigorous test cases by mitigating human biases in deciding what new training data to use. AL also reduces the training set size to a fraction of the data required when using naive random sampling techniques. To provide validation of our AL approach, we develop the COmprehensive Machine-learning Potential (COMP6) benchmark (publicly available on GitHub) which contains a diverse set of organic molecules. Active learning-based ANI potentials outperform the original random sampled ANI-1 potential with only 10% of the data, while the final active learning-based model vastly outperforms ANI-1 on the COMP6 benchmark after training to only 25% of the data. Finally, we show that our proposed AL technique develops a universal ANI potential (ANI-1x) that provides accurate energy and force predictions on the entire COMP6 benchmark. This universal ML potential achieves a level of accuracy on par with the best ML potentials for single molecules or materials, while remaining applicable to the general class of organic molecules composed of the elements CHNO.

Learning From The Past and Planning For The Future: The Challenges Of And Solutions For Integrating Aging Into Doctoral Psychology Training

PubMed Central

Holtzer, Roee; Zweig, Richard A.; Siegel, Lawrence J.

2013-01-01

The long forecast “elder boom” has begun. Beginning in 2011, ten thousand members of the “baby boom” generation began turning 65 each day. This demographic shift in our society mandates that pre-doctoral programs in clinical psychology incorporate aging as an integral component of their core and elective training. While fully supporting the concept of broad and general training for predoctoral professional psychology programs, we maintain that the infusion of aging into doctoral psychology training curricula has been inadequate. In this manuscript we provide an overview of geropsychology training models and discuss the challenges involved in incorporating aging to the curriculum of pre-doctoral training in clinical psychology. Potential solutions and examples for accelerating infusion of aging knowledge base are discussed in the context of different geropsychology training models. We conclude that providing services to this rapidly growing segment of our population presents both an employment opportunity to broaden the reach of our profession as well as an ethical responsibility to train future professionals who will practice within their area of knowledge and expertise. PMID:23483705

Documentary bioethics: visual narratives for Generations X and Y.

PubMed

Stys, John C

2006-01-01

Narrative bioethics is primarily understood to involve storytelling through the use of literature. This article suggests that other forms of media are necessary to convey stories of an ethical nature to an audience broader than one being trained as medical professionals. "Documentary bioethics" is a manner to present and interpret stories of an ethical nature using forms of popular electronic media in a reality-based documentary style to society at large, specifically Generations X and Y.

Reinforced Adversarial Neural Computer for de Novo Molecular Design.

PubMed

Putin, Evgeny; Asadulaev, Arip; Ivanenkov, Yan; Aladinskiy, Vladimir; Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán; Zhavoronkov, Alex

2018-06-12

In silico modeling is a crucial milestone in modern drug design and development. Although computer-aided approaches in this field are well-studied, the application of deep learning methods in this research area is at the beginning. In this work, we present an original deep neural network (DNN) architecture named RANC (Reinforced Adversarial Neural Computer) for the de novo design of novel small-molecule organic structures based on the generative adversarial network (GAN) paradigm and reinforcement learning (RL). As a generator RANC uses a differentiable neural computer (DNC), a category of neural networks, with increased generation capabilities due to the addition of an explicit memory bank, which can mitigate common problems found in adversarial settings. The comparative results have shown that RANC trained on the SMILES string representation of the molecules outperforms its first DNN-based counterpart ORGANIC by several metrics relevant to drug discovery: the number of unique structures, passing medicinal chemistry filters (MCFs), Muegge criteria, and high QED scores. RANC is able to generate structures that match the distributions of the key chemical features/descriptors (e.g., MW, logP, TPSA) and lengths of the SMILES strings in the training data set. Therefore, RANC can be reasonably regarded as a promising starting point to develop novel molecules with activity against different biological targets or pathways. In addition, this approach allows scientists to save time and covers a broad chemical space populated with novel and diverse compounds.

The design and implementation of an automated system for logging clinical experiences using an anesthesia information management system.

PubMed

Simpao, Allan; Heitz, James W; McNulty, Stephen E; Chekemian, Beth; Brenn, B Randall; Epstein, Richard H

2011-02-01

Residents in anesthesia training programs throughout the world are required to document their clinical cases to help ensure that they receive adequate training. Current systems involve self-reporting, are subject to delayed updates and misreported data, and do not provide a practicable method of validation. Anesthesia information management systems (AIMS) are being used increasingly in training programs and are a logical source for verifiable documentation. We hypothesized that case logs generated automatically from an AIMS would be sufficiently accurate to replace the current manual process. We based our analysis on the data reporting requirements of the American College of Graduate Medical Education (ACGME). We conducted a systematic review of ACGME requirements and our AIMS record, and made modifications after identifying data element and attribution issues. We studied 2 methods (parsing of free text procedure descriptions and CPT4 procedure code mapping) to automatically determine ACGME case categories and generated AIMS-based case logs and compared these to assignments made by manual inspection of the anesthesia records. We also assessed under- and overreporting of cases entered manually by our residents into the ACGME website. The parsing and mapping methods assigned cases to a majority of the ACGME categories with accuracies of 95% and 97%, respectively, as compared with determinations made by 2 residents and 1 attending who manually reviewed all procedure descriptions. Comparison of AIMS-based case logs with reports from the ACGME Resident Case Log System website showed that >50% of residents either underreported or overreported their total case counts by at least 5%. The AIMS database is a source of contemporaneous documentation of resident experience that can be queried to generate valid, verifiable case logs. The extent of AIMS adoption by academic anesthesia departments should encourage accreditation organizations to support uploading of AIMS-based case log files to improve accuracy and to decrease the clerical burden on anesthesia residents.

Neural Network and Regression Approximations in High Speed Civil Transport Aircraft Design Optimization

NASA Technical Reports Server (NTRS)

Patniak, Surya N.; Guptill, James D.; Hopkins, Dale A.; Lavelle, Thomas M.

1998-01-01

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, can make the process computational intensive. The computational burden can be greatly reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high speed civil transport aircraft is the subject of this paper. The Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and a regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the Lewis Research Center's CometBoards test bed to provide the optimization capability. With the combined software, both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. On the other hand, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the input-output pairs and to train the approximating analyzers was seven times that required for solution of the problem.

Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach.

PubMed

Blanc-Durand, Paul; Van Der Gucht, Axel; Guedj, Eric; Abulizi, Mukedaisi; Aoun-Sebaiti, Mehdi; Lerman, Lionel; Verger, Antoine; Authier, François-Jérôme; Itti, Emmanuel

2017-01-01

Macrophagic myofasciitis (MMF) is an emerging condition with highly specific myopathological alterations. A peculiar spatial pattern of a cerebral glucose hypometabolism involving occipito-temporal cortex and cerebellum have been reported in patients with MMF; however, the full pattern is not systematically present in routine interpretation of scans, and with varying degrees of severity depending on the cognitive profile of patients. Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF 18F-FDG brain profiles. 18F-FDG PET brain images of 119 patients with MMF and 64 healthy subjects were retrospectively analyzed. The whole-population was divided into two groups; a training set (100 MMF, 44 healthy subjects) and a testing set (19 MMF, 20 healthy subjects). Dimensionality reduction was performed using a t-map from statistical parametric mapping (SPM) and a SVM with a linear kernel was trained on the training set. To evaluate the performance of the SVM classifier, values of sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV) and accuracy (Acc) were calculated. The SPM12 analysis on the training set exhibited the already reported hypometabolism pattern involving occipito-temporal and fronto-parietal cortices, limbic system and cerebellum. The SVM procedure, based on the t-test mask generated from the training set, correctly classified MMF patients of the testing set with following Se, Sp, PPV, NPV and Acc: 89%, 85%, 85%, 89%, and 87%. We developed an original and individual approach including a SVM to classify patients between healthy or MMF metabolic brain profiles using 18F-FDG-PET. Machine learning algorithms are promising for computer-aided diagnosis but will need further validation in prospective cohorts.

All solid-state high power microwave source with high repetition frequency.

PubMed

Bragg, J-W B; Sullivan, W W; Mauch, D; Neuber, A A; Dickens, J C

2013-05-01

An all solid-state, megawatt-class high power microwave system featuring a silicon carbide (SiC) photoconductive semiconductor switch (PCSS) and a ferrimagnetic-based, coaxial nonlinear transmission line (NLTL) is presented. A 1.62 cm(2), 50 kV 4H-SiC PCSS is hard-switched to produce electrical pulses with 7 ns full width-half max (FWHM) pulse widths at 2 ns risetimes in single shot and burst-mode operation. The PCSS resistance drops to sub-ohm when illuminated with approximately 3 mJ of laser energy at 355 nm (tripled Nd:YAG) in a single pulse. Utilizing a fiber optic based optical delivery system, a laser pulse train of four 7 ns (FWHM) signals was generated at 65 MHz repetition frequency. The resulting electrical pulse train from the PCSS closely follows the optical input and is utilized to feed the NLTL generating microwave pulses with a base microwave-frequency of about 2.1 GHz at 65 MHz pulse repetition frequency (prf). Under typical experimental conditions, the NLTL produces sharpened output risetimes of 120 ps and microwave oscillations at 2-4 GHz that are generated due to damped gyromagnetic precession of the ferrimagnetic material's axially pre-biased magnetic moments. The complete system is discussed in detail with its output matched into 50 Ω, and results covering MHz-prf in burst-mode operation as well as frequency agility in single shot operation are discussed.

[The new face of Portuguese medicine: the generation of 1911 and the research school of Marck Athias].

PubMed

Amaral, Isabel Maria

2011-01-01

This paper aims to demonstrate that with Marck Athias, the Portuguese medicine inaugurated a new chapter in its history, in the Republic period, characterized by the experimental training at the laboratory. Thus, book-based knowledge gave way to a more clinically based approach favouring laboratory practice and basic research within several scientific domains. This new perspective operated important changes in the Portuguese medical community in the first half of century XX. Marck Athias (1857-1946), a Portuguese, was a physician trained at the University of Paris under Mathias Duval (a former student of Santiago Rámon y Cajal). It was in his laboratory that Athias began his career as researcher. Returning to Portugal, Athias founded a research school in physiology and histology which stressed a new approach in medicine based on experimental research. At the beginning of the twentieth century, scientific research in Portugal was virtually devoid of any of the practical clinic aspects. It is in fact Athias who introduced a new scientific perspective in Portuguese scientific community as well as influenced generations of graduate students in several national higher education and scientific research centres associated with Medicine. His influence and impact was due in great part to the underlying ideology of a positivist nature which succeeded in attracting several generations of followers, promoting a new step for the modernization of Portuguese medicine.

Lay Health Influencers: How They Tailor Brief Tobacco Cessation Interventions

PubMed Central

Yuan, Nicole P.; Castañeda, Heide; Nichter, Mark; Nichter, Mimi; Wind, Steven; Carruth, Lauren; Muramoto, Myra

2014-01-01

Interventions tailored to individual smoker characteristics have increasingly received attention in the tobacco control literature. The majority of tailored interventions are generated by computers and administered with printed materials or Web-based programs. The purpose of this study was to examine the tailoring activities of community lay health influencers who were trained to perform face-to-face brief tobacco cessation interventions. Eighty participants of a large-scale, randomized controlled trial completed a 6-week qualitative follow-up interview. A majority of participants (86%) reported that they made adjustments in their intervention behaviors based on individual smoker characteristics, their relationship with the smoker, and/or setting. Situational contexts (i.e., location and timing) primarily played a role after targeted smokers were selected. The findings suggest that lay health influencers benefit from a training curriculum that emphasizes a motivational, person-centered approach to brief cessation interventions. Recommendations for future tobacco cessation intervention trainings are presented. PMID:21986244

Lay health influencers: how they tailor brief tobacco cessation interventions.

PubMed

Yuan, Nicole P; Castañeda, Heide; Nichter, Mark; Nichter, Mimi; Wind, Steven; Carruth, Lauren; Muramoto, Myra

2012-10-01

Interventions tailored to individual smoker characteristics have increasingly received attention in the tobacco control literature. The majority of tailored interventions are generated by computers and administered with printed materials or web-based programs. The purpose of this study was to examine the tailoring activities of community lay health influencers who were trained to perform face-to-face brief tobacco cessation interventions. Eighty participants of a large-scale, randomized controlled trial completed a 6-week qualitative follow-up interview. A majority of participants (86%) reported that they made adjustments in their intervention behaviors based on individual smoker characteristics, their relationship with the smoker, and/or setting. Situational contexts (i.e., location and timing) primarily played a role after targeted smokers were selected. The findings suggest that lay health influencers benefit from a training curriculum that emphasizes a motivational, person-centered approach to brief cessation interventions. Recommendations for future tobacco cessation intervention trainings are presented.

Bringing X, Y, Z Generations Together to Facilitate School-Based Sexual and Reproductive Health Education

PubMed Central

Kamrani, Mahnaz Akbari; Yahya, Sharifah Syed

2016-01-01

This generic qualitative study explores the perspective of Malaysian teachers regarding the constraints of the current school-based sexual and reproductive health education in secondary schools of Klang-Valley Malaysia. For this study, in-depth interviews were conducted with twenty eight science teachers of government schools. The majority of participants named the teaching strategy and capacity of teachers, the lack of co-operation from the school and parents, limited resources in teaching and students themselves as some of the challenges. We concluded that if sexual health education is to be effective, it needs to be provided by people who have some specialized training. The teachers should be trained to teach sexual reproductive health education classes at the basic level, and in-service training for teachers already in the field should be intensified. Local adaptation to culture, language, religion, and so forth is often necessary. PMID:27157180

Bringing X, Y, Z Generations Together to Facilitate School-Based Sexual and Reproductive Health Education.

PubMed

Akbari Kamrani, Mahnaz; Yahya, Sharifah Syed

2016-09-01

This generic qualitative study explores the perspective of Malaysian teachers regarding the constraints of the current school-based sexual and reproductive health education in secondary schools of Klang-Valley Malaysia. For this study, in-depth interviews were conducted with twenty eight science teachers of government schools. The majority of participants named the teaching strategy and capacity of teachers, the lack of co-operation from the school and parents, limited resources in teaching and students themselves as some of the challenges. We concluded that if sexual health education is to be effective, it needs to be provided by people who have some specialized training. The teachers should be trained to teach sexual reproductive health education classes at the basic level, and in-service training for teachers already in the field should be intensified. Local adaptation to culture, language, religion, and so forth is often necessary.

Pseudo CT estimation from MRI using patch-based random forest

NASA Astrophysics Data System (ADS)

Yang, Xiaofeng; Lei, Yang; Shu, Hui-Kuo; Rossi, Peter; Mao, Hui; Shim, Hyunsuk; Curran, Walter J.; Liu, Tian

2017-02-01

Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.

A self-trained classification technique for producing 30 m percent-water maps from Landsat data

USGS Publications Warehouse

Rover, Jennifer R.; Wylie, Bruce K.; Ji, Lei

2010-01-01

Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.

A compact ball screw based electromagnetic energy harvester for railroad application

NASA Astrophysics Data System (ADS)

Pan, Yu; Lin, Teng; Liu, Cheng; Yu, Jie; Zuo, Jianyong; Zuo, Lei

2018-03-01

To enable the smart technologies, such as the positive train controls, rail damage detection and track health monitoring on the railroad side, the electricity is required and in needed. In this paper, we proposed a novel ball-screw based electromagnetic energy harvester for railway track with mechanical-motion-rectifier (MMR) mechanism, to harvest the energy that usually dissipated and wasted during train induced track vibration. Ball screw based design reduces backlash during motion transmission, and MMR nonlinear characteristics with one way clutches makes the harvester convert the bi-direction track vibration into a generator's unidirectional rotation, which improves the transmission reliability and increases the energy harvesting efficiency. A systematic model combining train-rail-harvester was established to analyze the dynamic characteristic of the proposed railway energy, and lab and in-field tests were carried out to experimentally characterize the proposed energy harvester. In lab bench test showed the proposed harvester reached a 70% mechanical efficiency with a high sensitivity to the environment vibration. In filed test showed that a peak 7.8W phase power was achieved when a two marshaling type A metro train passed by with a 30 km/h.

4D Cone-beam CT reconstruction using a motion model based on principal component analysis

PubMed Central

Staub, David; Docef, Alen; Brock, Robert S.; Vaman, Constantin; Murphy, Martin J.

2011-01-01

Purpose: To provide a proof of concept validation of a novel 4D cone-beam CT (4DCBCT) reconstruction algorithm and to determine the best methods to train and optimize the algorithm. Methods: The algorithm animates a patient fan-beam CT (FBCT) with a patient specific parametric motion model in order to generate a time series of deformed CTs (the reconstructed 4DCBCT) that track the motion of the patient anatomy on a voxel by voxel scale. The motion model is constrained by requiring that projections cast through the deformed CT time series match the projections of the raw patient 4DCBCT. The motion model uses a basis of eigenvectors that are generated via principal component analysis (PCA) of a training set of displacement vector fields (DVFs) that approximate patient motion. The eigenvectors are weighted by a parameterized function of the patient breathing trace recorded during 4DCBCT. The algorithm is demonstrated and tested via numerical simulation. Results: The algorithm is shown to produce accurate reconstruction results for the most complicated simulated motion, in which voxels move with a pseudo-periodic pattern and relative phase shifts exist between voxels. The tests show that principal component eigenvectors trained on DVFs from a novel 2D/3D registration method give substantially better results than eigenvectors trained on DVFs obtained by conventionally registering 4DCBCT phases reconstructed via filtered backprojection. Conclusions: Proof of concept testing has validated the 4DCBCT reconstruction approach for the types of simulated data considered. In addition, the authors found the 2D/3D registration approach to be our best choice for generating the DVF training set, and the Nelder-Mead simplex algorithm the most robust optimization routine. PMID:22149852

Simple and robust generation of ultrafast laser pulse trains using polarization-independent parallel-aligned thin films

NASA Astrophysics Data System (ADS)

Wang, Andong; Jiang, Lan; Li, Xiaowei; Wang, Zhi; Du, Kun; Lu, Yongfeng

2018-05-01

Ultrafast laser pulse temporal shaping has been widely applied in various important applications such as laser materials processing, coherent control of chemical reactions, and ultrafast imaging. However, temporal pulse shaping has been limited to only-in-lab technique due to the high cost, low damage threshold, and polarization dependence. Herein we propose a novel design of ultrafast laser pulse train generation device, which consists of multiple polarization-independent parallel-aligned thin films. Various pulse trains with controllable temporal profile can be generated flexibly by multi-reflections within the splitting films. Compared with other pulse train generation techniques, this method has advantages of compact structure, low cost, high damage threshold and polarization independence. These advantages endow it with high potential for broad utilization in ultrafast applications.

Automatic tissue segmentation of breast biopsies imaged by QPI

NASA Astrophysics Data System (ADS)

Majeed, Hassaan; Nguyen, Tan; Kandel, Mikhail; Marcias, Virgilia; Do, Minh; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

2016-03-01

The current tissue evaluation method for breast cancer would greatly benefit from higher throughput and less inter-observer variation. Since quantitative phase imaging (QPI) measures physical parameters of tissue, it can be used to find quantitative markers, eliminating observer subjectivity. Furthermore, since the pixel values in QPI remain the same regardless of the instrument used, classifiers can be built to segment various tissue components without need for color calibration. In this work we use a texton-based approach to segment QPI images of breast tissue into various tissue components (epithelium, stroma or lumen). A tissue microarray comprising of 900 unstained cores from 400 different patients was imaged using Spatial Light Interference Microscopy. The training data were generated by manually segmenting the images for 36 cores and labelling each pixel (epithelium, stroma or lumen.). For each pixel in the data, a response vector was generated by the Leung-Malik (LM) filter bank and these responses were clustered using the k-means algorithm to find the centers (called textons). A random forest classifier was then trained to find the relationship between a pixel's label and the histogram of these textons in that pixel's neighborhood. The segmentation was carried out on the validation set by calculating the texton histogram in a pixel's neighborhood and generating a label based on the model learnt during training. Segmentation of the tissue into various components is an important step toward efficiently computing parameters that are markers of disease. Automated segmentation, followed by diagnosis, can improve the accuracy and speed of analysis leading to better health outcomes.

Generation and Termination of Binary Decision Trees for Nonparametric Multiclass Classification.

DTIC Science & Technology

1984-10-01

O M coF=F;; UMBER2. GOVT ACCE5SION NO.1 3 . REC,PINS :A7AL:,G NUMBER ( ’eneration and Terminat_,on :)f Binary D-ecision jC j ik; Trees for Nonnararetrc...1-I . v)IAMO 0~I4 EDvt" O F I 00 . 3 15I OR%.OL.ETL - S-S OCTOBER 1984 LIDS-P-1411 GENERATION AND TERMINATION OF BINARY DECISION TREES FOR...minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively. 0 0 0/ 6 0¢ A 3 I. Introduction We state

Fast dictionary-based reconstruction for diffusion spectrum imaging.

PubMed

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F; Yendiki, Anastasia; Wald, Lawrence L; Adalsteinsson, Elfar

2013-11-01

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

Fast Dictionary-Based Reconstruction for Diffusion Spectrum Imaging

PubMed Central

Bilgic, Berkin; Chatnuntawech, Itthi; Setsompop, Kawin; Cauley, Stephen F.; Yendiki, Anastasia; Wald, Lawrence L.; Adalsteinsson, Elfar

2015-01-01

Diffusion Spectrum Imaging (DSI) reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation (TV) transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using Matlab running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using Principal Component Analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm. PMID:23846466

Artificial Neural Network with Hardware Training and Hardware Refresh

NASA Technical Reports Server (NTRS)

Duong, Tuan A. (Inventor)

2003-01-01

A neural network circuit is provided having a plurality of circuits capable of charge storage. Also provided is a plurality of circuits each coupled to at least one of the plurality of charge storage circuits and constructed to generate an output in accordance with a neuron transfer function. Each of a plurality of circuits is coupled to one of the plurality of neuron transfer function circuits and constructed to generate a derivative of the output. A weight update circuit updates the charge storage circuits based upon output from the plurality of transfer function circuits and output from the plurality of derivative circuits. In preferred embodiments, separate training and validation networks share the same set of charge storage circuits and may operate concurrently. The validation network has a separate transfer function circuits each being coupled to the charge storage circuits so as to replicate the training network s coupling of the plurality of charge storage to the plurality of transfer function circuits. The plurality of transfer function circuits may be constructed each having a transconductance amplifier providing differential currents combined to provide an output in accordance with a transfer function. The derivative circuits may have a circuit constructed to generate a biased differential currents combined so as to provide the derivative of the transfer function.

Financial impact of surgical training on hospital economics: an income analysis of 1184 out-patient clinic consultations.

PubMed

Fitzgerald, J E F; Ravindra, P; Lepore, M; Armstrong, A; Bhangu, A; Maxwell-Armstrong, C A

2013-01-01

In many countries healthcare commissioning bodies (state or insurance-based) reimburse hospitals for their activity. The costs associated with post-graduate clinical training as part of this are poorly understood. This study quantified the financial revenue generated by surgical trainees in the out-patient clinic setting. A retrospective analysis of surgical out-patient ambulatory care appointments under 6 full-time equivalent Consultants (Attendings) in one hospital over 2 months. Clinic attendance lists were generated from the Patient Access System. Appointments were categorised as: 'new', 'review' or 'procedure' as per the Department of Health Payment by Results (PbR) Outpatient Tariff (Outpatient Treatment Function Code 104; Outpatient Procedure Code OPRSI1). During the study period 78 clinics offered 1184 appointments; 133 of these were not attended (11.2%). Of those attended 1029 had sufficient detail for analysis (98%). 261 (25.4%) patients were seen by a trainee. Applying PbR reimbursement criteria to these gave a projected annual income of £GBP 218,712 (€EU 266,527; $USD 353,657) generated by 6 surgical trainees (Residents). This is equivalent to approximately £GBP 36,452 (€EU 44,415; $USD 58,943) per trainee annually compared to £GBP 48,732 (€EU 59,378; $USD 78,800) per Consultant. This projected yearly income off-set 95% of the trainee's basic salary. Surgical trainees generated a quarter of the out-patient clinic activity related income in this study, with each trainee producing three-quarters of that generated by a Consultant. This offers considerable commercial value to hospitals. Although this must offset productivity differences and overall running costs, training bodies should ensure hospitals offer an appropriate return. In a competitive market hospitals could be invited to compete for trainees, with preference given to those providing excellence in training. Copyright © 2013 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

Assessment of risks for high-speed rail grade crossings on the Empire Corridor : next generation high-speed rail program

DOT National Transportation Integrated Search

2000-08-01

The report describes a risk-based approach for assessing the implications of higher train speeds on highway-railroad grade crossing safety, and allocating limited resources to best reduce this risk. To predict accident frequency, an existing DOT mode...

Automated Instructional Monitors for Complex Operational Tasks. Final Report.

ERIC Educational Resources Information Center

Feurzeig, Wallace

A computer-based instructional system is described which incorporates diagnosis of students difficulties in acquiring complex concepts and skills. A computer automatically generated a simulated display. It then monitored and analyzed a student's work in the performance of assigned training tasks. Two major tasks were studied. The first,…

Natural Language Processing and Game-Based Practice in iSTART

ERIC Educational Resources Information Center

Jackson, Tanner; Boonthum-Denecke, Chutima; McNamara, Danielle

2015-01-01

Intelligent Tutoring Systems (ITSs) are situated in a potential struggle between effective pedagogy and system enjoyment and engagement. iSTART (Interactive Strategy Training for Active Reading and Thinking), a reading strategy tutoring system in which students practice generating self-explanations and using reading strategies, employs two devices…

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

Chen, X; Wang, J; Hu, W

Purpose: The Varian RapidPlan™ is a commercial knowledge-based optimization process which uses a set of clinically used treatment plans to train a model that can predict individualized dose-volume objectives. The purpose of this study is to evaluate the performance of RapidPlan to generate intensity modulated radiation therapy (IMRT) plans for cervical cancer. Methods: Totally 70 IMRT plans for cervical cancer with varying clinical and physiological indications were enrolled in this study. These patients were all previously treated in our institution. There were two prescription levels usually used in our institution: 45Gy/25 fractions and 50.4Gy/28 fractions. 50 of these plans weremore » selected to train the RapidPlan model for predicting dose-volume constraints. After model training, this model was validated with 10 plans from training pool(internal validation) and additional other 20 new plans(external validation). All plans used for the validation were re-optimized with the original beam configuration and the generated priorities from RapidPlan were manually adjusted to ensure that re-optimized DVH located in the range of the model prediction. DVH quantitative analysis was performed to compare the RapidPlan generated and the original manual optimized plans. Results: For all the validation cases, RapidPlan based plans (RapidPlan) showed similar or superior results compared to the manual optimized ones. RapidPlan increased the result of D98% and homogeneity in both two validations. For organs at risk, the RapidPlan decreased mean doses of bladder by 1.25Gy/1.13Gy (internal/external validation) on average, with p=0.12/p<0.01. The mean dose of rectum and bowel were also decreased by an average of 2.64Gy/0.83Gy and 0.66Gy/1.05Gy,with p<0.01/ p<0.01and p=0.04/<0.01 for the internal/external validation, respectively. Conclusion: The RapidPlan model based cervical cancer plans shows ability to systematically improve the IMRT plan quality. It suggests that RapidPlan has great potential to make the treatment planning process more efficient.« less

Advanced on-the-Job Training System: Readiness Test Plan

DTIC Science & Technology

1990-05-01

entry of completions into an airman’s ATR. To determine if a specific test objective has been met, a "measure of effectiveness" (MOE) must be de ...Evaluasto acces control Test 4 Supervisor s control Test 5 Training Manager s control Test 6 GenerateATR Test 7 Generat OPTR Test 8 Generate I TR Teot 9...Trainer, Supervisor , Evaluator, Training MMnger) into only properly authorized AOTS components. This critical function Is therefore related to each of the

Should Body Weight–Supported Treadmill Training and Robotic-Assistive Steppers for Locomotor Training Trot Back to the Starting Gate?

PubMed Central

Dobkin, Bruce H.; Duncan, Pamela W.

2014-01-01

Body weight–supported treadmill training (BWSTT) and robotic-assisted step training (RAST) have not, so far, led to better outcomes than a comparable dose of progressive over-ground training (OGT) for disabled persons with stroke, spinal cord injury, multiple sclerosis, Parkinson’s disease, or cerebral palsy. The conceptual bases for these promising rehabilitation interventions had once seemed quite plausible, but the results of well-designed, randomized clinical trials have been disappointing. The authors reassess the underpinning concepts for BWSTT and RAST, which were derived from mammalian studies of treadmill-induced hind-limb stepping associated with central pattern generation after low thoracic spinal cord transection, as well as human studies of the triple crown icons of task-oriented locomotor training, massed practice, and activity-induced neuroplasticity. The authors retrospectively consider where theory and practice may have fallen short in the pilot studies that aimed to produce thoroughbred interventions. Based on these shortcomings, the authors move forward with recommendations for the future development of workhorse interventions for walking. In the absence of evidence for physical therapists to employ these strategies, however, BWSTT and RAST should not be provided routinely to disabled, vulnerable persons in place of OGT outside of a scientifically conducted efficacy trial. PMID:22412172

Component-based subspace linear discriminant analysis method for face recognition with one training sample

NASA Astrophysics Data System (ADS)

Huang, Jian; Yuen, Pong C.; Chen, Wen-Sheng; Lai, J. H.

2005-05-01

Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging.

Should body weight-supported treadmill training and robotic-assistive steppers for locomotor training trot back to the starting gate?

PubMed

Dobkin, Bruce H; Duncan, Pamela W

2012-05-01

Body weight-supported treadmill training (BWSTT) and robotic-assisted step training (RAST) have not, so far, led to better outcomes than a comparable dose of progressive over-ground training (OGT) for disabled persons with stroke, spinal cord injury, multiple sclerosis, Parkinson's disease, or cerebral palsy. The conceptual bases for these promising rehabilitation interventions had once seemed quite plausible, but the results of well-designed, randomized clinical trials have been disappointing. The authors reassess the underpinning concepts for BWSTT and RAST, which were derived from mammalian studies of treadmill-induced hind-limb stepping associated with central pattern generation after low thoracic spinal cord transection, as well as human studies of the triple crown icons of task-oriented locomotor training, massed practice, and activity-induced neuroplasticity. The authors retrospectively consider where theory and practice may have fallen short in the pilot studies that aimed to produce thoroughbred interventions. Based on these shortcomings, the authors move forward with recommendations for the future development of workhorse interventions for walking. In the absence of evidence for physical therapists to employ these strategies, however, BWSTT and RAST should not be provided routinely to disabled, vulnerable persons in place of OGT outside of a scientifically conducted efficacy trial.

Towards case-based medical learning in radiological decision making using content-based image retrieval

PubMed Central

2011-01-01

Background Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education. Methods We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment. Results We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system. Conclusions The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer. PMID:22032775

Towards case-based medical learning in radiological decision making using content-based image retrieval.

PubMed

Welter, Petra; Deserno, Thomas M; Fischer, Benedikt; Günther, Rolf W; Spreckelsen, Cord

2011-10-27

Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education. We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment. We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system. The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer.

Neurobehavioral impairments produced by developmental lead exposure persisted for generations in zebrafish (Danio rerio).

PubMed

Xu, Xiaojuan; Weber, Daniel; Burge, Rebekah; VanAmberg, Kelsey

2016-01-01

The zebrafish has become a useful animal model for studying the effects of environmental contaminants on neurobehavioral development due to its ease of breeding, high number of eggs per female, short generation times, and a well-established avoidance conditioning paradigm. Using avoidance conditioning as the behavioral paradigm, the present study investigated the effects of embryonic exposure to lead (Pb) on learning in adult zebrafish and the third (F3) generation of those fish. In Experiment 1, adult zebrafish that were developmentally exposed to 0.0, 0.1, 1.0 or 10.0μM Pb (2-24h post fertilization) as embryos were trained and tested for avoidance responses. The results showed that adult zebrafish hatched from embryos exposed to 0.0 or 0.1μM Pb learned avoidance responses during training and displayed significantly increased avoidance responses during testing, while those hatched from embryos exposed to 1.0 or 10.0μM Pb displayed no significant increases in avoidance responses from training to testing. In Experiment 2, the F3 generation of zebrafish that were developmentally exposed to an identical exposure regimen as in Experiment 1 were trained and tested for avoidance responses. The results showed that the F3 generation of zebrafish developmentally exposed as embryos to 0.0 or 0.1μM Pb learned avoidance responses during training and displayed significantly increased avoidance responses during testing, while the F3 generation of zebrafish developmentally exposed as embryos to 1.0 or 10.0μM Pb displayed no significant changes in avoidance responses from training to testing. Thus, developmental Pb exposure produced learning impairments that persisted for at least three generations, demonstrating trans-generational effects of embryonic exposure to Pb. Copyright © 2015. Published by Elsevier B.V.

A hybrid deep learning approach to predict malignancy of breast lesions using mammograms

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Heidari, Morteza; Mirniaharikandehei, Seyedehnafiseh; Gong, Jing; Qian, Wei; Qiu, Yuchen; Zheng, Bin

2018-03-01

Applying deep learning technology to medical imaging informatics field has been recently attracting extensive research interest. However, the limited medical image dataset size often reduces performance and robustness of the deep learning based computer-aided detection and/or diagnosis (CAD) schemes. In attempt to address this technical challenge, this study aims to develop and evaluate a new hybrid deep learning based CAD approach to predict likelihood of a breast lesion detected on mammogram being malignant. In this approach, a deep Convolutional Neural Network (CNN) was firstly pre-trained using the ImageNet dataset and serve as a feature extractor. A pseudo-color Region of Interest (ROI) method was used to generate ROIs with RGB channels from the mammographic images as the input to the pre-trained deep network. The transferred CNN features from different layers of the CNN were then obtained and a linear support vector machine (SVM) was trained for the prediction task. By applying to a dataset involving 301 suspicious breast lesions and using a leave-one-case-out validation method, the areas under the ROC curves (AUC) = 0.762 and 0.792 using the traditional CAD scheme and the proposed deep learning based CAD scheme, respectively. An ensemble classifier that combines the classification scores generated by the two schemes yielded an improved AUC value of 0.813. The study results demonstrated feasibility and potentially improved performance of applying a new hybrid deep learning approach to develop CAD scheme using a relatively small dataset of medical images.

[The two-year post graduate training program for nurses: implementation status and personal perspectives].

PubMed

Yin, Yu-Chun

2013-06-01

The Taiwan Joint Commission on Hospital Accreditation (TJCHA) authorized the Teaching Quality Improvement Program for Teaching Hospitals as a way for the Department of Health to plan and implement improvements. The program assists medical and paramedical professionals to establish a postgraduate clinical training system. The two-year postgraduate training program for nurses is one of the program's regular activities, divided into three phases that include location-based curriculum training (3 months), core curriculum training (9 months), and professional courses training (12 months). This paper describes the origin, current implementation status, and efficacy / key problems of this two-year post graduate training program, Information regarding the opinions of new nurses, preceptors, and nursing managers on the three aspects is drawn from the author's relevant professional experience, interactions with nurses, and a review of the literature. Findings include: (1) nursing departments should operate in accordance with TJCHA guidelines; (2) department training should be adequate to promote the ability and willingness of nurses to train a new generation of clinical preceptors; and (3) participant opinions on project execution progress and difficulties. Findings may be referenced to better achieve Teaching Quality Improvement Program for Teaching Hospital objectives.

Vehicle classification in WAMI imagery using deep network

NASA Astrophysics Data System (ADS)

Yi, Meng; Yang, Fan; Blasch, Erik; Sheaff, Carolyn; Liu, Kui; Chen, Genshe; Ling, Haibin

2016-05-01

Humans have always had a keen interest in understanding activities and the surrounding environment for mobility, communication, and survival. Thanks to recent progress in photography and breakthroughs in aviation, we are now able to capture tens of megapixels of ground imagery, namely Wide Area Motion Imagery (WAMI), at multiple frames per second from unmanned aerial vehicles (UAVs). WAMI serves as a great source for many applications, including security, urban planning and route planning. These applications require fast and accurate image understanding which is time consuming for humans, due to the large data volume and city-scale area coverage. Therefore, automatic processing and understanding of WAMI imagery has been gaining attention in both industry and the research community. This paper focuses on an essential step in WAMI imagery analysis, namely vehicle classification. That is, deciding whether a certain image patch contains a vehicle or not. We collect a set of positive and negative sample image patches, for training and testing the detector. Positive samples are 64 × 64 image patches centered on annotated vehicles. We generate two sets of negative images. The first set is generated from positive images with some location shift. The second set of negative patches is generated from randomly sampled patches. We also discard those patches if a vehicle accidentally locates at the center. Both positive and negative samples are randomly divided into 9000 training images and 3000 testing images. We propose to train a deep convolution network for classifying these patches. The classifier is based on a pre-trained AlexNet Model in the Caffe library, with an adapted loss function for vehicle classification. The performance of our classifier is compared to several traditional image classifier methods using Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG) features. While the SVM+HOG method achieves an accuracy of 91.2%, the accuracy of our deep network-based classifier reaches 97.9%.

An Oracle-based co-training framework for writer identification in offline handwriting

NASA Astrophysics Data System (ADS)

Porwal, Utkarsh; Rajan, Sreeranga; Govindaraju, Venu

2012-01-01

State-of-the-art techniques for writer identification have been centered primarily on enhancing the performance of the system for writer identification. Machine learning algorithms have been used extensively to improve the accuracy of such system assuming sufficient amount of data is available for training. Little attention has been paid to the prospect of harnessing the information tapped in a large amount of un-annotated data. This paper focuses on co-training based framework that can be used for iterative labeling of the unlabeled data set exploiting the independence between the multiple views (features) of the data. This paradigm relaxes the assumption of sufficiency of the data available and tries to generate labeled data from unlabeled data set along with improving the accuracy of the system. However, performance of co-training based framework is dependent on the effectiveness of the algorithm used for the selection of data points to be added in the labeled set. We propose an Oracle based approach for data selection that learns the patterns in the score distribution of classes for labeled data points and then predicts the labels (writers) of the unlabeled data point. This method for selection statistically learns the class distribution and predicts the most probable class unlike traditional selection algorithms which were based on heuristic approaches. We conducted experiments on publicly available IAM dataset and illustrate the efficacy of the proposed approach.

Credit WCT. Photographic copy of photograph, view east southeast across ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Credit WCT. Photographic copy of photograph, view east southeast across Dd station ejectors showing detail of "Hyprox" steam generator. Note that steam generator is placed above Z-stage ejector; an insulated pipe running between the Dd train rails supplies steam to the Y-Stage ejector. Note emergency eyewash stand at extreme right of view. (JPL negative no. 384-3376, 3 December 1962) - Jet Propulsion Laboratory Edwards Facility, Test Stand D, Edwards Air Force Base, Boron, Kern County, CA

Evaluating Photographs as a Replacement for the In-Person Physical Examination of the Scored Patient-Generated Subjective Global Assessment in Elderly Hospital Patients.

PubMed

Miller, Michelle; Thomas, Jolene; Suen, Jenni; Ong, De Sheng; Sharma, Yogesh

2018-05-01

Undernourished patients discharged from the hospital require follow-up; however, attendance at return visits is low. Teleconsultations may allow remote follow-up of undernourished patients; however, no valid method to remotely perform physical examination, a critical component of assessing nutritional status, exists. This study aims to compare agreement between photographs taken by trained dietitians and in-person physical examinations conducted by trained dietitians to rate the overall physical examination section of the scored Patient Generated Subjective Global Assessment (PG-SGA). Nested cross-sectional study. Adults aged ≥60 years, admitted to the general medicine unit at Flinders Medical Centre between March 2015 and March 2016, were eligible. All components of the PG-SGA and photographs of muscle and fat sites were collected from 192 participants either in the hospital or at their place of residence after discharge. Validity of photograph-based physical examination was determined by collecting photographic and PG-SGA data from each participant at one encounter by trained dietitians. A dietitian blinded to data collection later assessed de-identified photographs on a computer. Percentage agreement, weighted kappa agreement, sensitivity, and specificity between the photographs and in-person physical examinations were calculated. All data collected were included in the analysis. Overall, the photograph-based physical examination rating achieved a percentage agreement of 75.8% against the in-person assessment, with a weighted kappa agreement of 0.526 (95% CI: 0.416, 0.637; P<0.05) and a sensitivity-specificity pair of 66.9% (95% CI: 57.8%, 75.0%) and 92.4% (95% CI: 82.5%, 97.2%). Photograph-based physical examination by trained dietitians achieved a nearly acceptable percentage agreement, moderate weighted kappa, and fair sensitivity-specificity pair. Methodological refinement before field testing with other personnel may improve the agreement and accuracy of photograph-based physical examination. Copyright © 2018 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

TermGenie – a web-application for pattern-based ontology class generation

DOE PAGES

Dietze, Heiko; Berardini, Tanya Z.; Foulger, Rebecca E.; ...

2014-01-01

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 newmore » classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. Lastly, TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.« less

TermGenie – a web-application for pattern-based ontology class generation

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

Dietze, Heiko; Berardini, Tanya Z.; Foulger, Rebecca E.

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 newmore » classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. Lastly, TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.« less

TermGenie - a web-application for pattern-based ontology class generation.

PubMed

Dietze, Heiko; Berardini, Tanya Z; Foulger, Rebecca E; Hill, David P; Lomax, Jane; Osumi-Sutherland, David; Roncaglia, Paola; Mungall, Christopher J

2014-01-01

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 new classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.

Meditation experts try Virtual Reality Mindfulness: A pilot study evaluation of the feasibility and acceptability of Virtual Reality to facilitate mindfulness practice in people attending a Mindfulness conference.

PubMed

Navarro-Haro, María V; López-Del-Hoyo, Yolanda; Campos, Daniel; Linehan, Marsha M; Hoffman, Hunter G; García-Palacios, Azucena; Modrego-Alarcón, Marta; Borao, Luis; García-Campayo, Javier

2017-01-01

Regular mindfulness practice benefits people both mentally and physically, but many populations who could benefit do not practice mindfulness. Virtual Reality (VR) is a new technology that helps capture participants' attention and gives users the illusion of "being there" in the 3D computer generated environment, facilitating sense of presence. By limiting distractions from the real world, increasing sense of presence and giving people an interesting place to go to practice mindfulness, Virtual Reality may facilitate mindfulness practice. Traditional Dialectical Behavioral Therapy (DBT®) mindfulness skills training was specifically designed for clinical treatment of people who have trouble focusing attention, however severe patients often show difficulties or lack of motivation to practice mindfulness during the training. The present pilot study explored whether a sample of mindfulness experts would find useful and recommend a new VR Dialectical Behavioral Therapy (DBT®) mindfulness skills training technique and whether they would show any benefit. Forty four participants attending a mindfulness conference put on an Oculus Rift DK2 Virtual Reality helmet and floated down a calm 3D computer generated virtual river while listening to digitized DBT® mindfulness skills training instructions. On subjective questionnaires completed by the participants before and after the VR DBT® mindfulness skills training session, participants reported increases/improvements in state of mindfulness, and reductions in negative emotional states. After VR, participants reported significantly less sadness, anger, and anxiety, and reported being significantly more relaxed. Participants reported a moderate to strong illusion of going inside the 3D computer generated world (i.e., moderate to high "presence" in VR) and showed high acceptance of VR as a technique to practice mindfulness. These results show encouraging preliminary evidence of the feasibility and acceptability of using VR to practice mindfulness based on clinical expert feedback. VR is a technology with potential to increase computerized dissemination of DBT® skills training modules. Future research is warranted.

Meditation experts try Virtual Reality Mindfulness: A pilot study evaluation of the feasibility and acceptability of Virtual Reality to facilitate mindfulness practice in people attending a Mindfulness conference.

PubMed Central

Navarro-Haro, María V.; López-del-Hoyo, Yolanda; Campos, Daniel; Linehan, Marsha M.; Hoffman, Hunter G.; García-Palacios, Azucena; Modrego-Alarcón, Marta; Borao, Luis; García-Campayo, Javier

2017-01-01

Regular mindfulness practice benefits people both mentally and physically, but many populations who could benefit do not practice mindfulness. Virtual Reality (VR) is a new technology that helps capture participants’ attention and gives users the illusion of “being there” in the 3D computer generated environment, facilitating sense of presence. By limiting distractions from the real world, increasing sense of presence and giving people an interesting place to go to practice mindfulness, Virtual Reality may facilitate mindfulness practice. Traditional Dialectical Behavioral Therapy (DBT®) mindfulness skills training was specifically designed for clinical treatment of people who have trouble focusing attention, however severe patients often show difficulties or lack of motivation to practice mindfulness during the training. The present pilot study explored whether a sample of mindfulness experts would find useful and recommend a new VR Dialectical Behavioral Therapy (DBT®) mindfulness skills training technique and whether they would show any benefit. Forty four participants attending a mindfulness conference put on an Oculus Rift DK2 Virtual Reality helmet and floated down a calm 3D computer generated virtual river while listening to digitized DBT® mindfulness skills training instructions. On subjective questionnaires completed by the participants before and after the VR DBT® mindfulness skills training session, participants reported increases/improvements in state of mindfulness, and reductions in negative emotional states. After VR, participants reported significantly less sadness, anger, and anxiety, and reported being significantly more relaxed. Participants reported a moderate to strong illusion of going inside the 3D computer generated world (i.e., moderate to high “presence” in VR) and showed high acceptance of VR as a technique to practice mindfulness. These results show encouraging preliminary evidence of the feasibility and acceptability of using VR to practice mindfulness based on clinical expert feedback. VR is a technology with potential to increase computerized dissemination of DBT® skills training modules. Future research is warranted. PMID:29166665

Development of clinical practice guidelines.

PubMed

Hollon, Steven D; Areán, Patricia A; Craske, Michelle G; Crawford, Kermit A; Kivlahan, Daniel R; Magnavita, Jeffrey J; Ollendick, Thomas H; Sexton, Thomas L; Spring, Bonnie; Bufka, Lynn F; Galper, Daniel I; Kurtzman, Howard

2014-01-01

Clinical practice guidelines (CPGs) are intended to improve mental, behavioral, and physical health by promoting clinical practices that are based on the best available evidence. The American Psychological Association (APA) is committed to generating patient-focused CPGs that are scientifically sound, clinically useful, and informative for psychologists, other health professionals, training programs, policy makers, and the public. The Institute of Medicine (IOM) 2011 standards for generating CPGs represent current best practices in the field. These standards involve multidisciplinary guideline development panels charged with generating recommendations based on comprehensive systematic reviews of the evidence. The IOM standards will guide the APA as it generates CPGs that can be used to inform the general public and the practice community regarding the benefits and harms of various treatment options. CPG recommendations are advisory rather than compulsory. When used appropriately, high-quality guidelines can facilitate shared decision making and identify gaps in knowledge.

Mach-zehnder based optical marker/comb generator for streak camera calibration

DOEpatents

Miller, Edward Kirk

2015-03-03

This disclosure is directed to a method and apparatus for generating marker and comb indicia in an optical environment using a Mach-Zehnder (M-Z) modulator. High speed recording devices are configured to record image or other data defining a high speed event. To calibrate and establish time reference, the markers or combs are indicia which serve as timing pulses (markers) or a constant-frequency train of optical pulses (comb) to be imaged on a streak camera for accurate time based calibration and time reference. The system includes a camera, an optic signal generator which provides an optic signal to an M-Z modulator and biasing and modulation signal generators configured to provide input to the M-Z modulator. An optical reference signal is provided to the M-Z modulator. The M-Z modulator modulates the reference signal to a higher frequency optical signal which is output through a fiber coupled link to the streak camera.

Resource constrained design of artificial neural networks using comparator neural network

NASA Technical Reports Server (NTRS)

Wah, Benjamin W.; Karnik, Tanay S.

1992-01-01

We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

Field-based generation and social validation managers and staff competencies for small community residences.

PubMed

Thousand, J S; Burchard, S N; Hasazi, J E

1986-01-01

Characteristics and competencies for four staff positions in community residences for individuals with mental retardation were identified utilizing multiple empirical and deductive methods with field-based practitioners and field-based experts. The more commonly used competency generation methods of expert opinion and job performance analysis generated a high degree of knowledge and skill-based competencies similar to course curricula. Competencies generated by incumbent practitioners through open-ended methods of personal structured interview and critical incident analysis were ones which related to personal style, interpersonal interaction, and humanistic orientation. Although seldom included in staff, paraprofessional, or professional training curricula, these latter competencies include those identified by Carl Rogers as essential for developing an effective helping relationship in a therapeutic situation (i.e., showing liking, interest, and respect for the clients; being able to communicate positive regard to the client). Of 21 core competency statements selected as prerequisites to employment for all four staff positions, the majority (17 of 21) represented interpersonal skills important to working with others, including responsiveness to resident needs, personal valuation of persons with mental retardation, and normalization principles.

Sociopathic Knowledge Bases: Correct Knowledge Can Be Harmful Even Given Unlimited Computation

DTIC Science & Technology

1989-08-01

pobitive, as false positives generated by a medical program can often be caught by a physician upon further testing . False negatives, however, may be...improvement over the knowledge base tested is obtained. Although our work is pretty much theoretical research oriented one example of ex- periments is...knowledge base, improves the performance by about 10%. of tests . First, we divide the cases into a training set and a validation set with 70% vs. 30% each

Unbiased All-Optical Random-Number Generator

NASA Astrophysics Data System (ADS)

Steinle, Tobias; Greiner, Johannes N.; Wrachtrup, Jörg; Giessen, Harald; Gerhardt, Ilja

2017-10-01

The generation of random bits is of enormous importance in modern information science. Cryptographic security is based on random numbers which require a physical process for their generation. This is commonly performed by hardware random-number generators. These often exhibit a number of problems, namely experimental bias, memory in the system, and other technical subtleties, which reduce the reliability in the entropy estimation. Further, the generated outcome has to be postprocessed to "iron out" such spurious effects. Here, we present a purely optical randomness generator, based on the bistable output of an optical parametric oscillator. Detector noise plays no role and postprocessing is reduced to a minimum. Upon entering the bistable regime, initially the resulting output phase depends on vacuum fluctuations. Later, the phase is rigidly locked and can be well determined versus a pulse train, which is derived from the pump laser. This delivers an ambiguity-free output, which is reliably detected and associated with a binary outcome. The resulting random bit stream resembles a perfect coin toss and passes all relevant randomness measures. The random nature of the generated binary outcome is furthermore confirmed by an analysis of resulting conditional entropies.

Machine-learning-based real-bogus system for the HSC-SSP moving object detection pipeline

NASA Astrophysics Data System (ADS)

Lin, Hsing-Wen; Chen, Ying-Tung; Wang, Jen-Hung; Wang, Shiang-Yu; Yoshida, Fumi; Ip, Wing-Huen; Miyazaki, Satoshi; Terai, Tsuyoshi

2018-01-01

Machine-learning techniques are widely applied in many modern optical sky surveys, e.g., Pan-STARRS1, PTF/iPTF, and the Subaru/Hyper Suprime-Cam survey, to reduce human intervention in data verification. In this study, we have established a machine-learning-based real-bogus system to reject false detections in the Subaru/Hyper-Suprime-Cam Strategic Survey Program (HSC-SSP) source catalog. Therefore, the HSC-SSP moving object detection pipeline can operate more effectively due to the reduction of false positives. To train the real-bogus system, we use stationary sources as the real training set and "flagged" data as the bogus set. The training set contains 47 features, most of which are photometric measurements and shape moments generated from the HSC image reduction pipeline (hscPipe). Our system can reach a true positive rate (tpr) ˜96% with a false positive rate (fpr) ˜1% or tpr ˜99% at fpr ˜5%. Therefore, we conclude that stationary sources are decent real training samples, and using photometry measurements and shape moments can reject false positives effectively.

Effects of interactive visual feedback training on post-stroke pusher syndrome: a pilot randomized controlled study.

PubMed

Yang, Yea-Ru; Chen, Yi-Hua; Chang, Heng-Chih; Chan, Rai-Chi; Wei, Shun-Hwa; Wang, Ray-Yau

2015-10-01

We investigated the effects of a computer-generated interactive visual feedback training program on the recovery from pusher syndrome in stroke patients. Assessor-blinded, pilot randomized controlled study. A total of 12 stroke patients with pusher syndrome were randomly assigned to either the experimental group (N = 7, computer-generated interactive visual feedback training) or control group (N = 5, mirror visual feedback training). The scale for contraversive pushing for severity of pusher syndrome, the Berg Balance Scale for balance performance, and the Fugl-Meyer assessment scale for motor control were the outcome measures. Patients were assessed pre- and posttraining. A comparison of pre- and posttraining assessment results revealed that both training programs led to the following significant changes: decreased severity of pusher syndrome scores (decreases of 4.0 ± 1.1 and 1.4 ± 1.0 in the experimental and control groups, respectively); improved balance scores (increases of 14.7 ± 4.3 and 7.2 ± 1.6 in the experimental and control groups, respectively); and higher scores for lower extremity motor control (increases of 8.4 ± 2.2 and 5.6 ± 3.3 in the experimental and control groups, respectively). Furthermore, the computer-generated interactive visual feedback training program produced significantly better outcomes in the improvement of pusher syndrome (p < 0.01) and balance (p < 0.05) compared with the mirror visual feedback training program. Although both training programs were beneficial, the computer-generated interactive visual feedback training program more effectively aided recovery from pusher syndrome compared with mirror visual feedback training. © The Author(s) 2014.

Regression and statistical shape model based substitute CT generation for MRI alone external beam radiation therapy from standard clinical MRI sequences.

PubMed

Ghose, Soumya; Greer, Peter B; Sun, Jidi; Pichler, Peter; Rivest-Henault, David; Mitra, Jhimli; Richardson, Haylea; Wratten, Chris; Martin, Jarad; Arm, Jameen; Best, Leah; Dowling, Jason A

2017-10-27

In MR only radiation therapy planning, generation of the tissue specific HU map directly from the MRI would eliminate the need of CT image acquisition and may improve radiation therapy planning. The aim of this work is to generate and validate substitute CT (sCT) scans generated from standard T2 weighted MR pelvic scans in prostate radiation therapy dose planning. A Siemens Skyra 3T MRI scanner with laser bridge, flat couch and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole pelvis MRI (1.6 mm 3D isotropic T2w SPACE sequence) was acquired. Patients received a routine planning CT scan. Co-registered whole pelvis CT and T2w MRI pairs were used as training images. Advanced tissue specific non-linear regression models to predict HU for the fat, muscle, bladder and air were created from co-registered CT-MRI image pairs. On a test case T2w MRI, the bones and bladder were automatically segmented using a novel statistical shape and appearance model, while other soft tissues were separated using an Expectation-Maximization based clustering model. The CT bone in the training database that was most 'similar' to the segmented bone was then transformed with deformable registration to create the sCT component of the test case T2w MRI bone tissue. Predictions for the bone, air and soft tissue from the separate regression models were successively combined to generate a whole pelvis sCT. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same IMRT dose plan was found to be [Formula: see text] (mean  ±  standard deviation) for 39 patients. The 3D Gamma pass rate was [Formula: see text] (2 mm/2%). The novel hybrid model is computationally efficient, generating an sCT in 20 min from standard T2w images for prostate cancer radiation therapy dose planning and DRR generation.

Regression and statistical shape model based substitute CT generation for MRI alone external beam radiation therapy from standard clinical MRI sequences

NASA Astrophysics Data System (ADS)

Ghose, Soumya; Greer, Peter B.; Sun, Jidi; Pichler, Peter; Rivest-Henault, David; Mitra, Jhimli; Richardson, Haylea; Wratten, Chris; Martin, Jarad; Arm, Jameen; Best, Leah; Dowling, Jason A.

2017-11-01

In MR only radiation therapy planning, generation of the tissue specific HU map directly from the MRI would eliminate the need of CT image acquisition and may improve radiation therapy planning. The aim of this work is to generate and validate substitute CT (sCT) scans generated from standard T2 weighted MR pelvic scans in prostate radiation therapy dose planning. A Siemens Skyra 3T MRI scanner with laser bridge, flat couch and pelvic coil mounts was used to scan 39 patients scheduled for external beam radiation therapy for localized prostate cancer. For sCT generation a whole pelvis MRI (1.6 mm 3D isotropic T2w SPACE sequence) was acquired. Patients received a routine planning CT scan. Co-registered whole pelvis CT and T2w MRI pairs were used as training images. Advanced tissue specific non-linear regression models to predict HU for the fat, muscle, bladder and air were created from co-registered CT-MRI image pairs. On a test case T2w MRI, the bones and bladder were automatically segmented using a novel statistical shape and appearance model, while other soft tissues were separated using an Expectation-Maximization based clustering model. The CT bone in the training database that was most ‘similar’ to the segmented bone was then transformed with deformable registration to create the sCT component of the test case T2w MRI bone tissue. Predictions for the bone, air and soft tissue from the separate regression models were successively combined to generate a whole pelvis sCT. The change in monitor units between the sCT-based plans relative to the gold standard CT plan for the same IMRT dose plan was found to be 0.3%+/-0.9% (mean  ±  standard deviation) for 39 patients. The 3D Gamma pass rate was 99.8+/-0.00 (2 mm/2%). The novel hybrid model is computationally efficient, generating an sCT in 20 min from standard T2w images for prostate cancer radiation therapy dose planning and DRR generation.

Leader Developmental Readiness of Generation Y in the Training Industry

ERIC Educational Resources Information Center

Garrigue, Marie

2012-01-01

Members of Generation Y in the training and development industry will be required to assume leadership roles as Baby Boomers retire, yet little empirical research exists regarding how best to prepare them for leadership. The purpose of this study was to examine differences in leader developmental readiness between generational cohorts in the…

New Drive Train Concept with Multiple High Speed Generator

NASA Astrophysics Data System (ADS)

Barenhorst, F.; Serowy, S.; Andrei, C.; Schelenz, R.; Jacobs, G.; Hameyer, K.

2016-09-01

In the research project RapidWind (financed by the German Federal Ministry for Economic Affairs and Energy under Grant 0325642) an alternative 6 MW drive train configuration with six high-speed (n = 5000 rpm) permanent magnet synchronous generators for wind turbine generators (WTG) is designed. The gearbox for this drive train concept is assembled with a six fold power split spur gear stage in the first stage, followed by six individual 1 MW geared driven generators. Switchable couplings are developed to connect and disconnect individual geared generators depending on the input power. With this drive train configuration it is possible to improve the efficiency during partial load operation, increasing the energy yield about 1.15% for an exemplary low-wind site. The focus of this paper is the investigation of the dynamic behavior of this new WTG concept. Due to the high gear ratio the inertia relationship between rotor and generator differs from conventional WT concepts, possibly leading to intensified vibration behavior. Moreover there are switching procedures added, that might also lead to vibration issues.

Comparison of Computational-Model and Experimental-Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model-generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Comparison of Computational, Model and Experimental, Example Trained Neural Networks for Processing Speckled Fringe Patterns

NASA Technical Reports Server (NTRS)

Decker, A. J.; Fite, E. B.; Thorp, S. A.; Mehmed, O.

1998-01-01

The responses of artificial neural networks to experimental and model-generated inputs are compared for detection of damage in twisted fan blades using electronic holography. The training-set inputs, for this work, are experimentally generated characteristic patterns of the vibrating blades. The outputs are damage-flag indicators or second derivatives of the sensitivity-vector-projected displacement vectors from a finite element model. Artificial neural networks have been trained in the past with computational-model- generated training sets. This approach avoids the difficult inverse calculations traditionally used to compare interference fringes with the models. But the high modeling standards are hard to achieve, even with fan-blade finite-element models.

Generation of dark and bright spin wave envelope soliton trains through self-modulational instability in magnetic films.

PubMed

Wu, Mingzhong; Kalinikos, Boris A; Patton, Carl E

2004-10-08

The generation of dark spin wave envelope soliton trains from a continuous wave input signal due to spontaneous modulational instability has been observed for the first time. The dark soliton trains were formed from high dispersion dipole-exchange spin waves propagated in a thin yttrium iron garnet film with pinned surface spins at frequencies situated near the dipole gaps in the dipole-exchange spin wave spectrum. Dark and bright soliton trains were generated for one and the same film through placement of the input carrier frequency in regions of negative and positive dispersion, respectively. Two unreported effects in soliton dynamics, hysteresis and period doubling, were also observed.

The Effect of Training Data Set Composition on the Performance of a Neural Image Caption Generator

DTIC Science & Technology

2017-09-01

objects was compared using the Metric for Evaluation of Translation with Explicit Ordering (METEOR) and Consensus-Based Image Description Evaluation...using automated scoring systems. Many such systems exist, including Bilingual Evaluation Understudy (BLEU), Consensus-Based Image Description Evaluation...shown to be essential to automated scoring, which correlates highly with human precision.5 CIDEr uses a system of consensus among the captions and

Daughters of the Earth: Skills-Based Literacy Programme for Women, China. Education for All: Making It Work. Innovation Series.

ERIC Educational Resources Information Center

Aksornkool, Namtip

Begun in the early 1990s, The Skills-Based Literacy Programme for Women aims to improve the lives of rural Chinese women by linking literacy education with skills training in agriculture and other forms of income generation. Xuan Wei County, Yunnan Province, was chosen as the seat of the project because of high female illiteracy rates and the need…

Current Status and Future Prospects of Clinical Psycholog

PubMed Central

Baker, Timothy B.; McFall, Richard M.; Shoham, Varda

2010-01-01

SUMMARY The escalating costs of health care and other recent trends have made health care decisions of great societal import, with decision-making responsibility often being transferred from practitioners to health economists, health plans, and insurers. Health care decision making increasingly is guided by evidence that a treatment is efficacious, effective–disseminable, cost-effective, and scientifically plausible. Under these conditions of heightened cost concerns and institutional–economic decision making, psychologists are losing the opportunity to play a leadership role in mental and behavioral health care: Other types of practitioners are providing an increasing proportion of delivered treatment, and the use of psychiatric medication has increased dramatically relative to the provision of psychological interventions. Research has shown that numerous psychological interventions are efficacious, effective, and cost-effective. However, these interventions are used infrequently with patients who would benefit from them, in part because clinical psychologists have not made a convincing case for the use of these interventions (e.g., by supplying the data that decision makers need to support implementation of such interventions) and because clinical psychologists do not themselves use these interventions even when given the opportunity to do so. Clinical psychologists’ failure to achieve a more significant impact on clinical and public health may be traced to their deep ambivalence about the role of science and their lack of adequate science training, which leads them to value personal clinical experience over research evidence, use assessment practices that have dubious psychometric support, and not use the interventions for which there is the strongest evidence of efficacy. Clinical psychology resembles medicine at a point in its history when practitioners were operating in a largely prescientific manner. Prior to the scientific reform of medicine in the early 1900s, physicians typically shared the attitudes of many of today’s clinical psychologists, such as valuing personal experience over scientific research. Medicine was reformed, in large part, by a principled effort by the American Medical Association to increase the science base of medical school education. Substantial evidence shows that many clinical psychology doctoral training programs, especially PsyD and for-profit programs, do not uphold high standards for graduate admission, have high student–faculty ratios, deemphasize science in their training, and produce students who fail to apply or generate scientific knowledge. A promising strategy for improving the quality and clinical and public health impact of clinical psychology is through a new accreditation system that demands highquality science training as a central feature of doctoral training in clinical psychology. Just as strengthening training standards in medicine markedly enhanced the quality of health care, improved training standards in clinical psychology will enhance health and mental health care. Such a system will (a) allow the public and employers to identify scientifically trained psychologists; (b) stigmatize ascientific training programs and practitioners; (c) produce aspirational effects, thereby enhancing training quality generally; and (d) help accredited programs improve their training in the application and generation of science. These effects should enhance the generation, application, and dissemination of experimentally supported interventions, thereby improving clinical and public health. Experimentally based treatments not only are highly effective but also are cost-effective relative to other interventions; therefore, they could help control spiraling health care costs. The new Psychological Clinical Science Accreditation System (PCSAS) is intended to accredit clinical psychology training programs that offer highquality science-centered education and training, producing graduates who are successful in generating and applying scientific knowledge. Psychologists, universities, and other stakeholders should vigorously support this new accreditation system as the surest route to a scientifically principled clinical psychology that can powerfully benefit clinical and public health. PMID:20865146

Perceptions and experiences of nurse preceptors regarding their training courses: a mixed method study.

PubMed

Chang, Chia-Chi; Lin, Li-Min; Chen, I-Hui; Kang, Chun-Mei; Chang, Wen-Yin

2015-01-01

Although the benefits of preceptor training programs on the performance of nurse preceptors have been reported, research related to nurse preceptors' perceptions of and experiences with preceptor training courses is relatively limited. To explore nurse preceptors' perceptions of preceptor training courses and obtain information on their experiences in working as preceptors. A mixed method design was conducted. Nurse preceptors who currently work at one of eight hospitals in northern Taiwan were recruited to participate in this study. A questionnaire survey and focus group interviews were conducted. A training course perception scale was developed and generated based on the current nurse preceptor training programs offered in eight hospitals. Focus group interviews were conducted to obtain additional information on nurse preceptors' experiences in working as preceptors. The survey data were analyzed using descriptive statistics. Interview data were transcribed and analyzed using a qualitative content analysis approach. The results from the surveys of 386 nurse preceptors revealed that most courses included in the current preceptor training programs did not fulfill the learning needs of nurse preceptors and were clinically impractical. The most necessary and clinically useful course was the communication skills course, whereas the least useful course was the adult learning theory and principles course. Three themes were identified as problems based on the three focus group interviews conducted with 36 nurse preceptors: inadequate training was received before nurses were appointed as nurse preceptors, the courses were more theoretical rather than practical, and the preceptors experienced stress from multiple sources. The results revealed that the current preceptor training courses are impractical; therefore, the content of preceptor training courses must be altered to fulfill nurse preceptors' training needs. Furthermore, problems identified through the focus group interviews reinforce the survey results. Copyright © 2014 Elsevier Ltd. All rights reserved.

Polyspectral signal analysis techniques for condition based maintenance of helicopter drive-train system

NASA Astrophysics Data System (ADS)

Hassan Mohammed, Mohammed Ahmed

For an efficient maintenance of a diverse fleet of air- and rotorcraft, effective condition based maintenance (CBM) must be established based on rotating components monitored vibration signals. In this dissertation, we present theory and applications of polyspectral signal processing techniques for condition monitoring of critical components in the AH-64D helicopter tail rotor drive train system. Currently available vibration-monitoring tools are mostly built around auto- and cross-power spectral analysis which have limited performance in detecting frequency correlations higher than second order. Studying higher order correlations and their Fourier transforms, higher order spectra, provides more information about the vibration signals which helps in building more accurate diagnostic models of the mechanical system. Based on higher order spectral analysis, different signal processing techniques are developed to assess health conditions of different critical rotating-components in the AH-64D helicopter drive-train. Based on cross-bispectrum, quadratic nonlinear transfer function is presented to model second order nonlinearity in a drive-shaft running between the two hanger bearings. Then, quadratic-nonlinearity coupling coefficient between frequency harmonics of the rotating shaft is used as condition metric to study different seeded shaft faults compared to baseline case, namely: shaft misalignment, shaft imbalance, and combination of shaft misalignment and imbalance. The proposed quadratic-nonlinearity metric shows better capabilities in distinguishing the four studied shaft settings than the conventional linear coupling based on cross-power spectrum. We also develop a new concept of Quadratic-Nonlinearity Power-Index spectrum, QNLPI(f), that can be used in signal detection and classification, based on bicoherence spectrum. The proposed QNLPI(f) is derived as a projection of the three-dimensional bicoherence spectrum into two-dimensional spectrum that quantitatively describes how much of the mean square power at certain frequency f is generated due to nonlinear quadratic interaction between different frequency components. The proposed index, QNLPI(f), can be used to simplify the study of bispectrum and bicoherence signal spectra. It also inherits useful characteristics from the bicoherence such as high immunity to additive Gaussian noise, high capability of nonlinear-systems identifications, and amplification invariance. The quadratic-nonlinear power spectral density PQNL(f) and percentage of quadratic nonlinear power PQNLP are also introduced based on the QNLPI(f). Concept of the proposed indices and their computational considerations are discussed first using computer generated data, and then applied to real-world vibration data to assess health conditions of different rotating components in the drive train including drive-shaft, gearbox, and hanger bearing faults. The QNLPI(f) spectrum enables us to gain more details about nonlinear harmonic generation patterns that can be used to distinguish between different cases of mechanical faults, which in turn helps to gaining more diagnostic/prognostic capabilities.

Robot training of upper limb in multiple sclerosis: comparing protocols with or without manipulative task components.

PubMed

Carpinella, Ilaria; Cattaneo, Davide; Bertoni, Rita; Ferrarin, Maurizio

2012-05-01

In this pilot study, we compared two protocols for robot-based rehabilitation of upper limb in multiple sclerosis (MS): a protocol involving reaching tasks (RT) requiring arm transport only and a protocol requiring both objects' reaching and manipulation (RMT). Twenty-two MS subjects were assigned to RT or RMT group. Both protocols consisted of eight sessions. During RT training, subjects moved the handle of a planar robotic manipulandum toward circular targets displayed on a screen. RMT protocol required patients to reach and manipulate real objects, by moving the robotic arm equipped with a handle which left the hand free for distal tasks. In both trainings, the robot generated resistive and perturbing forces. Subjects were evaluated with clinical and instrumental tests. The results confirmed that MS patients maintained the ability to adapt to the robot-generated forces and that the rate of motor learning increased across sessions. Robot-therapy significantly reduced arm tremor and improved arm kinematics and functional ability. Compared to RT, RMT protocol induced a significantly larger improvement in movements involving grasp (improvement in Grasp ARAT sub-score: RMT 77.4%, RT 29.5%, p=0.035) but not precision grip. Future studies are needed to evaluate if longer trainings and the use of robotic handles would significantly improve also fine manipulation.

Margined winner-take-all: New learning rule for pattern recognition.

PubMed

Fukushima, Kunihiko

2018-01-01

The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer. Every time when a training pattern is presented during the learning, if the result of recognition by WTA (Winner-Take-All) is an error, a new cell is generated in the deepest layer. Here we put a certain amount of margin to the WTA. In other words, only during the learning, a certain amount of handicap is given to cells of classes other than that of the training vector, and the winner is chosen under this handicap. By introducing the margin to the WTA, we can generate a compact set of cells, with which a high recognition rate can be obtained with a small computational cost. The ability of this mWTA is demonstrated by computer simulation. Copyright © 2017 Elsevier Ltd. All rights reserved.

REVIEW ARTICLE: Harmonically mode-locked semiconductor-based lasers as high repetition rate ultralow noise pulse train and optical frequency comb sources

NASA Astrophysics Data System (ADS)

Quinlan, F.; Ozharar, S.; Gee, S.; Delfyett, P. J.

2009-10-01

Recent experimental work on semiconductor-based harmonically mode-locked lasers geared toward low noise applications is reviewed. Active, harmonic mode-locking of semiconductor-based lasers has proven to be an excellent way to generate 10 GHz repetition rate pulse trains with pulse-to-pulse timing jitter of only a few femtoseconds without requiring active feedback stabilization. This level of timing jitter is achieved in long fiberized ring cavities and relies upon such factors as low noise rf sources as mode-lockers, high optical power, intracavity dispersion management and intracavity phase modulation. When a high finesse etalon is placed within the optical cavity, semiconductor-based harmonically mode-locked lasers can be used as optical frequency comb sources with 10 GHz mode spacing. When active mode-locking is replaced with regenerative mode-locking, a completely self-contained comb source is created, referenced to the intracavity etalon.

From the Bottom Up: Toward a Strategy for Income and Employment Generation among the Disadvantaged. An Interim Report.

ERIC Educational Resources Information Center

O'Regan, Fred; Conway, Maureen

The Aspen Institute's ongoing action-research program, Local Employment Approaches for the Disadvantaged (LEAD), assessed 60 programs nationally. Local initiatives fell into four general categories, with numerous subcategories: self-employment, job training and placement, job creation and retention, and community-based finance. A second breakdown…

Mobile Learning 4ALL

ERIC Educational Resources Information Center

Correia, Secundino; Medeiros, Paula; Mendes, Mafalda; Silva, Margarida

2013-01-01

We are in an innovation process for the development of a new generation of tools and resources for education and training throughout life, available in any platform, at anytime and place and in any language. The project TOPQX intends to congregate a set of theoretical and empirical resources that form a scientific base from which it will be…

Night Attack Workload Steering Group. Volume 3. Simulation and Human Factors Subgroup

DTIC Science & Technology

1982-06-01

information intepretation . The second is the use of pictorial formats or computer generated displays that combine many present-day displays into a small number...base exists in any form (digital, film , or model) which supports the wide area, long track, low level requirements levied by night attack training

Methodological Reflections: Supervisory Discourses and Practice-Based Learning

ERIC Educational Resources Information Center

Sarja, Anneli; Janhonen, Sirpa

2009-01-01

The concept of dialogue is often examined apart from the social and historical context in which it is embedded. This paper identifies how dialogue between a superior and a subordinate generates a reorganisation of situated knowledge in the education and training of nurse teachers. We created an analytic method of supervisory discourse founded on…

Training creative cognition: adolescence as a flexible period for improving creativity

PubMed Central

Stevenson, Claire E.; Kleibeuker, Sietske W.; de Dreu, Carsten K. W.; Crone, Eveline A.

2014-01-01

Creativity commonly refers to the ability to generate ideas, solutions, or insights that are novel yet feasible. The ability to generate creative ideas appears to develop and change from childhood to adulthood. Prior research, although inconsistent, generally indicates that adults perform better than adolescents on the alternative uses task (AUT), a commonly used index of creative ideation. The focus of this study was whether performance could be improved by practicing alternative uses generation. We examined the effectiveness of creative ideation training in adolescents (13–16 years, N = 71) and adults (23–30 years, N = 61). Participants followed one of three types of training, each comprising eight 20-min practice sessions within 2 week time: (1) alternative uses generation (experimental condition: creative ideation); (2) object characteristic generation (control condition: general ideation); (3) rule-switching (control condition: rule-switching). Progression in fluency, flexibility, originality of creative ideation was compared between age-groups and training conditions. Participants improved in creative ideation and cognitive flexibility, but not in general ideation. Participants in all three training conditions became better in fluency and originality on the AUT. With regard to originality, adolescents benefitted more from training than adults, although this was not specific for the creative ideation training condition. These results are interpreted in relation to (a) the different underlying processes targeted in the three conditions and (b) developmental differences in brain plasticity with increased sensitivity to training in adolescents. In sum, the results show that improvement can be made in creative ideation and supports the hypothesis that adolescence is a developmental stage of increased flexibility optimized for learning and explorative behavior. PMID:25400565

Critical race theory as a bridge in science training: the California State University, Northridge BUILD PODER program.

PubMed

Saetermoe, Carrie L; Chavira, Gabriela; Khachikian, Crist S; Boyns, David; Cabello, Beverly

2017-01-01

Unconscious bias and explicit forms of discrimination continue to pervade academic institutions. Multicultural and diversity training activities have not been sufficient in making structural and social changes leading to equity, therefore, a new form of critical consciousness is needed to train diverse scientists with new research questions, methods, and perspectives. The purpose of this paper is to describe Building Infrastructure Leading to Diversity (BUILD); Promoting Opportunities for Diversity in Education and Research (PODER), which is an undergraduate biomedical research training program based on transformative framework rooted in Critical Race Theory (CRT). By employing a CRT-informed curriculum and training in BUILD PODER, students are empowered not only to gain access but also to thrive in graduate programs and beyond. Poder means "power" or "to be able to" in Spanish. Essentially, we are "building power" using students' strengths and empowering them as learners. The new curriculum helps students understand institutional policies and practices that may prevent them from persisting in higher education, learn to become their own advocates, and successfully confront social barriers and instances of inequities and discrimination. To challenge these barriers and sustain campus changes in support of students, BUILD PODER works toward changing campus culture and research mentoring relationships. By joining with ongoing university structures such as the state university Graduation Initiative, we include CRT tenets into the campus dialogue and stimulate campus-wide discussions around institutional change. Strong ties with five community college partners also enrich BUILD PODER's student body and strengthen mentor diversity. Preliminary evaluation data suggest that BUILD PODER's program has enhanced the racial/ethnic consciousness of the campus community, is effective in encouraging more egalitarian and respectful faculty-student relationships, and is a rigorous program of biomedical research training that supports students as they achieve their goals. Biomedical research programs may benefit from a reanalysis of the fit between current training programs and student strengths. By incorporating the voices of talented youth, drawing upon their native strengths, we will generate a new science that links biomedical research to community health and social justice, generating progress toward health equity through a promising new generation of scholars.

Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

PubMed Central

Morris, Melody K.; Saez-Rodriguez, Julio; Clarke, David C.; Sorger, Peter K.; Lauffenburger, Douglas A.

2011-01-01

Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone. PMID:21408212

Projects, Training, and Strategies for Generating Income: A Selected Annotated Bibliography. Annotated Bibliography #4.

ERIC Educational Resources Information Center

Michigan State Univ., East Lansing. Non-Formal Education Information Center.

A selected annotated bibliography on projects, training, and strategies for generating income, intended for persons actively engaged in non-formal education for development, reflects a growing number of projects on income generation by and for women's groups, and a reliance upon indigenous associations and group action. Documents dating from 1969…

Exploring the bases for a mixed reality stroke rehabilitation system, Part II: design of interactive feedback for upper limb rehabilitation.

PubMed

Lehrer, Nicole; Chen, Yinpeng; Duff, Margaret; L Wolf, Steven; Rikakis, Thanassis

2011-09-08

Few existing interactive rehabilitation systems can effectively communicate multiple aspects of movement performance simultaneously, in a manner that appropriately adapts across various training scenarios. In order to address the need for such systems within stroke rehabilitation training, a unified approach for designing interactive systems for upper limb rehabilitation of stroke survivors has been developed and applied for the implementation of an Adaptive Mixed Reality Rehabilitation (AMRR) System. The AMRR system provides computational evaluation and multimedia feedback for the upper limb rehabilitation of stroke survivors. A participant's movements are tracked by motion capture technology and evaluated by computational means. The resulting data are used to generate interactive media-based feedback that communicates to the participant detailed, intuitive evaluations of his performance. This article describes how the AMRR system's interactive feedback is designed to address specific movement challenges faced by stroke survivors. Multimedia examples are provided to illustrate each feedback component. Supportive data are provided for three participants of varying impairment levels to demonstrate the system's ability to train both targeted and integrated aspects of movement. The AMRR system supports training of multiple movement aspects together or in isolation, within adaptable sequences, through cohesive feedback that is based on formalized compositional design principles. From preliminary analysis of the data, we infer that the system's ability to train multiple foci together or in isolation in adaptable sequences, utilizing appropriately designed feedback, can lead to functional improvement. The evaluation and feedback frameworks established within the AMRR system will be applied to the development of a novel home-based system to provide an engaging yet low-cost extension of training for longer periods of time.

Exploring the bases for a mixed reality stroke rehabilitation system, Part II: Design of Interactive Feedback for upper limb rehabilitation

PubMed Central

2011-01-01

Background Few existing interactive rehabilitation systems can effectively communicate multiple aspects of movement performance simultaneously, in a manner that appropriately adapts across various training scenarios. In order to address the need for such systems within stroke rehabilitation training, a unified approach for designing interactive systems for upper limb rehabilitation of stroke survivors has been developed and applied for the implementation of an Adaptive Mixed Reality Rehabilitation (AMRR) System. Results The AMRR system provides computational evaluation and multimedia feedback for the upper limb rehabilitation of stroke survivors. A participant's movements are tracked by motion capture technology and evaluated by computational means. The resulting data are used to generate interactive media-based feedback that communicates to the participant detailed, intuitive evaluations of his performance. This article describes how the AMRR system's interactive feedback is designed to address specific movement challenges faced by stroke survivors. Multimedia examples are provided to illustrate each feedback component. Supportive data are provided for three participants of varying impairment levels to demonstrate the system's ability to train both targeted and integrated aspects of movement. Conclusions The AMRR system supports training of multiple movement aspects together or in isolation, within adaptable sequences, through cohesive feedback that is based on formalized compositional design principles. From preliminary analysis of the data, we infer that the system's ability to train multiple foci together or in isolation in adaptable sequences, utilizing appropriately designed feedback, can lead to functional improvement. The evaluation and feedback frameworks established within the AMRR system will be applied to the development of a novel home-based system to provide an engaging yet low-cost extension of training for longer periods of time. PMID:21899779

The Multidisciplinary Translational Team (MTT) Model for Training and Development of Translational Research Investigators

PubMed Central

Hellmich, Mark R.; Cestone, Christina M.; Wooten, Kevin C.; Ottenbacher, Kenneth J.; Chonmaitree, Tasnee; Anderson, Karl E.; Brasier, Allan R.

2015-01-01

ABSTRACT Multiinstitutional research collaborations now form the most rapid and productive project execution structures in the health sciences. Effective adoption of a multidisciplinary team research approach is widely accepted as one mechanism enabling rapid translation of new discoveries into interventions in human health. Although the impact of successful team‐based approaches facilitating innovation has been well‐documented, its utility for training a new generation of scientists has not been thoroughly investigated. We describe the characteristics of how multidisciplinary translational teams (MTTs) promote career development of translational research scholars through competency building, interprofessional integration, and team‐based mentoring approaches. Exploratory longitudinal and outcome assessments from our experience show that MTT membership had a positive effect on the development of translational research competencies, as determined by a self‐report survey of 32 scholars. We also observed that all trainees produced a large number of collaborative publications that appeared to be associated with their CTSA association and participation with MTTs. We conclude that the MTT model provides a unique training environment for translational and team‐based learning activities, for investigators at early stages of career development. PMID:26010046

Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space.

PubMed

Miyao, Tomoyuki; Funatsu, Kimito

2017-08-01

When chemical structures are searched based on descriptor values, or descriptors are interpreted based on values, it is important that corresponding chemical structures actually exist. In order to consider the existence of chemical structures located in a specific region in the chemical space, we propose to search them inside training data domains (TDDs), which are dense areas of a training dataset in the chemical space. We investigated TDDs' features using diverse and local datasets, assuming that GDB11 is the chemical universe. These two analyses showed that considering TDDs gives higher chance of finding chemical structures than a random search-based method, and that novel chemical structures actually exist inside TDDs. In addition to those findings, we tested the hypothesis that chemical structures were distributed on the limited areas of chemical space. This hypothesis was confirmed by the fact that distances among chemical structures in several descriptor spaces were much shorter than those among randomly generated coordinates in the training data range. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Meeting the Needs of Underserved Patients in Western Kenya by Creating the Next Generation of Global Health Pharmacists.

PubMed

Miller, Monica L; Karwa, Rakhi; Schellhase, Ellen M; Pastakia, Sonak D; Crowe, Susie; Manji, Imran; Jakait, Beatrice; Maina, Mercy

2016-03-25

Objective. To describe a novel training model used to create a sustainable public health-focused pharmacy residency based in Kenya and to describe the outcomes of this training program on underserved populations. Design. The postgraduate year 2 residency was designed to expose trainees to the unique public health facets of inpatient, outpatient, and community-based care delivery in low and middle-income countries. Public health areas of focus included supply chain management, reproductive health, pediatrics, HIV, chronic disease management, and teaching. Assessment. The outcomes of the residency were assessed based on the number of new clinical programs developed by residents, articles and abstracts written by residents, and resident participation in grant writing. To date, six residents from the United States and eight Kenyan residents have completed the residency. Eleven sustainable patient care services have been implemented as a result of the residency program. Conclusion. This pharmacy residency training model developed accomplished pharmacists in public health pharmacy, with each residency class expanding funding and clinical programming, contributing to curriculum development, and creating jobs.

Applied Epidemiology and Public Health: Are We Training the Future Generations Appropriately?

PubMed Central

Brownson, Ross C.; Samet, Jonathan M.; Bensyl, Diana M.

2017-01-01

To extend the reach and relevance of epidemiology for public health practice, the science needs be broadened beyond etiologic research, to link more strongly with emerging technologies and to acknowledge key societal transformations. This new focus for epidemiology and its implications for epidemiologic training can be considered in the context of macro trends affecting society, including a greater focus on upstream causes of disease, shifting demographics, the Affordable Care Act and health care system reform, globalization, changing health communication environment, growing centrality of team and transdisciplinary science, emergence of translational sciences, greater focus on accountability, big data, informatics, high-throughput technologies (“omics”), privacy changes, and the evolving funding environment. This commentary describes existing approaches to and competencies for training in epidemiology, maps macro trends with competencies, highlights an example of competency-based education in the Epidemic Intelligence Service of Centers for Disease Control and Prevention, and suggests expanded and more dynamic training approaches. A re-examination of current approaches to epidemiologic training is needed. PMID:28038933

Real-time individualized training vectors for experiential learning.

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

Willis, Matt; Tucker, Eilish Marie; Raybourn, Elaine Marie

2011-01-01

Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD)more » project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.« less

Applied epidemiology and public health: are we training the future generations appropriately?

PubMed

Brownson, Ross C; Samet, Jonathan M; Bensyl, Diana M

2017-02-01

To extend the reach and relevance of epidemiology for public health practice, the science needs be broadened beyond etiologic research, to link more strongly with emerging technologies and to acknowledge key societal transformations. This new focus for epidemiology and its implications for epidemiologic training can be considered in the context of macro trends affecting society, including a greater focus on upstream causes of disease, shifting demographics, the Affordable Care Act and health care system reform, globalization, changing health communication environment, growing centrality of team and transdisciplinary science, emergence of translational sciences, greater focus on accountability, big data, informatics, high-throughput technologies ("omics"), privacy changes, and the evolving funding environment. This commentary describes existing approaches to and competencies for training in epidemiology, maps macro trends with competencies, highlights an example of competency-based education in the Epidemic Intelligence Service of Centers for Disease Control and Prevention, and suggests expanded and more dynamic training approaches. A reexamination of current approaches to epidemiologic training is needed. Copyright © 2016 Elsevier Inc. All rights reserved.

Preparing tomorrow's behavioral medicine scientists and practitioners: a survey of future directions for education and training.

PubMed

Goldstein, Carly M; Minges, Karl E; Schoffman, Danielle E; Cases, Mallory G

2017-02-01

Behavioral medicine training is due for an overhaul given the rapid evolution of the field, including a tight funding climate, changing job prospects, and new research and industry collaborations. The purpose of the present study was to collect responses from trainee and practicing members of a multidisciplinary professional society about their perceptions of behavioral medicine training and their suggestions for changes to training for future behavioral medicine scientists and practitioners. A total of 162 faculty and 110 students (total n = 272) completed a web-based survey on strengths of their current training programs and ideas for changes. Using a mixed-methods approach, the survey findings are used to highlight seven key areas for improved preparation of the next generation of behavioral medicine scientists and practitioners, which are grant writing, interdisciplinary teamwork, advanced statistics and methods, evolving research program, publishable products from coursework, evolution and use of theory, and non-traditional career paths.

System diagnostic builder: a rule-generation tool for expert systems that do intelligent data evaluation

NASA Astrophysics Data System (ADS)

Nieten, Joseph L.; Burke, Roger

1993-03-01

The system diagnostic builder (SDB) is an automated knowledge acquisition tool using state- of-the-art artificial intelligence (AI) technologies. The SDB uses an inductive machine learning technique to generate rules from data sets that are classified by a subject matter expert (SME). Thus, data is captured from the subject system, classified by an expert, and used to drive the rule generation process. These rule-bases are used to represent the observable behavior of the subject system, and to represent knowledge about this system. The rule-bases can be used in any knowledge based system which monitors or controls a physical system or simulation. The SDB has demonstrated the utility of using inductive machine learning technology to generate reliable knowledge bases. In fact, we have discovered that the knowledge captured by the SDB can be used in any number of applications. For example, the knowledge bases captured from the SMS can be used as black box simulations by intelligent computer aided training devices. We can also use the SDB to construct knowledge bases for the process control industry, such as chemical production, or oil and gas production. These knowledge bases can be used in automated advisory systems to ensure safety, productivity, and consistency.

The Evaluation of Two CDU Concepts and Their Effects on FMS Training

NASA Technical Reports Server (NTRS)

Abbott, Terence S.

1995-01-01

One of the biggest challenges for a pilot in the transition to a "glass" cockpit is understanding the Flight Management System (FMS). This is due to both the complex nature of the FMS and to the pilot-FMS interface. For these reasons, a large portion of transition training is devoted to the FMS. The intent of the current study was to examine the impact of the primary pilot-FMS interface, the Control Display Unit (CDU), on FMS training. The hypothesis of this study was that the interface design could have a significant impact on training. An FMS simulation was developed with two separate interfaces. One interface was similar to a current-generation design and the other was a multi-windows CDU based on graphical user interface techniques. For both application and evaluation reasons, constraints were applied to the graphical CDU design to maintain as much similarity as possible with the conventional CDU.

Overview of graduate training program of John Adams Institute for Accelerator Science

NASA Astrophysics Data System (ADS)

Seryi, Andrei

The John Adams Institute for Accelerator Science is a center of excellence in the UK for advanced and novel accelerator technology, providing expertise, research, development and training in accelerator techniques, and promoting advanced accelerator applications in science and society. We work in JAI on design of novel light sources upgrades of 3-rd generation and novel FELs, on plasma acceleration and its application to industrial and medical fields, on novel energy recovery compact linacs and advanced beam diagnostics, and many other projects. The JAI is based on three universities - University of Oxford, Imperial College London and Royal Holloway University of London. Every year 6 to 10 accelerators science experts, trained via research on cutting edge projects, defend their PhD thesis in JAI partner universities. In this presentation we will overview the research and in particular the highly successful graduate training program in JAI.

SU-F-T-447: The Impact of Treatment Planning Methods On RapidPlan Modeling for Rectum Cancer

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

Lu, S; Peng, J; Li, K

2016-06-15

Purpose: To investigate the dose volume histogram (DVH) prediction varieties based on intensity modulate radiotherapy (IMRT) plan or volume arc modulate radiotherapy (VMAT) plan models on the RapidPlan. Methods: Two DVH prediction models were generated in this study, including an IMRT model trained from 83 IMRT rectum plans and a VMAT model trained from 60 VMAT rectum plans. In the internal validation, 20 plans from each training database were selected to verify the clinical feasibility of the model. Then, 10 IMRT plans (PIMRT-by-IMRT-model) generated from IMRT model and 10 IMRT plans generated from VMAT model (PIMRT-by-VMAT-model) were compared on themore » dose to organs at risk (OAR), which included bladder, left and right femoral heads. The similar comparison was also performed on the VMAT plans generated from IMRT model (PVMAT-by-IMRT-model) and VMAT plans generated from VMAT (PVMAT-by-VMAT-model) model. Results: For the internal validation, all plans from IMRT or VMAT model shows significantly improvement on OAR sparing compared with the corresponded clinical ones. Compared to the PIMRT-by-VMAT-model, the PIMRT-by-IMRT-model has a reduction of 6.90±3.87%(p<0.001) on V40 6.63±3.62%(p<0.001) on V45 and 4.74±2.26%(p<0.001) on V50 in bladder; and a mean dose reduction of 2.12±1.75Gy(p=0.004) and 2.84±1.53Gy(p<0.001) in right and left femoral head, respectively. There was no significant difference on OAR sparing between PVMAT-by-IMRT-model and PVMAT-by-VMAT-model. Conclusion: The IMRT model for the rectal cancer in the RapidPlan can be applied to for VMAT planning. However, the VMAT model is not suggested to use in the IMRT planning. Cautions should be taken that the planning model based on some technique may not feasible to other planning techniques.« less

Vibrations of Railroad Due to The Passage of The Underground Train

NASA Astrophysics Data System (ADS)

Konowrocki, Robert; Bajer, Czesław

2010-03-01

In the paper we present results of vibration measurements in the train and on the base of the railroad in tunnels of Warsaw Underground. Measurements were performed at straight and curved sections of the track. The paper is focused on the influence of the lateral slip in rail/wheel contact zone on the generation of vibrations and a noise. Vibrations were analyzed in terms of accelerations, velocities or displacements as a function of time and frequency. Results ware compared with the experiment of rolling of the wheel with lateral sleep. In both cases we observed double periodic oscillations.

Credit WCT. Photographic copy of photograph, view of Test Stand ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Credit WCT. Photographic copy of photograph, view of Test Stand "D" from the south with tower ejector system in operation during a 1972 engine test. Note steam evolving from Z-stage ejectors atop the interstage condenser in the tower. Note also the "Hyprox" steam generator straddling the Dd ejector train to the right. The new Dy horizontal train has not been erected as of this date. In the distance is Test Stand "E." (JPL negative no. 384-9766-AC, 28 November 1972) - Jet Propulsion Laboratory Edwards Facility, Test Stand D, Edwards Air Force Base, Boron, Kern County, CA

Optical path switching based differential absorption radiometry for substance detection

NASA Technical Reports Server (NTRS)

Sachse, Glen W. (Inventor)

2005-01-01

An optical path switch divides sample path radiation into a time series of alternating first polarized components and second polarized components. The first polarized components are transmitted along a first optical path and the second polarized components along a second optical path. A first gasless optical filter train filters the first polarized components to isolate at least a first wavelength band thereby generating first filtered radiation. A second gasless optical filter train filters the second polarized components to isolate at least a second wavelength band thereby generating second filtered radiation. A beam combiner combines the first and second filtered radiation to form a combined beam of radiation. A detector is disposed to monitor magnitude of at least a portion of the combined beam alternately at the first wavelength band and the second wavelength band as an indication of the concentration of the substance in the sample path.

Optical path switching based differential absorption radiometry for substance detection

NASA Technical Reports Server (NTRS)

Sachse, Glen W. (Inventor)

2003-01-01

An optical path switch divides sample path radiation into a time series of alternating first polarized components and second polarized components. The first polarized components are transmitted along a first optical path and the second polarized components along a second optical path. A first gasless optical filter train filters the first polarized components to isolate at least a first wavelength band thereby generating first filtered radiation. A second gasless optical filter train filters the second polarized components to isolate at least a second wavelength band thereby generating second filtered radiation. A beam combiner combines the first and second filtered radiation to form a combined beam of radiation. A detector is disposed to monitor magnitude of at least a portion of the combined beam alternately at the first wavelength band and the second wavelength band as an indication of the concentration of the substance in the sample path.

Alchemical and structural distribution based representation for universal quantum machine learning

NASA Astrophysics Data System (ADS)

Faber, Felix A.; Christensen, Anders S.; Huang, Bing; von Lilienfeld, O. Anatole

2018-06-01

We introduce a representation of any atom in any chemical environment for the automatized generation of universal kernel ridge regression-based quantum machine learning (QML) models of electronic properties, trained throughout chemical compound space. The representation is based on Gaussian distribution functions, scaled by power laws and explicitly accounting for structural as well as elemental degrees of freedom. The elemental components help us to lower the QML model's learning curve, and, through interpolation across the periodic table, even enable "alchemical extrapolation" to covalent bonding between elements not part of training. This point is demonstrated for the prediction of covalent binding in single, double, and triple bonds among main-group elements as well as for atomization energies in organic molecules. We present numerical evidence that resulting QML energy models, after training on a few thousand random training instances, reach chemical accuracy for out-of-sample compounds. Compound datasets studied include thousands of structurally and compositionally diverse organic molecules, non-covalently bonded protein side-chains, (H2O)40-clusters, and crystalline solids. Learning curves for QML models also indicate competitive predictive power for various other electronic ground state properties of organic molecules, calculated with hybrid density functional theory, including polarizability, heat-capacity, HOMO-LUMO eigenvalues and gap, zero point vibrational energy, dipole moment, and highest vibrational fundamental frequency.

General purpose architecture for intelligent computer-aided training

NASA Technical Reports Server (NTRS)

Loftin, R. Bowen (Inventor); Wang, Lui (Inventor); Baffes, Paul T. (Inventor); Hua, Grace C. (Inventor)

1994-01-01

An intelligent computer-aided training system having a general modular architecture is provided for use in a wide variety of training tasks and environments. It is comprised of a user interface which permits the trainee to access the same information available in the task environment and serves as a means for the trainee to assert actions to the system; a domain expert which is sufficiently intelligent to use the same information available to the trainee and carry out the task assigned to the trainee; a training session manager for examining the assertions made by the domain expert and by the trainee for evaluating such trainee assertions and providing guidance to the trainee which are appropriate to his acquired skill level; a trainee model which contains a history of the trainee interactions with the system together with summary evaluative data; an intelligent training scenario generator for designing increasingly complex training exercises based on the current skill level contained in the trainee model and on any weaknesses or deficiencies that the trainee has exhibited in previous interactions; and a blackboard that provides a common fact base for communication between the other components of the system. Preferably, the domain expert contains a list of 'mal-rules' which typifies errors that are usually made by novice trainees. Also preferably, the training session manager comprises an intelligent error detection means and an intelligent error handling means. The present invention utilizes a rule-based language having a control structure whereby a specific message passing protocol is utilized with respect to tasks which are procedural or step-by-step in structure. The rules can be activated by the trainee in any order to reach the solution by any valid or correct path.

Learning the 3-D structure of objects from 2-D views depends on shape, not format

PubMed Central

Tian, Moqian; Yamins, Daniel; Grill-Spector, Kalanit

2016-01-01

Humans can learn to recognize new objects just from observing example views. However, it is unknown what structural information enables this learning. To address this question, we manipulated the amount of structural information given to subjects during unsupervised learning by varying the format of the trained views. We then tested how format affected participants' ability to discriminate similar objects across views that were rotated 90° apart. We found that, after training, participants' performance increased and generalized to new views in the same format. Surprisingly, the improvement was similar across line drawings, shape from shading, and shape from shading + stereo even though the latter two formats provide richer depth information compared to line drawings. In contrast, participants' improvement was significantly lower when training used silhouettes, suggesting that silhouettes do not have enough information to generate a robust 3-D structure. To test whether the learned object representations were format-specific or format-invariant, we examined if learning novel objects from example views transfers across formats. We found that learning objects from example line drawings transferred to shape from shading and vice versa. These results have important implications for theories of object recognition because they suggest that (a) learning the 3-D structure of objects does not require rich structural cues during training as long as shape information of internal and external features is provided and (b) learning generates shape-based object representations independent of the training format. PMID:27153196

Impact of a web based interactive simulation game (PULSE) on nursing students' experience and performance in life support training--a pilot study.

PubMed

Cook, Neal F; McAloon, Toni; O'Neill, Philip; Beggs, Richard

2012-08-01

The delivery of effective life support measures is highly associated with the quality, design and implementation of the education that underpins it. Effectively responding to a critical event is a requirement for all nurses illustrating the need for effective educational approaches from pre-registration training through to enhancing and maintaining life support skills after qualification. This paper reports the findings of utilising a web-based multimedia simulation game PULSE (Platform for Undergraduate Life Support Education). The platform was developed to enhance the student experience of life support education, to motivate on-going learning and engagement and to improve psychomotor skills associated with the provision of Intermediate Life Support (ILS) training. Pre training participants played PULSE and during life support training data was collected from an intervention and a control group of final year undergraduate nursing students (N=34). Quantitative analysis of performance took place and qualitative data was generated from a questionnaire assessing the learning experience. A statistically significant difference was found between the competence the groups displayed in the three skills sets of checking equipment, airway assessment and the safe/effective use of defibrillator at ILS level, and PULSE was positively evaluated as an educational tool when used alongside traditional life support training. Copyright © 2011 Elsevier Ltd. All rights reserved.

An Emotional ANN (EANN) approach to modeling rainfall-runoff process

NASA Astrophysics Data System (ADS)

Nourani, Vahid

2017-01-01

This paper presents the first hydrological implementation of Emotional Artificial Neural Network (EANN), as a new generation of Artificial Intelligence-based models for daily rainfall-runoff (r-r) modeling of the watersheds. Inspired by neurophysiological form of brain, in addition to conventional weights and bias, an EANN includes simulated emotional parameters aimed at improving the network learning process. EANN trained by a modified version of back-propagation (BP) algorithm was applied to single and multi-step-ahead runoff forecasting of two watersheds with two distinct climatic conditions. Also to evaluate the ability of EANN trained by smaller training data set, three data division strategies with different number of training samples were considered for the training purpose. The overall comparison of the obtained results of the r-r modeling indicates that the EANN could outperform the conventional feed forward neural network (FFNN) model up to 13% and 34% in terms of training and verification efficiency criteria, respectively. The superiority of EANN over classic ANN is due to its ability to recognize and distinguish dry (rainless days) and wet (rainy days) situations using hormonal parameters of the artificial emotional system.

Obstetrics and gynaecology training in Europe needs a next step.

PubMed

Scheele, Fedde; Novak, Ziva; Vetter, Klaus; Caccia, Nicolette; Goverde, Angelique

2014-09-01

Changing societal demands on doctors necessitate changes in the training of gynaecologists. Adapting this training will need well-thought-out and comprehensive planning that addresses the needs of the major stakeholders: society, patients, and doctors themselves. Doctors need to be cognizant of societal issues such as rapidly rising healthcare costs and budgetary crises, and be able to participate in the solutions. This demands effective medical leadership, which has been a neglected area in postgraduate training. It has become increasingly evident that a holistic view of the patient rooted in proper teamwork and systems-based practice is essential to provide patient-centered care. Specialists need to expand their skill set to participate in this kind of care. Furthermore, the feminisation of the medical profession and a new generation of doctors rejecting the constraints of the traditional model of medical care introduce new professional perspectives. This manuscript briefly reviews the challenges faced in the training of European gynaecologists in an effort to provoke discussion about how to best train the gynaecologists of the future. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The Relationship of Training in Self-Generated Questioning with Passage Difficulty and Immediate and Delayed Retention.

ERIC Educational Resources Information Center

Balajthy, Ernest

A study was conducted to determine if covert reader-generation of interspersed prequestions would affect recall of science-oriented prose. Sixty college freshmen in a basic skills reading course were divided into three groups: Group I received 5 hours of training and practive in the construction of self-generated questions, including recognition…

Fine-Tuning Neural Patient Question Retrieval Model with Generative Adversarial Networks.

PubMed

Tang, Guoyu; Ni, Yuan; Wang, Keqiang; Yong, Qin

2018-01-01

The online patient question and answering (Q&A) system attracts an increasing amount of users in China. Patient will post their questions and wait for doctors' response. To avoid the lag time involved with the waiting and to reduce the workload on the doctors, a better method is to automatically retrieve the semantically equivalent question from the archive. We present a Generative Adversarial Networks (GAN) based approach to automatically retrieve patient question. We apply supervised deep learning based approaches to determine the similarity between patient questions. Then a GAN framework is used to fine-tune the pre-trained deep learning models. The experiment results show that fine-tuning by GAN can improve the performance.

All-fiber tunable laser based on an acousto-optic tunable filter and a tapered fiber.

PubMed

Huang, Ligang; Song, Xiaobo; Chang, Pengfa; Peng, Weihua; Zhang, Wending; Gao, Feng; Bo, Fang; Zhang, Guoquan; Xu, Jingjun

2016-04-04

An all-fiber tunable laser was fabricated based on an acousto-optic tunable filter and a tapered fiber. The structure was of a high signal-to-noise ratio, therefore, no extra gain flattening was needed in the laser. In the experiment, the wavelength of the laser could be tuned from 1532.1 nm to 1570.4 nm with a 3-dB bandwidth of about 0.2 nm. Given enough nonlinearity in the laser cavity, it could also generate a sliding-frequency pulse train. The laser gains advantages of fast tuning and agility in pulse generation, and its simple structure is low cost for practical applications.

Machine learning enhanced optical distance sensor

NASA Astrophysics Data System (ADS)

Amin, M. Junaid; Riza, N. A.

2018-01-01

Presented for the first time is a machine learning enhanced optical distance sensor. The distance sensor is based on our previously demonstrated distance measurement technique that uses an Electronically Controlled Variable Focus Lens (ECVFL) with a laser source to illuminate a target plane with a controlled optical beam spot. This spot with varying spot sizes is viewed by an off-axis camera and the spot size data is processed to compute the distance. In particular, proposed and demonstrated in this paper is the use of a regularized polynomial regression based supervised machine learning algorithm to enhance the accuracy of the operational sensor. The algorithm uses the acquired features and corresponding labels that are the actual target distance values to train a machine learning model. The optimized training model is trained over a 1000 mm (or 1 m) experimental target distance range. Using the machine learning algorithm produces a training set and testing set distance measurement errors of <0.8 mm and <2.2 mm, respectively. The test measurement error is at least a factor of 4 improvement over our prior sensor demonstration without the use of machine learning. Applications for the proposed sensor include industrial scenario distance sensing where target material specific training models can be generated to realize low <1% measurement error distance measurements.

Generation of light and dark soliton trains in a dissipative four-wave mixing, mode-locked fibre ring laser

NASA Astrophysics Data System (ADS)

Zolotovskii, I. O.; Korobko, D. A.; Sysolyatin, A. A.

2018-02-01

We consider a model of a dissipative four-wave mixing, mode-locked fibre ring laser with an intracavity interferometer. The necessary conditions required for mode locking are presented. A pulse train generation is numerically simulated at different repetition rates and gain levels. Admissible ranges of values, for which successful mode locking is possible, are found. It is shown that in the case of normal dispersion of the resonator, a laser with an intracavity interferometer can generate a train of pulses with an energy much greater than that in the case of anomalous dispersion.

The Relationship between Training and Mental Health among Caregivers of Individuals with Polytrauma

PubMed Central

Pickett, Treven C.; Wilder Schaaf, Kathryn P.; Taylor, Brent C.; Gravely, Amy; Van Houtven, Courtney Harold; Friedemann-Sánchez, Greta; Griffin, Joan M.

2015-01-01

This was a hypothesis-generating exploration of relationships between caregiver training during TBI/polytrauma rehabilitation and caregiver mental health. In this cross-sectional study, 507 informal caregivers to US service members with TBI who received inpatient rehabilitation care in a Veterans Affairs' Polytrauma Rehabilitation Center from 2001 to 2009 completed a retrospective, self-report survey. Embedded in the survey were measures of caregiver mental health, including the National Institutes of Health's Patient Reported Outcome Measurement Information System (PROMIS) Anxiety and Depression Short Forms, the Rosenberg Self-Esteem scale, and the Zarit Burden Short Form. Though no groups endorsed clinical levels, mental health symptoms varied by caregiver training category (Trained, Not Trained, and Did Not Need Training). Caregivers who did not receive training on how to navigate healthcare systems endorsed higher depression and burden and lower self-esteem than those who did. Caregivers who did not receive training in supporting their care recipients' emotions endorsed higher anxiety, depression, and burden and lower self-esteem than those who did. Analyses also suggested a different association between training and mental health based on caregivers' relationship to the care recipient and the intensity of care recipient needs. Potential hypotheses for testing in future studies raised by these findings are discussed. PMID:26770015

Adaptive template generation for amyloid PET using a deep learning approach.

PubMed

Kang, Seung Kwan; Seo, Seongho; Shin, Seong A; Byun, Min Soo; Lee, Dong Young; Kim, Yu Kyeong; Lee, Dong Soo; Lee, Jae Sung

2018-05-11

Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this study, we applied deep neural networks to generate individually adaptive PET templates for robust and accurate SN of amyloid PET without using matched 3D MR images. Using 681 pairs of simultaneously acquired 11 C-PIB PET and T1-weighted 3D MRI scans of AD, MCI, and cognitively normal subjects, we trained and tested two deep neural networks [convolutional auto-encoder (CAE) and generative adversarial network (GAN)] that produce adaptive best PET templates. More specifically, the networks were trained using 685,100 pieces of augmented data generated by rotating 527 randomly selected datasets and validated using 154 datasets. The input to the supervised neural networks was the 3D PET volume in native space and the label was the spatially normalized 3D PET image using the transformation parameters obtained from MRI-based SN. The proposed deep learning approach significantly enhanced the quantitative accuracy of MRI-less amyloid PET assessment by reducing the SN error observed when an average amyloid PET template is used. Given an input image, the trained deep neural networks rapidly provide individually adaptive 3D PET templates without any discontinuity between the slices (in 0.02 s). As the proposed method does not require 3D MRI for the SN of PET images, it has great potential for use in routine analysis of amyloid PET images in clinical practice and research. © 2018 Wiley Periodicals, Inc.

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

PubMed

Song, Qi; Song, Yong-Duan

2011-12-01

This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only--the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations.

Creation and testing of an artificial neural network based carbonate detector for Mars rovers

NASA Technical Reports Server (NTRS)

Bornstein, Benjamin; Castano, Rebecca; Gilmore, Martha S.; Merrill, Matthew; Greenwood, James P.

2005-01-01

We have developed an artificial neural network (ANN) based carbonate detector capable of running on current and future rover hardware. The detector can identify calcite in visible/NIR (350-2500 nm) spectra of both laboratory specimens covered by ferric dust and rocks in Mars analogue field environments. The ANN was trained using the Backpropagation algorithm with sigmoid activation neurons. For the training dataset, we chose nine carbonate and eight non-carbonate representative mineral spectra from the USGS spectral library. Using these spectra as seeds, we generated 10,000 variants with up to 2% Gaussian noise in each reflectance measurement. We cross-validated several ANN architectures, training on 9,900 spectra and testing on the remaining 100. The best performing ANN correctly detected, with perfect accuracy, the presence (or absence) of carbonate in spectral data taken on field samples from the Mojave desert and clean, pure marbles from CT. Sensitivity experiments with JSC Mars-1 simulant dust suggest the carbonate detector would perform well in aeolian Martian environments.

A Review of Endoscopic Simulation: Current Evidence on Simulators and Curricula.

PubMed

King, Neil; Kunac, Anastasia; Merchant, Aziz M

2016-01-01

Upper and lower endoscopy is an important tool that is being utilized more frequently by general surgeons. Training in therapeutic endoscopic techniques has become a mandatory requirement for general surgery residency programs in the United States. The Fundamentals of Endoscopic Surgery has been developed to train and assess competency in these advanced techniques. Simulation has been shown to increase the skill and learning curve of trainees in other surgical disciplines. Several types of endoscopy simulators are commercially available; mechanical trainers, animal based, and virtual reality or computer-based simulators all have their benefits and limitations. However they have all been shown to improve trainee's endoscopic skills. Endoscopic simulators will play a critical role as part of a comprehensive curriculum designed to train the next generation of surgeons. We reviewed recent literature related to the various types of endoscopic simulators and their use in an educational curriculum, and discuss the relevant findings. Copyright © 2015 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Program review. Challenges and opportunities for training the next generation of biophysicists: perspectives of the directors of the Molecular Biophysics Training Program at Northwestern University.

PubMed

Neuhaus, Francis; Widom, Jonathan; MacDonald, Robert; Jardetzky, Theodore; Radhakrishnan, Ishwar

2008-04-01

Molecular biophysics is a broad, diverse, and dynamic field that has presented a variety of unique challenges and opportunities for training future generations of investigators. Having been or currently being intimately associated with the Molecular Biophysics Training Program at Northwestern, we present our perspectives on various issues that we have encountered over the years. We propose no cookie-cutter solutions, as there is no consensus on what constitutes the "ideal" program. However, there is uniformity in opinion on some key issues that might be useful to those interested in establishing a biophysics training program.

CGAT: a model for immersive personalized training in computational genomics

PubMed Central

Sims, David; Ponting, Chris P.

2016-01-01

How should the next generation of genomics scientists be trained while simultaneously pursuing high quality and diverse research? CGAT, the Computational Genomics Analysis and Training programme, was set up in 2010 by the UK Medical Research Council to complement its investment in next-generation sequencing capacity. CGAT was conceived around the twin goals of training future leaders in genome biology and medicine, and providing much needed capacity to UK science for analysing genome scale data sets. Here we outline the training programme employed by CGAT and describe how it dovetails with collaborative research projects to launch scientists on the road towards independent research careers in genomics. PMID:25981124

Final Technical Report - DE-EE0003542

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

Haley, James D

Wind has provided energy for thousands of years: some of the earliest windmill engineering designs date back to ancient Babylonia and India where wind would be used as a source of irrigation. Today, wind is the quickest growing resource in Americas expanding energy infrastructure. However, to continue to positively diversify Americas energy portfolio and further reduce the countrys reliance of foreign oil, the industry must grow substantially over the next two decades in both turbine installations and skilled industrial manpower to support. The wind sector is still an emergent industry requiring maturation and development of its labor force: dedicated trainingmore » is needed to provide the hard and soft skills to support the increasingly complex wind turbine generators as the technology evolves. Furthermore, the American workforce is facing a steep decline in available labor resources as the baby boomer generation enters retirement age. It is therefore vital that a process is quickly created for supporting the next generation of wind technicians. However, the manpower growth must incorporate three key components. First, the safety and technical training curriculum must be standardized across the industry - current wind educational programs are disparate and dedicated standardization programs must be further refined and implemented. Second, it is essential that the wind sector avoid disrupting other energy production industries by cannibalizing workers, which would indirectly affect the rest of Americas energy portfolio. The future wind workforce must be created organically utilizing either young people entering the workforce or train personnel emerging from careers outside of energy production. Third, the training must be quick and efficient as large amounts of wind turbines are being erected each year and this growth is expected to continue until at least 2035. One source that matches these three requirements is personnel transitioning from military service to the civilian sector. Utilizing the labor pool of transitioning military personnel and a dedicated training program specifically tailored to military hard and soft skills, the wind workforce can rapidly expand with highly skilled personnel. A tailored training program also provides career opportunities to an underutilized labor force as the personnel return from active military duty. This projects goal was to create a Wind Workforce Development Program that streamlines the wind technician training process using industry-leading safety programs and building on existing military experience. The approach used was to gather data from the wind industry, develop the curriculum and test the process to ensure it provides adequate training to equip the technicians as they transition from the military into wind. The platform for the curriculum development is called Personal Qualification Standards (PQS), which is based on the program of the same name from the United States Navy. Not only would the program provide multiple delivery methods of training (including classroom, computer-based training and on-the-job training), but it also is a familiar style of training to many military men and women. By incorporating a familiar method of training, it encourages active participation in the training and reduces the time for personnel to grasp the concept and flow of the training requirements. The program was tested for thoroughness, schedule and efficacy using a 5-person pilot phase during the last two years. The results of the training were a reduction in time to complete training and increased customer satisfaction on client project sites. However, there were obstacles that surfaced and required adaptation throughout the project including method of delivery, curriculum development and project schedules and are discussed in detail throughout the report. There are several key recommendations in the report that discuss additional training infrastructure, scalability within additional alternative energy markets and organizational certification through standardization committees.« less

Transurethral Resection of Bladder Tumors: Next-generation Virtual Reality Training for Surgeons.

PubMed

Neumann, Eva; Mayer, Julian; Russo, Giorgio Ivan; Amend, Bastian; Rausch, Steffen; Deininger, Susanne; Harland, Niklas; da Costa, Inês Anselmo; Hennenlotter, Jörg; Stenzl, Arnulf; Kruck, Stephan; Bedke, Jens

2018-05-22

The number of virtual reality (VR) simulators is increasing. The aim of this prospective trial was to determine the benefit of VR cystoscopy (UC) and transurethral bladder tumor resection (TURBT) training in students. Medical students without endoscopic experience (n=51, median age=25 yr, median 4th academic year) were prospectively randomized into groups A and B. After an initial VR-UC and VR-TURBT task, group A (n=25) underwent a video-based tutorial by a skilled expert. Group B (n=26) was trained using a VR training program (Uro-Trainer). Following the training, every participant performed a final VR-UC and VR-TURBT task. Performance indicators were recorded via the simulator. Data was analyzed by Mann-Whitney U test. VR cystoscopy and TURBT. No baseline and post-training differences were found for VR-UC between groups. During baseline, VR-TURBT group A showed higher inspected bladder surface than group B (56% vs 73%, p=0.03). Subgroup analysis detected differences related to sex before training (male: 31.2% decreased procedure time; 38.1% decreased resectoscope movement; p=0.02). After training, significant differences in procedure time (3.9min vs 2.7min, p=0.007), resectoscope movement (857mm vs 529mm, p=0.005), and accidental bladder injury (n=3.0 vs n=0.88, p=0.003) were found. Male participants showed reduced blood loss (males: 3.92ml vs females: 10.12ml; p=0.03) after training. Measuring endoscopic skills within a virtual environment can be done easily. Short training improved efficacy and safety of VR-TURBT. Nevertheless, transfer of improved VR performance into real world surgery needs further clarification. We investigated how students without endoscopic experience profit from simulation-based training. The safe environment and repeated simulations can improve the surgical training. It may be possible to enhance patient's safety and the training of surgeons in long term. Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Evaluating the effect of the Helping Mothers Survive Bleeding after Birth (HMS BAB) training in Tanzania and Uganda: study protocol for a randomised controlled trial.

PubMed

Hanson, Claudia; Pembe, Andrea B; Alwy, Fadhlun; Atuhairwe, Susan; Leshabari, Sebalda; Morris, Jessica; Kaharuza, Frank; Marrone, Gaetano

2017-07-06

Postpartum haemorrhage complicates approximately 10% of all deliveries and contributes to at least a quarter of all maternal deaths worldwide. The competency-based Helping Mothers Survive Bleeding after Birth (HMS BAB) training was developed to support evidence-based management of postpartum haemorrhage. This one-day training includes low-cost MamaNatalie® birthing simulators and addresses both prevention and first-line treatment of haemorrhage. While evidence is accumulating that the training improves health provider's knowledge, skills and confidence, evidence is missing as to whether this translates into improved practices and reduced maternal morbidity and mortality. This cluster-randomised trial aims to assess whether this training package - involving a one-day competency-based HMS BAB in-facility training provided by certified trainers followed by 8 weeks of in-service peer-based practice - has an effect on the occurrence of haemorrhage-related morbidity and mortality. In Tanzania and Uganda we randomise 20 and 18 districts (clusters) respectively, with half receiving the training intervention. We use unblinded matched-pair randomisation to balance district health system characteristics and the main outcome, which is in-facility severe morbidity due to haemorrhage defined by the World Health Organizationation-promoted disease and management-based near-miss criteria. Data are collected continuously in the intervention and comparison districts throughout the 6-month baseline and the 9-month intervention phase, which commences after the training intervention. Trained facility midwives or clinicians review severe maternal complications to identify near misses on a daily basis. They abstract the case information from case notes and enter it onto programmed tablets where it is uploaded. Intention-to-treat analysis will be used, taking the matched design into consideration using paired t test statistics to compare the outcomes between the intervention and comparison districts. We also assess the impact pathway from the effects of the training on the health provider's skills, care and interventions and health system readiness. This trial aims to generate evidence on the effect and limitations of this well-designed training package supported by birthing simulations. While the lack of blinding of participants and data collectors provides an inevitable limitation of this trial, the additional evaluation along the pathway of implementation will provide solid evidence on the effects of this HMS BAB training package. Pan African Clinical Trials Registry, PACTR201604001582128 . Registered on 12 April 2016.

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

A DMA-train for precision measurement of sub-10 nm aerosol dynamics

NASA Astrophysics Data System (ADS)

Stolzenburg, Dominik; Steiner, Gerhard; Winkler, Paul M.

2017-05-01

Measurements of aerosol dynamics in the sub-10 nm size range are crucially important for quantifying the impact of new particle formation onto the global budget of cloud condensation nuclei. Here we present the development and characterization of a differential mobility analyzer train (DMA-train), operating six DMAs in parallel for high-time-resolution particle-size-distribution measurements below 10 nm. The DMAs are operated at six different but fixed voltages and hence sizes, together with six state-of-the-art condensation particle counters (CPCs). Two Airmodus A10 particle size magnifiers (PSM) are used for channels below 2.5 nm while sizes above 2.5 nm are detected by TSI 3776 butanol-based or TSI 3788 water-based CPCs. We report the transfer functions and characteristics of six identical Grimm S-DMAs as well as the calibration of a butanol-based TSI model 3776 CPC, a water-based TSI model 3788 CPC and an Airmodus A10 PSM. We find cutoff diameters similar to those reported in the literature. The performance of the DMA-train is tested with a rapidly changing aerosol of a tungsten oxide particle generator during warmup. Additionally we report a measurement of new particle formation taken during a nucleation event in the CLOUD chamber experiment at CERN. We find that the DMA-train is able to bridge the gap between currently well-established measurement techniques in the cluster-particle transition regime, providing high time resolution and accurate size information of neutral and charged particles even at atmospheric particle concentrations.

Helpers program: A pilot test of brief tobacco intervention training in three corporations.

PubMed

Muramoto, Myra L; Wassum, Ken; Connolly, Tim; Matthews, Eva; Floden, Lysbeth

2010-03-01

Quitlines and worksite-sponsored cessation programs are effective and highly accessible, but limited by low utilization. Efforts to encourage use of cessation aids have focused almost exclusively on the smoker, overlooking the potential for friends, family, co-workers, and others in a tobacco user's social network to influence quitting and use of effective treatment. Longitudinal, observational pilot feasibility study with 6-week follow-up survey. Employees of three national corporations, with a combined target audience of 102,100 employees. The Helpers Program offers web-based, brief intervention training to activate social networks of tobacco users to encourage quitting and use of effective treatment. Helpers was offered from January 10 to March 31, 2008, as a treatment engagement strategy, together with Free & Clear's telephone/web-based cessation services. Website utilization, training completion, post-training changes in knowledge and self-efficacy with delivery of brief interventions, referrals to Free & Clear, and use of brief intervention training. There were 19,109 unique visitors to the Helpers website. Of these, 4727 created user accounts; 1427 registered for Helpers Training; 766 completed training. There were 445 visits to the referral page and 201 e-mail or letter referrals generated. There were 67 requests for technical support. Of follow-up survey respondents (n=289), 78.9% reported offering a brief intervention. Offering the Helpers Program website to a large, diverse audience as part of an employer-sponsored worksite health promotion program is both feasible and well accepted by employees. Website users will participate in training, encourage quitting, and refer smokers to quitline services. 2010. Published by Elsevier Inc.

Neural network error correction for solving coupled ordinary differential equations

NASA Technical Reports Server (NTRS)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Evaluating and Enhancing Driving Ability Among Teens with Autism Spectrum Disorder (ASD)

DTIC Science & Technology

2014-10-01

able to engage in the driving training, and none have experienced simulation adaptation syndrome. 15. SUBJECT TERMS Autism, Driving Safety , Driving...routine driving training (RT) required by the DMV, VRDS training + RT (VRDS-T) would lead to greater improvement in driving safety and less driving...improved driving safety above and beyond RT. We hypothesized that computer-generated feedback would be more palatable than human-generated feedback to

Anthropogenic and technogenic factors of operational risk at hazardous industrial objects of fuel-power complex

NASA Astrophysics Data System (ADS)

Magid, S. I.; Arkhipova, E. N.; Kulichikhin, V. V.; Zagretdinov, I. Sh.

2016-12-01

Technogenic and anthropogenic accidence at hazardous industrial objects (HIO) in the Russian Federation has been considered. The accidence level at HIO, including power plants and network enterprises, is determined by anthropogenic reasons, so-called "human factor", in 70% of all cases. The analysis of incidents caused by personnel has shown that errors occur most often during accidental situations, launches, holdups, routine switches, and other effects on equipment controls. It has been demonstrated that skills needed to perform type and routine switches can be learned, to certain limits, on real operating equipment, while combating emergency and accidental situations can be learned only with the help of modern training simulators developed based on information technologies. Problems arising during the following processes have been considered: development of mathematical and software support of modern training equipment associated, in one way or another, with adequate power-generating object modeling in accordance with human operator specifics; modeling and/or simulation of the corresponding control and management systems; organization of the education system (functional supply of the instructor, education and methodological resources (EMR)); organization of the program-technical, scalable and adaptable, platform for modeling of the main and secondary functions of the training simulator. It has been concluded that the systemic approach principle on the necessity and sufficiency in the applied methodology allows to reproduce all technological characteristics of the equipment, its topological completeness, as well as to achieve the acceptable counting rate. The initial "rough" models of processes in the equipment are based on the normative techniques and equation coefficients taken from the normative materials as well. Then, the synthesis of "fine" models has been carried out following the global practice in modeling and training simulator building, i.e., verification of "rough" models based on experimental data available to the developer. Finally, the last stage of modeling is adaptation (validation) of "fine" models to the prototype object using experimental data on the power-generating object and tests of these models with operating and maintaining personnel. These stages determine adequacy of the used mathematical model for a particular training simulator and, thus, its compliance with such modern scientific criteria as objectivity and experimental verifiability.

Graphene based widely-tunable and singly-polarized pulse generation with random fiber lasers

PubMed Central

Yao, B. C.; Rao, Y. J.; Wang, Z. N.; Wu, Y.; Zhou, J. H.; Wu, H.; Fan, M. Q.; Cao, X. L.; Zhang, W. L.; Chen, Y. F.; Li, Y. R.; Churkin, D.; Turitsyn, S.; Wong, C. W.

2015-01-01

Pulse generation often requires a stabilized cavity and its corresponding mode structure for initial phase-locking. Contrastingly, modeless cavity-free random lasers provide new possibilities for high quantum efficiency lasing that could potentially be widely tunable spectrally and temporally. Pulse generation in random lasers, however, has remained elusive since the discovery of modeless gain lasing. Here we report coherent pulse generation with modeless random lasers based on the unique polarization selectivity and broadband saturable absorption of monolayer graphene. Simultaneous temporal compression of cavity-free pulses are observed with such a polarization modulation, along with a broadly-tunable pulsewidth across two orders of magnitude down to 900 ps, a broadly-tunable repetition rate across three orders of magnitude up to 3 MHz, and a singly-polarized pulse train at 41 dB extinction ratio, about an order of magnitude larger than conventional pulsed fiber lasers. Moreover, our graphene-based pulse formation also demonstrates robust pulse-to-pulse stability and wide-wavelength operation due to the cavity-less feature. Such a graphene-based architecture not only provides a tunable pulsed random laser for fiber-optic sensing, speckle-free imaging, and laser-material processing, but also a new way for the non-random CW fiber lasers to generate widely tunable and singly-polarized pulses. PMID:26687730

Graphene based widely-tunable and singly-polarized pulse generation with random fiber lasers.

PubMed

Yao, B C; Rao, Y J; Wang, Z N; Wu, Y; Zhou, J H; Wu, H; Fan, M Q; Cao, X L; Zhang, W L; Chen, Y F; Li, Y R; Churkin, D; Turitsyn, S; Wong, C W

2015-12-21

Pulse generation often requires a stabilized cavity and its corresponding mode structure for initial phase-locking. Contrastingly, modeless cavity-free random lasers provide new possibilities for high quantum efficiency lasing that could potentially be widely tunable spectrally and temporally. Pulse generation in random lasers, however, has remained elusive since the discovery of modeless gain lasing. Here we report coherent pulse generation with modeless random lasers based on the unique polarization selectivity and broadband saturable absorption of monolayer graphene. Simultaneous temporal compression of cavity-free pulses are observed with such a polarization modulation, along with a broadly-tunable pulsewidth across two orders of magnitude down to 900 ps, a broadly-tunable repetition rate across three orders of magnitude up to 3 MHz, and a singly-polarized pulse train at 41 dB extinction ratio, about an order of magnitude larger than conventional pulsed fiber lasers. Moreover, our graphene-based pulse formation also demonstrates robust pulse-to-pulse stability and wide-wavelength operation due to the cavity-less feature. Such a graphene-based architecture not only provides a tunable pulsed random laser for fiber-optic sensing, speckle-free imaging, and laser-material processing, but also a new way for the non-random CW fiber lasers to generate widely tunable and singly-polarized pulses.

Skill Based Teaching--Learning Science Implementing Metaphorical Thinking

ERIC Educational Resources Information Center

Navaneedhan, Cittoor Girija; Kamalanabhan, T. J.

2017-01-01

Education in its general sense is a form of learning in which knowledge, skills, and habits of a group of people are transferred from one generation to the next through teaching, training, research, or simply through auto didacticism, Generally, it occurs through any experience that has a formative effect on the way one thinks, feels, or acts. The…

Multiple Exemplar Instruction and the Emergence of Generative Production of Suffixes as Autoclitic Frames

ERIC Educational Resources Information Center

Speckman, JeanneMarie; Greer, R. Douglas; Rivera-Valdes, Celestina

2012-01-01

We report 2 experiments that tested the effects of multiple exemplar instruction (MEI) across training sets on the emergence of productive autoclitic frames (suffixes) for 6 preschoolers with and without language-based disabilities. We implemented multiple exemplar tact instruction with subsets of stimuli whose "names" contained the suffix "-er"…

Whole-School Approaches to Incorporating Mindfulness-Based Interventions: Supporting the Capacity for Optimal Functioning in School Settings

ERIC Educational Resources Information Center

Kielty, Michele L.; Gilligan, Tammy D.; Staton, A. Renee

2017-01-01

With any intervention program, involving all stakeholders in a joint effort toward implementation is most likely to lead to success. Whole-school approaches that involve school personnel, students, families, and local communities have been associated with positive, sustained outcomes. For mindfulness training programs to generate the most…

Developing and Implementing a Reorganized Undergraduate Chemistry Curriculum Based on the Foundational Chemistry Topics of Structure, Reactivity, and Quantitation

ERIC Educational Resources Information Center

Schaller, Chris P.; Graham, Kate J.; Johnson, Brian J.; Fazal, M. A.; Jones, T. Nicholas; McIntee, Edward J.; Jakubowski, Henry V.

2014-01-01

The recent revision of undergraduate curricular guidelines from the American Chemical Society Committee on Professional Training (ACS-CPT) has generated interest in examining new ways of organizing course sequences both for chemistry majors and for nonmajors. A radical reconstruction of the foundation-level chemistry curriculum is presented in…

Stimulus meanings alter illusory self-motion (vection)--experimental examination of the train illusion.

PubMed

Seno, Takeharu; Fukuda, Haruaki

2012-01-01

Over the last 100 years, numerous studies have examined the effective visual stimulus properties for inducing illusory self-motion (known as vection). This vection is often experienced more strongly in daily life than under controlled experimental conditions. One well-known example of vection in real life is the so-called 'train illusion'. In the present study, we showed that this train illusion can also be generated in the laboratory using virtual computer graphics-based motion stimuli. We also demonstrated that this vection can be modified by altering the meaning of the visual stimuli (i.e., top down effects). Importantly, we show that the semantic meaning of a stimulus can inhibit or facilitate vection, even when there is no physical change to the stimulus.

Application of a neural network for reflectance spectrum classification

NASA Astrophysics Data System (ADS)

Yang, Gefei; Gartley, Michael

2017-05-01

Traditional reflectance spectrum classification algorithms are based on comparing spectrum across the electromagnetic spectrum anywhere from the ultra-violet to the thermal infrared regions. These methods analyze reflectance on a pixel by pixel basis. Inspired by high performance that Convolution Neural Networks (CNN) have demonstrated in image classification, we applied a neural network to analyze directional reflectance pattern images. By using the bidirectional reflectance distribution function (BRDF) data, we can reformulate the 4-dimensional into 2 dimensions, namely incident direction × reflected direction × channels. Meanwhile, RIT's micro-DIRSIG model is utilized to simulate additional training samples for improving the robustness of the neural networks training. Unlike traditional classification by using hand-designed feature extraction with a trainable classifier, neural networks create several layers to learn a feature hierarchy from pixels to classifier and all layers are trained jointly. Hence, the our approach of utilizing the angular features are different to traditional methods utilizing spatial features. Although training processing typically has a large computational cost, simple classifiers work well when subsequently using neural network generated features. Currently, most popular neural networks such as VGG, GoogLeNet and AlexNet are trained based on RGB spatial image data. Our approach aims to build a directional reflectance spectrum based neural network to help us to understand from another perspective. At the end of this paper, we compare the difference among several classifiers and analyze the trade-off among neural networks parameters.

Transgenerational effects of adolescent nicotine exposure in rats: Evidence for cognitive deficits in adult female offspring.

PubMed

Renaud, Samantha M; Fountain, Stephen B

2016-01-01

This study investigated whether adolescent nicotine exposure in one generation of rats would impair the cognitive capacity of a subsequent generation. Male and female rats in the parental F0 generation were given twice-daily i.p. injections of either 1.0mg/kg nicotine or an equivalent volume of saline for 35days during adolescence on postnatal days 25-59 (P25-59). After reaching adulthood, male and female nicotine-exposed rats were paired for breeding as were male and female saline control rats. Only female offspring were used in this experiment. Half of the offspring of F0 nicotine-exposed breeders and half of the offspring of F0 saline control rats received twice-daily i.p. injections of 1.0mg/kg nicotine during adolescence on P25-59. The remainder of the rats received twice-daily saline injections for the same period. To evaluate transgenerational effects of nicotine exposure on complex cognitive learning abilities, F1 generation rats were trained to perform a highly structured serial pattern in a serial multiple choice (SMC) task. Beginning on P95, rats in the F1 generation were given either 4days of massed training (20patterns/day) followed by spaced training (10 patterns/day) or only spaced training. Transgenerational effects of adolescent nicotine exposure were observed as greater difficulty in learning a "violation element" of the pattern, which indicated that rats were impaired in the ability to encode and remember multiple sequential elements as compound or configural cues. The results indicated that for rats that received massed training, F1 generation rats with adolescent nicotine exposure whose F0 generation parents also experienced adolescent nicotine exposure showed poorer learning of the violation element than rats that experienced adolescent nicotine exposure only in the F1 generation. Thus, adolescent nicotine exposure in one generation of rats produced a cognitive impairment in the next generation. Copyright © 2016 Elsevier Inc. All rights reserved.

Generating Seismograms with Deep Neural Networks

NASA Astrophysics Data System (ADS)

Krischer, L.; Fichtner, A.

2017-12-01

The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of neural networks by estimating the quality and uncertainties of the generated seismograms.

A game-based crowdsourcing platform for rapidly training middle and high school students to perform biomedical image analysis

NASA Astrophysics Data System (ADS)

Feng, Steve; Woo, Min-jae; Kim, Hannah; Kim, Eunso; Ki, Sojung; Shao, Lei; Ozcan, Aydogan

2016-03-01

We developed an easy-to-use and widely accessible crowd-sourcing tool for rapidly training humans to perform biomedical image diagnostic tasks and demonstrated this platform's ability on middle and high school students in South Korea to diagnose malaria infected red-blood-cells (RBCs) using Giemsa-stained thin blood smears imaged under light microscopes. We previously used the same platform (i.e., BioGames) to crowd-source diagnostics of individual RBC images, marking them as malaria positive (infected), negative (uninfected), or questionable (insufficient information for a reliable diagnosis). Using a custom-developed statistical framework, we combined the diagnoses from both expert diagnosticians and the minimally trained human crowd to generate a gold standard library of malaria-infection labels for RBCs. Using this library of labels, we developed a web-based training and educational toolset that provides a quantified score for diagnosticians/users to compare their performance against their peers and view misdiagnosed cells. We have since demonstrated the ability of this platform to quickly train humans without prior training to reach high diagnostic accuracy as compared to expert diagnosticians. Our initial trial group of 55 middle and high school students has collectively played more than 170 hours, each demonstrating significant improvements after only 3 hours of training games, with diagnostic scores that match expert diagnosticians'. Next, through a national-scale educational outreach program in South Korea we recruited >1660 students who demonstrated a similar performance level after 5 hours of training. We plan to further demonstrate this tool's effectiveness for other diagnostic tasks involving image labeling and aim to provide an easily-accessible and quickly adaptable framework for online training of new diagnosticians.

Human Factors in Training

NASA Technical Reports Server (NTRS)

Barshi, Immanuel; Byrne, Vicky; Arsintescu, Lucia; Connell, Erin

2010-01-01

Future space missions will be significantly longer than current shuttle missions and new systems will be more complex than current systems. Increasing communication delays between crews and Earth-based support means that astronauts need to be prepared to handle the unexpected on their own. As crews become more autonomous, their potential span of control and required expertise must grow to match their autonomy. It is not possible to train for every eventuality ahead of time on the ground, or to maintain trained skills across long intervals of disuse. To adequately prepare NASA personnel for these challenges, new training approaches, methodologies, and tools are required. This research project aims at developing these training capabilities. By researching established training principles, examining future needs, and by using current practices in space flight training as test beds, both in Flight Controller and Crew Medical domains, this research project is mitigating program risks and generating templates and requirements to meet future training needs. Training efforts in Fiscal Year 09 (FY09) strongly focused on crew medical training, but also began exploring how Space Flight Resource Management training for Mission Operations Directorate (MOD) Flight Controllers could be integrated with systems training for optimal Mission Control Center (MCC) operations. The Training Task addresses Program risks that lie at the intersection of the following three risks identified by the Project: 1) Risk associated with poor task design; 2) Risk of error due to inadequate information; and 3) Risk associated with reduced safety and efficiency due to poor human factors design.

Human Factors in Training

NASA Technical Reports Server (NTRS)

Barshi, Immanuel; Byrne, Vicky; Arsintescu, Lucia; Connell, Erin; Sandor, Aniko

2009-01-01

Future space missions will be significantly longer than current shuttle missions and new systems will be more complex than current systems. Increasing communication delays between crews and Earth-based support means that astronauts need to be prepared to handle the unexpected on their own. As crews become more autonomous, their potential span of control and required expertise must grow to match their autonomy. It is not possible to train for every eventuality ahead of time on the ground, or to maintain trained skills across long intervals of disuse. To adequately prepare NASA personnel for these challenges, new training approaches, methodologies, and tools are required. This research project aims at developing these training capabilities. By researching established training principles, examining future needs, and by using current practices in space flight training as test beds, both in Flight Controller and Crew Medical domains, this research project is mitigating program risks and generating templates and requirements to meet future training needs. Training efforts in Fiscal Year 08 (FY08) strongly focused on crew medical training, but also began exploring how Space Flight Resource Management training for Mission Operations Directorate (MOD) Flight Controllers could be integrated with systems training for optimal Mission Control Center (MCC) operations. The Training Task addresses Program risks that lie at the intersection of the following three risks identified by the Project: (1) Risk associated with poor task design (2) Risk of error due to inadequate information (3) Risk associated with reduced safety and efficiency due to poor human factors design

Shifting mindsets: a realist synthesis of evidence from self-management support training.

PubMed

Davies, Freya; Wood, Fiona; Bullock, Alison; Wallace, Carolyn; Edwards, Adrian

2018-03-01

Accompanying the growing expectation of patient self-management is the need to ensure health care professionals (HCPs) have the required attitudes and skills to provide effective self-management support (SMS). Results from existing training interventions for HCPs in SMS have been mixed and the evidence base is weaker for certain settings, including supporting people with progressive neurological conditions (PNCs). We set out to understand how training operates, and to identify barriers and facilitators to training designed to support shifts in attitudes amongst HCPs. We undertook a realist literature synthesis focused on: (i) the influence of how HCPs, teams and organisations view and adopt self-management; and (ii) how SMS needs to be tailored for people with PNCs. A traditional database search strategy was used alongside citation tracking, grey literature searching and stakeholder recommendations. We supplemented PNC-specific literature with data from other long-term conditions. Key informant interviews and stakeholder advisory group meetings informed the synthesis process. Realist context-mechanism-outcome configurations were generated and mapped onto the stages described in Mezirow's Transformative Learning Theory. Forty-four original articles were included (19 relating to PNCs), from which seven refined theories were developed. The theories identified important training elements (evidence provision, building skills and confidence, facilitating reflection and generating empathy). The significant influence of workplace factors as possible barriers or facilitators was highlighted. Embracing SMS often required challenging traditional professional role boundaries. The integration of SMS into routine care is not an automatic outcome from training. A transformative learning process is often required to trigger the necessary mindset shift. Training should focus on how individual HCPs define and value SMS and how their work context (patient group and organisational constraints) influences this process. Proactively addressing potential contextual barriers may facilitate implementation. These findings could be applied to other types of training designed to shift attitudes amongst HCPs. © 2018 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Fuel cell power trains for road traffic

NASA Astrophysics Data System (ADS)

Höhlein, Bernd; Biedermann, Peter; Grube, Thomas; Menzer, Reinhard

Legal regulations, especially the low emission vehicle (LEV) laws in California, are the driving forces for more intensive technological developments with respect to a global automobile market. In the future, high efficient vehicles at very low emission levels will include low temperature fuel cell systems (e.g., polymer electrolyte fuel cell (PEFC)) as units of hydrogen-, methanol- or gasoline-based electric power trains. In the case of methanol or gasoline/diesel, hydrogen has to be produced on-board using heated steam or partial oxidation reformers as well as catalytic burners and gas cleaning units. Methanol could also be used for direct electricity generation inside the fuel cell (direct methanol fuel cell (DMFC)). The development potentials and the results achieved so far for these concepts differ extremely. Based on the experience gained so far, the goals for the next few years include cost and weight reductions as well as optimizations in terms of the energy management of power trains with PEFC systems. At the same time, questions of fuel specification, fuel cycle management, materials balances and environmental assessment will have to be discussed more intensively. On the basis of process engineering analyses for net electricity generation in PEFC-powered power trains as well as on assumptions for both electric power trains and vehicle configurations, overall balances have been carried out. They will lead not only to specific energy demand data and specific emission levels (CO 2, CO, VOC, NO x) for the vehicle but will also present data of its full fuel cycle (FFC) in comparison to those of FFCs including internal combustion engines (ICE) after the year 2005. Depending on the development status (today or in 2010) and the FFC benchmark results, the advantages of balances results of FFC with PEFC vehicles are small in terms of specific energy demand and CO 2 emissions, but very high with respect to local emission levels.

Neural Network-Based Sensor Validation for Turboshaft Engines

NASA Technical Reports Server (NTRS)

Moller, James C.; Litt, Jonathan S.; Guo, Ten-Huei

1998-01-01

Sensor failure detection, isolation, and accommodation using a neural network approach is described. An auto-associative neural network is configured to perform dimensionality reduction on the sensor measurement vector and provide estimated sensor values. The sensor validation scheme is applied in a simulation of the T700 turboshaft engine in closed loop operation. Performance is evaluated based on the ability to detect faults correctly and maintain stable and responsive engine operation. The set of sensor outputs used for engine control forms the network input vector. Analytical redundancy is verified by training networks of successively smaller bottleneck layer sizes. Training data generation and strategy are discussed. The engine maintained stable behavior in the presence of sensor hard failures. With proper selection of fault determination thresholds, stability was maintained in the presence of sensor soft failures.

Fault detection for hydraulic pump based on chaotic parallel RBF network

NASA Astrophysics Data System (ADS)

Lu, Chen; Ma, Ning; Wang, Zhipeng

2011-12-01

In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.

MLP based LOGSIG transfer function for solar generation monitoring

NASA Astrophysics Data System (ADS)

Hashim, Fakroul Ridzuan; Din, Muhammad Faiz Md; Ahmad, Shahril; Arif, Farah Khairunnisa; Rizman, Zairi Ismael

2018-02-01

Solar panel is one of the renewable energy that can reduce the environmental pollution and have a wide potential of application. The exact solar prediction model will give a big impact on the management of solar power plants and the design of solar energy systems. This paper attempts to use Multilayer Perceptron (MLP) neural network based transfer function. The MLP network can be used to calculate the temperature module (TM) in Malaysia. This can be done by simulating the collected data of four weather variables which are the ambient temperature (TA), local wind speed (VW), solar radiation flux (GT) and the relative humidity (RH) as the input into the neural network. The transfer function will be applied to the 14 types of training. Finally, an equation from the best training algorithm will be deduced to calculate the temperature module based on the input of weather variables in Malaysia.

A Customized Attention-Based Long Short-Term Memory Network for Distant Supervised Relation Extraction.

PubMed

He, Dengchao; Zhang, Hongjun; Hao, Wenning; Zhang, Rui; Cheng, Kai

2017-07-01

Distant supervision, a widely applied approach in the field of relation extraction can automatically generate large amounts of labeled training corpus with minimal manual effort. However, the labeled training corpus may have many false-positive data, which would hurt the performance of relation extraction. Moreover, in traditional feature-based distant supervised approaches, extraction models adopt human design features with natural language processing. It may also cause poor performance. To address these two shortcomings, we propose a customized attention-based long short-term memory network. Our approach adopts word-level attention to achieve better data representation for relation extraction without manually designed features to perform distant supervision instead of fully supervised relation extraction, and it utilizes instance-level attention to tackle the problem of false-positive data. Experimental results demonstrate that our proposed approach is effective and achieves better performance than traditional methods.

Using the core curriculum on childhood trauma to strengthen clinical knowledge in evidence-based practitioners.

PubMed

Layne, Christopher M; Strand, Virginia; Popescu, Marciana; Kaplow, Julie B; Abramovitz, Robert; Stuber, Margaret; Amaya-Jackson, Lisa; Ross, Leslie; Pynoos, Robert S

2014-01-01

The high prevalence of trauma exposure in mental health service-seeking populations, combined with advances in evidence-based practice, competency-based training, common-elements research, and adult learning make this an opportune time to train the mental health workforce in trauma competencies. The Core Curriculum on Childhood Trauma (CCCT) utilizes a five-tiered conceptual framework (comprising Empirical Evidence, Core Trauma Concepts, Intervention Objectives, Practice Elements, and Skills), coupled with problem-based learning, to build foundational trauma knowledge and clinical reasoning skills. We present findings from three studies: Study 1 found that social work graduate students' participation in a CCCT course (N = 1,031) was linked to significant pre-post increases in self-reported confidence in applying core trauma concepts to their clinical work. Study 2 found significant pre-post increases in self-reported conceptual readiness (N = 576) and field readiness (N = 303) among social work graduate students participating in a "Gold Standard Plus" educational model that integrated classroom instruction in core trauma concepts, training in evidence-based trauma treatment (EBTT), and implementation of that EBTT in a supervised field placement. Students ranked the core concepts course as an equivalent or greater contributor to field readiness compared to standard EBTT training. Study 3 used qualitative methods to "distill" common elements (35 intervention objectives, 59 practice elements) from 26 manualized trauma interventions. The CCCT is a promising tool for educating "next-generation" evidence-based practitioners who possess competencies needed to implement modularized, individually tailored trauma interventions by strengthening clinical knowledge, clinical reasoning, and familiarity with common elements.

Interventional radiology virtual simulator for liver biopsy.

PubMed

Villard, P F; Vidal, F P; ap Cenydd, L; Holbrey, R; Pisharody, S; Johnson, S; Bulpitt, A; John, N W; Bello, F; Gould, D

2014-03-01

Training in Interventional Radiology currently uses the apprenticeship model, where clinical and technical skills of invasive procedures are learnt during practice in patients. This apprenticeship training method is increasingly limited by regulatory restrictions on working hours, concerns over patient risk through trainees' inexperience and the variable exposure to case mix and emergencies during training. To address this, we have developed a computer-based simulation of visceral needle puncture procedures. A real-time framework has been built that includes: segmentation, physically based modelling, haptics rendering, pseudo-ultrasound generation and the concept of a physical mannequin. It is the result of a close collaboration between different universities, involving computer scientists, clinicians, clinical engineers and occupational psychologists. The technical implementation of the framework is a robust and real-time simulation environment combining a physical platform and an immersive computerized virtual environment. The face, content and construct validation have been previously assessed, showing the reliability and effectiveness of this framework, as well as its potential for teaching visceral needle puncture. A simulator for ultrasound-guided liver biopsy has been developed. It includes functionalities and metrics extracted from cognitive task analysis. This framework can be useful during training, particularly given the known difficulties in gaining significant practice of core skills in patients.

A Comparison of the Effects of Ethics Training on International and US Students.

PubMed

Steele, Logan M; Johnson, James F; Watts, Logan L; MacDougall, Alexandra E; Mumford, Michael D; Connelly, Shane; Lee Williams, T H

2016-08-01

As scientific and engineering efforts become increasingly global in nature, the need to understand differences in perceptions of research ethics issues across countries and cultures is imperative. However, investigations into the connection between nationality and ethical decision-making in the sciences have largely generated mixed results. In Study 1 of this paper, a measure of biases and compensatory strategies that could influence ethical decisions was administered. Results from this study indicated that graduate students from the United States and international graduate students studying in the US are prone to different biases. Based on these findings, recommendations are made for developing ethics education interventions to target these decision-making biases. In Study 2, we employed an ethics training intervention based on ethical sensemaking and used a well-established measure of ethical decision-making that more fully captures the content of ethical judgment. Similar to Study 1, the results obtained in this study suggest differences do exist between graduate students from the US and international graduate students in ethical decision-making prior to taking the research ethics training. However, similar effects were observed for both groups following the completion of the ethics training intervention.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks.

PubMed

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-12-08

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the "small sample size" (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0-1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system.

Generic Learning-Based Ensemble Framework for Small Sample Size Face Recognition in Multi-Camera Networks

PubMed Central

Zhang, Cuicui; Liang, Xuefeng; Matsuyama, Takashi

2014-01-01

Multi-camera networks have gained great interest in video-based surveillance systems for security monitoring, access control, etc. Person re-identification is an essential and challenging task in multi-camera networks, which aims to determine if a given individual has already appeared over the camera network. Individual recognition often uses faces as a trial and requires a large number of samples during the training phrase. This is difficult to fulfill due to the limitation of the camera hardware system and the unconstrained image capturing conditions. Conventional face recognition algorithms often encounter the “small sample size” (SSS) problem arising from the small number of training samples compared to the high dimensionality of the sample space. To overcome this problem, interest in the combination of multiple base classifiers has sparked research efforts in ensemble methods. However, existing ensemble methods still open two questions: (1) how to define diverse base classifiers from the small data; (2) how to avoid the diversity/accuracy dilemma occurring during ensemble. To address these problems, this paper proposes a novel generic learning-based ensemble framework, which augments the small data by generating new samples based on a generic distribution and introduces a tailored 0–1 knapsack algorithm to alleviate the diversity/accuracy dilemma. More diverse base classifiers can be generated from the expanded face space, and more appropriate base classifiers are selected for ensemble. Extensive experimental results on four benchmarks demonstrate the higher ability of our system to cope with the SSS problem compared to the state-of-the-art system. PMID:25494350

Diffraction leveraged modulation of X-ray pulses using MEMS-based X-ray optics

DOEpatents

Lopez, Daniel; Shenoy, Gopal; Wang, Jin; Walko, Donald A.; Jung, Il-Woong; Mukhopadhyay, Deepkishore

2016-08-09

A method and apparatus are provided for implementing Bragg-diffraction leveraged modulation of X-ray pulses using MicroElectroMechanical systems (MEMS) based diffractive optics. An oscillating crystalline MEMS device generates a controllable time-window for diffraction of the incident X-ray radiation. The Bragg-diffraction leveraged modulation of X-ray pulses includes isolating a particular pulse, spatially separating individual pulses, and spreading a single pulse from an X-ray pulse-train.

Fabric defect detection based on visual saliency using deep feature and low-rank recovery

NASA Astrophysics Data System (ADS)

Liu, Zhoufeng; Wang, Baorui; Li, Chunlei; Li, Bicao; Dong, Yan

2018-04-01

Fabric defect detection plays an important role in improving the quality of fabric product. In this paper, a novel fabric defect detection method based on visual saliency using deep feature and low-rank recovery was proposed. First, unsupervised training is carried out by the initial network parameters based on MNIST large datasets. The supervised fine-tuning of fabric image library based on Convolutional Neural Networks (CNNs) is implemented, and then more accurate deep neural network model is generated. Second, the fabric images are uniformly divided into the image block with the same size, then we extract their multi-layer deep features using the trained deep network. Thereafter, all the extracted features are concentrated into a feature matrix. Third, low-rank matrix recovery is adopted to divide the feature matrix into the low-rank matrix which indicates the background and the sparse matrix which indicates the salient defect. In the end, the iterative optimal threshold segmentation algorithm is utilized to segment the saliency maps generated by the sparse matrix to locate the fabric defect area. Experimental results demonstrate that the feature extracted by CNN is more suitable for characterizing the fabric texture than the traditional LBP, HOG and other hand-crafted features extraction method, and the proposed method can accurately detect the defect regions of various fabric defects, even for the image with complex texture.

40 CFR 262.207 - Training.

Code of Federal Regulations, 2014 CFR

2014-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) STANDARDS APPLICABLE TO GENERATORS OF HAZARDOUS WASTE Alternative Requirements for Hazardous Waste Determination and... experiment; or (2) Formal classroom training; or (3) Electronic/written training; or (4) On-the-job training...

40 CFR 262.207 - Training.

Code of Federal Regulations, 2012 CFR

2012-07-01

... Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) SOLID WASTES (CONTINUED) STANDARDS APPLICABLE TO GENERATORS OF HAZARDOUS WASTE Alternative Requirements for Hazardous Waste Determination and... experiment; or (2) Formal classroom training; or (3) Electronic/written training; or (4) On-the-job training...

Train Generated Air Contaminants in the Train Crew's Working Environment

DOT National Transportation Integrated Search

1977-02-01

This document contains data on the levels of air contaminants in the train crew's working environment. Measurements were made in locomotive cabs and cabooses of freight trains travelling through long tunnels and over mountainous terrain. In addition,...

Strategic Issues for Training.

ERIC Educational Resources Information Center

Pollitt, David, Ed.

1998-01-01

Includes 21 short articles on a variety of subjects: Internet for management development, lifelong learning in virtual universities, high performance organizations, National Vocational Qualifications, cost effectiveness and training effectiveness, mind maps, Generation X training, Japanese vocational training, management development in Libya, and…

Quality Assessment and Comparison of Smartphone and Leica C10 Laser Scanner Based Point Clouds

NASA Astrophysics Data System (ADS)

Sirmacek, Beril; Lindenbergh, Roderik; Wang, Jinhu

2016-06-01

3D urban models are valuable for urban map generation, environment monitoring, safety planning and educational purposes. For 3D measurement of urban structures, generally airborne laser scanning sensors or multi-view satellite images are used as a data source. However, close-range sensors (such as terrestrial laser scanners) and low cost cameras (which can generate point clouds based on photogrammetry) can provide denser sampling of 3D surface geometry. Unfortunately, terrestrial laser scanning sensors are expensive and trained persons are needed to use them for point cloud acquisition. A potential effective 3D modelling can be generated based on a low cost smartphone sensor. Herein, we show examples of using smartphone camera images to generate 3D models of urban structures. We compare a smartphone based 3D model of an example structure with a terrestrial laser scanning point cloud of the structure. This comparison gives us opportunity to discuss the differences in terms of geometrical correctness, as well as the advantages, disadvantages and limitations in data acquisition and processing. We also discuss how smartphone based point clouds can help to solve further problems with 3D urban model generation in a practical way. We show that terrestrial laser scanning point clouds which do not have color information can be colored using smartphones. The experiments, discussions and scientific findings might be insightful for the future studies in fast, easy and low-cost 3D urban model generation field.

Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

PubMed

Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

2015-01-01

For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

Input-output mapping reconstruction of spike trains at dorsal horn evoked by manual acupuncture

NASA Astrophysics Data System (ADS)

Wei, Xile; Shi, Dingtian; Yu, Haitao; Deng, Bin; Lu, Meili; Han, Chunxiao; Wang, Jiang

2016-12-01

In this study, a generalized linear model (GLM) is used to reconstruct mapping from acupuncture stimulation to spike trains driven by action potential data. The electrical signals are recorded in spinal dorsal horn after manual acupuncture (MA) manipulations with different frequencies being taken at the “Zusanli” point of experiment rats. Maximum-likelihood method is adopted to estimate the parameters of GLM and the quantified value of assumed model input. Through validating the accuracy of firings generated from the established GLM, it is found that the input-output mapping of spike trains evoked by acupuncture can be successfully reconstructed for different frequencies. Furthermore, via comparing the performance of several GLMs based on distinct inputs, it suggests that input with the form of half-sine with noise can well describe the generator potential induced by acupuncture mechanical action. Particularly, the comparison of reproducing the experiment spikes for five selected inputs is in accordance with the phenomenon found in Hudgkin-Huxley (H-H) model simulation, which indicates the mapping from half-sine with noise input to experiment spikes meets the real encoding scheme to some extent. These studies provide us a new insight into coding processes and information transfer of acupuncture.

Biosignals learning and synthesis using deep neural networks.

PubMed

Belo, David; Rodrigues, João; Vaz, João R; Pezarat-Correia, Pedro; Gamboa, Hugo

2017-09-25

Modeling physiological signals is a complex task both for understanding and synthesize biomedical signals. We propose a deep neural network model that learns and synthesizes biosignals, validated by the morphological equivalence of the original ones. This research could lead the creation of novel algorithms for signal reconstruction in heavily noisy data and source detection in biomedical engineering field. The present work explores the gated recurrent units (GRU) employed in the training of respiration (RESP), electromyograms (EMG) and electrocardiograms (ECG). Each signal is pre-processed, segmented and quantized in a specific number of classes, corresponding to the amplitude of each sample and fed to the model, which is composed by an embedded matrix, three GRU blocks and a softmax function. This network is trained by adjusting its internal parameters, acquiring the representation of the abstract notion of the next value based on the previous ones. The simulated signal was generated by forecasting a random value and re-feeding itself. The resulting generated signals are similar with the morphological expression of the originals. During the learning process, after a set of iterations, the model starts to grasp the basic morphological characteristics of the signal and later their cyclic characteristics. After training, these models' prediction are closer to the signals that trained them, specially the RESP and ECG. This synthesis mechanism has shown relevant results that inspire the use to characterize signals from other physiological sources.

Automatic detection of apical roots in oral radiographs

NASA Astrophysics Data System (ADS)

Wu, Yi; Xie, Fangfang; Yang, Jie; Cheng, Erkang; Megalooikonomou, Vasileios; Ling, Haibin

2012-03-01

The apical root regions play an important role in analysis and diagnosis of many oral diseases. Automatic detection of such regions is consequently the first step toward computer-aided diagnosis of these diseases. In this paper we propose an automatic method for periapical root region detection by using the state-of-theart machine learning approaches. Specifically, we have adapted the AdaBoost classifier for apical root detection. One challenge in the task is the lack of training cases especially for diseased ones. To handle this problem, we boost the training set by including more root regions that are close to the annotated ones and decompose the original images to randomly generate negative samples. Based on these training samples, the Adaboost algorithm in combination with Haar wavelets is utilized in this task to train an apical root detector. The learned detector usually generates a large amount of true and false positives. In order to reduce the number of false positives, a confidence score for each candidate detection result is calculated for further purification. We first merge the detected regions by combining tightly overlapped detected candidate regions and then we use the confidence scores from the Adaboost detector to eliminate the false positives. The proposed method is evaluated on a dataset containing 39 annotated digitized oral X-Ray images from 21 patients. The experimental results show that our approach can achieve promising detection accuracy.

Neural Spike Train Synchronisation Indices: Definitions, Interpretations and Applications.

PubMed

Halliday, D M; Rosenberg, J R

2017-04-24

A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% { 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1 - 250 spikes/sec). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multi electrode array data is briefly discussed.

A self-adapting heuristic for automatically constructing terrain appreciation exercises

NASA Astrophysics Data System (ADS)

Nanda, S.; Lickteig, C. L.; Schaefer, P. S.

2008-04-01

Appreciating terrain is a key to success in both symmetric and asymmetric forms of warfare. Training to enable Soldiers to master this vital skill has traditionally required their translocation to a selected number of areas, each affording a desired set of topographical features, albeit with limited breadth of variety. As a result, the use of such methods has proved to be costly and time consuming. To counter this, new computer-aided training applications permit users to rapidly generate and complete training exercises in geo-specific open and urban environments rendered by high-fidelity image generation engines. The latter method is not only cost-efficient, but allows any given exercise and its conditions to be duplicated or systematically varied over time. However, even such computer-aided applications have shortcomings. One of the principal ones is that they usually require all training exercises to be painstakingly constructed by a subject matter expert. Furthermore, exercise difficulty is usually subjectively assessed and frequently ignored thereafter. As a result, such applications lack the ability to grow and adapt to the skill level and learning curve of each trainee. In this paper, we present a heuristic that automatically constructs exercises for identifying key terrain. Each exercise is created and administered in a unique iteration, with its level of difficulty tailored to the trainee's ability based on the correctness of that trainee's responses in prior iterations.

CGAT: a model for immersive personalized training in computational genomics.

PubMed

Sims, David; Ponting, Chris P; Heger, Andreas

2016-01-01

How should the next generation of genomics scientists be trained while simultaneously pursuing high quality and diverse research? CGAT, the Computational Genomics Analysis and Training programme, was set up in 2010 by the UK Medical Research Council to complement its investment in next-generation sequencing capacity. CGAT was conceived around the twin goals of training future leaders in genome biology and medicine, and providing much needed capacity to UK science for analysing genome scale data sets. Here we outline the training programme employed by CGAT and describe how it dovetails with collaborative research projects to launch scientists on the road towards independent research careers in genomics. © The Author 2015. Published by Oxford University Press.

Program Review Challenges and Opportunities for Training the Next Generation of Biophysicists: Perspectives of the Directors of the Molecular Biophysics Training Program at Northwestern University

PubMed Central

Neuhaus, Francis; Widom, Jonathan; MacDonald, Robert; Jardetzky, Theodore; Radhakrishnan, Ishwar

2009-01-01

Molecular biophysics is a broad, diverse, and dynamic field that has presented a variety of unique challenges and opportunities for training future generations of investigators. Having been or currently being intimately associated with the Molecular Biophysics Training Program at Northwestern, we present our perspectives on various issues that we have encountered over the years. We propose no cookie-cutter solutions, as there is no consensus on what constitutes the “ideal” program. However, there is uniformity in opinion on some key issues that might be useful to those interested in establishing a biophysics training program. PMID:18293401

Adversarial Threshold Neural Computer for Molecular de Novo Design.

PubMed

Putin, Evgeny; Asadulaev, Arip; Vanhaelen, Quentin; Ivanenkov, Yan; Aladinskaya, Anastasia V; Aliper, Alex; Zhavoronkov, Alex

2018-03-30

In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network architecture and reinforcement learning. ATNC uses a Differentiable Neural Computer as a generator and has a new specific block, called adversarial threshold (AT). AT acts as a filter between the agent (generator) and the environment (discriminator + objective reward functions). Furthermore, to generate more diverse molecules we introduce a new objective reward function named Internal Diversity Clustering (IDC). In this work, ATNC is tested and compared with the ORGANIC model. Both models were trained on the SMILES string representation of the molecules, using four objective functions (internal similarity, Muegge druglikeness filter, presence or absence of sp 3 -rich fragments, and IDC). The SMILES representations of 15K druglike molecules from the ChemDiv collection were used as a training data set. For the different functions, ATNC outperforms ORGANIC. Combined with the IDC, ATNC generates 72% of valid and 77% of unique SMILES strings, while ORGANIC generates only 7% of valid and 86% of unique SMILES strings. For each set of molecules generated by ATNC and ORGANIC, we analyzed distributions of four molecular descriptors (number of atoms, molecular weight, logP, and tpsa) and calculated five chemical statistical features (internal diversity, number of unique heterocycles, number of clusters, number of singletons, and number of compounds that have not been passed through medicinal chemistry filters). Analysis of key molecular descriptors and chemical statistical features demonstrated that the molecules generated by ATNC elicited better druglikeness properties. We also performed in vitro validation of the molecules generated by ATNC; results indicated that ATNC is an effective method for producing hit compounds.

Theoretical analysis and simulation study of the deep overcompression mode of velocity bunching for a comblike electron bunch train

NASA Astrophysics Data System (ADS)

Wang, Dan; Yan, Lixin; Du, YingChao; Huang, Wenhui; Gai, Wei; Tang, Chuanxiang

2018-02-01

Premodulated comblike electron bunch trains are used in a wide range of research fields, such as for wakefield-based particle acceleration and tunable radiation sources. We propose an optimized compression scheme for bunch trains in which a traveling wave accelerator tube and a downstream drift segment are together used as a compressor. When the phase injected into the accelerator tube for the bunch train is set to ≪-10 0 ° , velocity bunching occurs in a deep overcompression mode, which reverses the phase space and maintains a velocity difference within the injected beam, thereby giving rise to a compressed comblike electron bunch train after a few-meter-long drift segment; we call this the deep overcompression scheme. The main benefits of this scheme are the relatively large phase acceptance and the uniformity of compression for the bunch train. The comblike bunch train generated via this scheme is widely tunable: For the two-bunch case, the energy and time spacings can be continuously adjusted from +1 to -1 MeV and from 13 to 3 ps, respectively, by varying the injected phase of the bunch train from -22 0 ° to -14 0 ° . Both theoretical analysis and beam dynamics simulations are presented to study the properties of the deep overcompression scheme.

THE TRAINING OF NEXT GENERATION DATA SCIENTISTS IN BIOMEDICINE.

PubMed

Garmire, Lana X; Gliske, Stephen; Nguyen, Quynh C; Chen, Jonathan H; Nemati, Shamim; VAN Horn, John D; Moore, Jason H; Shreffler, Carol; Dunn, Michelle

2017-01-01

With the booming of new technologies, biomedical science has transformed into digitalized, data intensive science. Massive amount of data need to be analyzed and interpreted, demand a complete pipeline to train next generation data scientists. To meet this need, the transinstitutional Big Data to Knowledge (BD2K) Initiative has been implemented since 2014, complementing other NIH institutional efforts. In this report, we give an overview the BD2K K01 mentored scientist career awards, which have demonstrated early success. We address the specific trainings needed in representative data science areas, in order to make the next generation of data scientists in biomedicine.

Molecular Dynamics-based Simulations of Bulk/Interfacial Structures and Diffusion Behaviors in Nuclear Waste Glasses

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

Du, Jincheng; Rimsza, Jessica; Deng, Lu

This NEUP Project aimed to generate accurate atomic structural models of nuclear waste glasses by using large-scale molecular dynamics-based computer simulations and to use these models to investigate self-diffusion behaviors, interfacial structures, and hydrated gel structures formed during dissolution of these glasses. The goal was to obtain realistic and accurate short and medium range structures of these complex oxide glasses, to provide a mechanistic understanding of the dissolution behaviors, and to generate reliable information with predictive power in designing nuclear waste glasses for long-term geological storage. Looking back of the research accomplishments of this project, most of the scientific goalsmore » initially proposed have been achieved through intensive research in the three and a half year period of the project. This project has also generated a wealth of scientific data and vibrant discussions with various groups through collaborations within and outside of this project. Throughout the project one book chapter and 14 peer reviewed journal publications have been generated (including one under review) and 16 presentations (including 8 invited talks) have been made to disseminate the results of this project in national and international conference. Furthermore, this project has trained several outstanding graduate students and young researchers for future workforce in nuclear related field, especially on nuclear waste immobilization. One postdoc and four PhD students have been fully or partially supported through the project with intensive training in the field material science and engineering with expertise on glass science and nuclear waste disposal« less

The effects of climate downscaling technique and observational data set on modeled ecological responses.

PubMed

Pourmokhtarian, Afshin; Driscoll, Charles T; Campbell, John L; Hayhoe, Katharine; Stoner, Anne M K

2016-07-01

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. © 2016 by the Ecological Society of America.

Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI.

PubMed

Shiradkar, Rakesh; Podder, Tarun K; Algohary, Ahmad; Viswanath, Satish; Ellis, Rodney J; Madabhushi, Anant

2016-11-10

Radiomics or computer - extracted texture features have been shown to achieve superior performance than multiparametric MRI (mpMRI) signal intensities alone in targeting prostate cancer (PCa) lesions. Radiomics along with deformable co-registration tools can be used to develop a framework to generate targeted focal radiotherapy treatment plans. The Rad-TRaP framework comprises three distinct modules. Firstly, a module for radiomics based detection of PCa lesions on mpMRI via a feature enabled machine learning classifier. The second module comprises a multi-modal deformable co-registration scheme to map tissue, organ, and delineated target volumes from MRI onto CT. Finally, the third module involves generation of a radiomics based dose plan on MRI for brachytherapy and on CT for EBRT using the target delineations transferred from the MRI to the CT. Rad-TRaP framework was evaluated using a retrospective cohort of 23 patient studies from two different institutions. 11 patients from the first institution were used to train a radiomics classifier, which was used to detect tumor regions in 12 patients from the second institution. The ground truth cancer delineations for training the machine learning classifier were made by an experienced radiation oncologist using mpMRI, knowledge of biopsy location and radiology reports. The detected tumor regions were used to generate treatment plans for brachytherapy using mpMRI, and tumor regions mapped from MRI to CT to generate corresponding treatment plans for EBRT. For each of EBRT and brachytherapy, 3 dose plans were generated - whole gland homogeneous ([Formula: see text]) which is the current clinical standard, radiomics based focal ([Formula: see text]), and whole gland with a radiomics based focal boost ([Formula: see text]). Comparison of [Formula: see text] against conventional [Formula: see text] revealed that targeted focal brachytherapy would result in a marked reduction in dosage to the OARs while ensuring that the prescribed dose is delivered to the lesions. [Formula: see text] resulted in only a marginal increase in dosage to the OARs compared to [Formula: see text]. A similar trend was observed in case of EBRT with [Formula: see text] and [Formula: see text] compared to [Formula: see text]. A radiotherapy planning framework to generate targeted focal treatment plans has been presented. The focal treatment plans generated using the framework showed reduction in dosage to the organs at risk and a boosted dose delivered to the cancerous lesions.

Improving reading comprehension in reading and listening settings: the effect of two training programmes focusing on metacognition and working memory.

PubMed

Carretti, Barbara; Caldarola, Nadia; Tencati, Chiara; Cornoldi, Cesare

2014-06-01

Metacognition and working memory (WM) have been found associated with success in reading comprehension, but no studies have examined their combined effect on the training of reading comprehension. Another open question concerns the role of listening comprehension: In particular, it is not clear whether training to improve reading comprehension must necessarily be based on processing written material or whether, as suggested in a recent study by Clarke et al. (2010, Psychol. Sci., 21, 1106), a programme based on verbal language could also be effective. The study examined the feasibility of improving text comprehension in school children by comparing the efficacy of two training programmes, both involving metacognition and WM, but one based on listening comprehension, the other on reading comprehension. The study involved a sample of 159 pupils attending eight classes in the fourth and fifth grades (age range 9-11 years). The listening and reading programmes focused on the same abilities/processes strictly related to text comprehension, and particularly metacognitive knowledge and control, WM (per se and in terms of integrating information in a text). The training programmes were implemented by school teachers as part of the class's normal school activities, under the supervision of experts. Their efficacy was compared with the results obtained in an active control group that completed standard text comprehension activities. Our results showed that both the training programmes focusing on specific text comprehension skills were effective in improving the children's achievement, but training in reading comprehension generated greater gains than the listening comprehension programme. Our study suggests that activities focusing specifically on metacognition and WM could foster text comprehension, but the potential benefit is influenced by the training modality, that is, the Reading group obtained greater and longer-lasting improvements than the Active control or Listening groups. © 2013 The British Psychological Society.

Membership generation using multilayer neural network

NASA Technical Reports Server (NTRS)

Kim, Jaeseok

1992-01-01

There has been intensive research in neural network applications to pattern recognition problems. Particularly, the back-propagation network has attracted many researchers because of its outstanding performance in pattern recognition applications. In this section, we describe a new method to generate membership functions from training data using a multilayer neural network. The basic idea behind the approach is as follows. The output values of a sigmoid activation function of a neuron bear remarkable resemblance to membership values. Therefore, we can regard the sigmoid activation values as the membership values in fuzzy set theory. Thus, in order to generate class membership values, we first train a suitable multilayer network using a training algorithm such as the back-propagation algorithm. After the training procedure converges, the resulting network can be treated as a membership generation network, where the inputs are feature values and the outputs are membership values in the different classes. This method allows fairly complex membership functions to be generated because the network is highly nonlinear in general. Also, it is to be noted that the membership functions are generated from a classification point of view. For pattern recognition applications, this is highly desirable, although the membership values may not be indicative of the degree of typicality of a feature value in a particular class.

Mathematical modeling of electrical activity of uterine muscle cells.

PubMed

Rihana, Sandy; Terrien, Jeremy; Germain, Guy; Marque, Catherine

2009-06-01

The uterine electrical activity is an efficient parameter to study the uterine contractility. In order to understand the ionic mechanisms responsible for its generation, we aimed at building a mathematical model of the uterine cell electrical activity based upon the physiological mechanisms. First, based on the voltage clamp experiments found in the literature, we focus on the principal ionic channels and their cognate currents involved in the generation of this electrical activity. Second, we provide the methodology of formulations of uterine ionic currents derived from a wide range of electrophysiological data. The model is validated step by step by comparing simulated voltage-clamp results with the experimental ones. The model reproduces successfully the generation of single spikes or trains of action potentials that fit with the experimental data. It allows analyzing ionic channels implications. Likewise, the calcium-dependent conductance influences significantly the cellular oscillatory behavior.

Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device.

PubMed

McKinstry, Jeffrey L; Edelman, Gerald M

2013-01-01

Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Droplet-based microfluidic washing module for magnetic particle-based assays

PubMed Central

Lee, Hun; Xu, Linfeng; Oh, Kwang W.

2014-01-01

In this paper, we propose a continuous flow droplet-based microfluidic platform for magnetic particle-based assays by employing in-droplet washing. The droplet-based washing was implemented by traversing functionalized magnetic particles across a laterally merged droplet from one side (containing sample and reagent) to the other (containing buffer) by an external magnetic field. Consequently, the magnetic particles were extracted to a parallel-synchronized train of washing buffer droplets, and unbound reagents were left in an original train of sample droplets. To realize the droplet-based washing function, the following four procedures were sequentially carried in a droplet-based microfluidic device: parallel synchronization of two trains of droplets by using a ladder-like channel network; lateral electrocoalescence by an electric field; magnetic particle manipulation by a magnetic field; and asymmetrical splitting of merged droplets. For the stable droplet synchronization and electrocoalescence, we optimized droplet generation conditions by varying the flow rate ratio (or droplet size). Image analysis was carried out to determine the fluorescent intensity of reagents before and after the washing step. As a result, the unbound reagents in sample droplets were significantly removed by more than a factor of 25 in the single washing step, while the magnetic particles were successfully extracted into washing buffer droplets. As a proof-of-principle, we demonstrate a magnetic particle-based immunoassay with streptavidin-coated magnetic particles and fluorescently labelled biotin in the proposed continuous flow droplet-based microfluidic platform. PMID:25379098

GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.

PubMed

Bakas, Spyridon; Zeng, Ke; Sotiras, Aristeidis; Rathore, Saima; Akbari, Hamed; Gaonkar, Bilwaj; Rozycki, Martin; Pati, Sarthak; Davatzikos, Christos

2016-01-01

We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.

Pairwise domain adaptation module for CNN-based 2-D/3-D registration.

PubMed

Zheng, Jiannan; Miao, Shun; Jane Wang, Z; Liao, Rui

2018-04-01

Accurate two-dimensional to three-dimensional (2-D/3-D) registration of preoperative 3-D data and intraoperative 2-D x-ray images is a key enabler for image-guided therapy. Recent advances in 2-D/3-D registration formulate the problem as a learning-based approach and exploit the modeling power of convolutional neural networks (CNN) to significantly improve the accuracy and efficiency of 2-D/3-D registration. However, for surgery-related applications, collecting a large clinical dataset with accurate annotations for training can be very challenging or impractical. Therefore, deep learning-based 2-D/3-D registration methods are often trained with synthetically generated data, and a performance gap is often observed when testing the trained model on clinical data. We propose a pairwise domain adaptation (PDA) module to adapt the model trained on source domain (i.e., synthetic data) to target domain (i.e., clinical data) by learning domain invariant features with only a few paired real and synthetic data. The PDA module is designed to be flexible for different deep learning-based 2-D/3-D registration frameworks, and it can be plugged into any pretrained CNN model such as a simple Batch-Norm layer. The proposed PDA module has been quantitatively evaluated on two clinical applications using different frameworks of deep networks, demonstrating its significant advantages of generalizability and flexibility for 2-D/3-D medical image registration when a small number of paired real-synthetic data can be obtained.

Generating Reflection and Improving Teacher Pedagogy through the Use of Cognitive Coaching in a Mentor/Beginning Teacher Relationship

ERIC Educational Resources Information Center

Loeschen, Susan

2012-01-01

The purpose of this phenomenological study was to determine if training mentors in the use of the Cognitive Coaching process could effectively facilitate their own critical self-reflection and improve their pedagogy. This study consisted of a criterion-based sample of four teachers in a metropolitan Chicago school district and followed the…

Re-inventing medical work and training: a view from generation X.

PubMed

Skinner, Clare A

2006-07-03

Medical career preferences are changing, with doctors working fewer hours and seeking "work-life balance". There is an urgent need for creative workplace redesign if Australia is to have a sustainable health care system. Postgraduate medical education must adapt to changing medical roles. Curricula should be outcomes-based, should allow flexible delivery, and should consider future workforce needs.

Training Multidisciplinary Biomedical Informatics Students: Three Years of Experience

PubMed Central

van Mulligen, Erik M.; Cases, Montserrat; Hettne, Kristina; Molero, Eva; Weeber, Marc; Robertson, Kevin A.; Oliva, Baldomero; de la Calle, Guillermo; Maojo, Victor

2008-01-01

Objective The European INFOBIOMED Network of Excellence 1 recognized that a successful education program in biomedical informatics should include not only traditional teaching activities in the basic sciences but also the development of skills for working in multidisciplinary teams. Design A carefully developed 3-year training program for biomedical informatics students addressed these educational aspects through the following four activities: (1) an internet course database containing an overview of all Medical Informatics and BioInformatics courses, (2) a BioMedical Informatics Summer School, (3) a mobility program based on a ‘brokerage service’ which published demands and offers, including funding for research exchange projects, and (4) training challenges aimed at the development of multi-disciplinary skills. Measurements This paper focuses on experiences gained in the development of novel educational activities addressing work in multidisciplinary teams. The training challenges described here were evaluated by asking participants to fill out forms with Likert scale based questions. For the mobility program a needs assessment was carried out. Results The mobility program supported 20 exchanges which fostered new BMI research, resulted in a number of peer-reviewed publications and demonstrated the feasibility of this multidisciplinary BMI approach within the European Union. Students unanimously indicated that the training challenge experience had contributed to their understanding and appreciation of multidisciplinary teamwork. Conclusion The training activities undertaken in INFOBIOMED have contributed to a multi-disciplinary BMI approach. It is our hope that this work might provide an impetus for training efforts in Europe, and yield a new generation of biomedical informaticians. PMID:18096914

Implementation of a novel portfolio of structured, curriculum-aligned, simulation-based, cardiothoracic surgery training courses: Evolving the delivery of surgical education.

PubMed

Moorjani, Narain; Lewis, Michael; Shah, Rajesh; Barnard, Sion; Graham, Tim; Rathinam, Sridhar

2017-12-01

The provision of high-quality cardiothoracic surgical training faces many challenges. This has generated an increased interest in simulation-based learning, which can provide a less stressful environment for deliberate practice. We developed a comprehensive, structured program of knowledge and simulation-based learning aligned to the official cardiothoracic surgery curriculum. A portfolio of 10 curriculum-aligned training courses was designed for cardiothoracic surgical trainees during their 6-year training program. The courses were delivered through a multitude of education methods, including live porcine operating simulation models, and were evaluated through a series of quantitative (5-point Likert-scale) and qualitative assessments. The trainees (n = 15-21 per course) also completed pre- and postsession self-confidence and competency levels for each training episode of knowledge and skill, respectively. In addition, board examination pass rates were assessed in the 3-year periods before and after implementation of the courses. Quantitative analysis of the trainees' feedback demonstrated an extremely positive view of the portfolio of the simulation-based training courses with excellent satisfaction scores (out of 5) for teaching sessions (4.44 ± 0.07), faculty (4.64 ± 0.07), content and materials (4.63 ± 0.07), and facilities (4.73 ± 0.05). The courses have shown a significant improvement in the post-self-confidence (7.98 ± 0.13 vs 5.62 ± 0.20, P < .01) and perceived self-competency (8.10 ± 0.10 vs 5.67 ± 0.11, P < .01) scores for all courses. Examination pass rates significantly improved in the 3-year period after attendance at the courses (94.82% ± 2.34% vs 76.26% ± 3.23%, P < .005). This study has described the implementation of the only extensive program of structured simulation-based courses that has been developed to complement clinical training in cardiothoracic surgery. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

Oak Ridge National Laboratory Office of International Nuclear Safeguards: Human Capital Development Activity in FY16

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

Gilligan, Kimberly V.; Gaudet, Rachel N.

In 2007, the U.S. Department of Energy National Nuclear Security Administration (DOE NNSA) Office of Nonproliferation and Arms Control (NPAC) completed a comprehensive review of the current and potential future challenges facing the international safeguards system. One of the report’s key recommendations was for DOE NNSA to launch a major new program to revitalize the international safeguards technology and human resource base. In 2007, at the International Atomic Energy Agency (IAEA) General Conference, then Secretary of Energy Samuel W. Bodman announced the newly created Next Generation Safeguards Initiative (NGSI). NGSI consists of five program elements: policy development and outreach, conceptsmore » and approaches, technology and analytical methodologies, human capital development (HCD), and infrastructure development. This report addresses the HCD component of NGSI. The goal of the HCD component as defined in the NNSA Program Plan is “to revitalize and expand the international safeguards human capital base by attracting and training a new generation of talent.” The major objectives listed in the HCD goal include education and training, outreach to universities and professional societies, postdoctoral appointments, and summer internships at national laboratories.« less

Prediction of a thermodynamic wave train from the monsoon to the Arctic following extreme rainfall events

NASA Astrophysics Data System (ADS)

Krishnamurti, T. N.; Kumar, Vinay

2017-04-01

This study addresses numerical prediction of atmospheric wave trains that provide a monsoonal link to the Arctic ice melt. The monsoonal link is one of several ways that heat is conveyed to the Arctic region. This study follows a detailed observational study on thermodynamic wave trains that are initiated by extreme rain events of the northern summer south Asian monsoon. These wave trains carry large values of heat content anomalies, heat transports and convergence of flux of heat. These features seem to be important candidates for the rapid melt scenario. This present study addresses numerical simulation of the extreme rains, over India and Pakistan, and the generation of thermodynamic wave trains, simulations of large heat content anomalies, heat transports along pathways and heat flux convergences, potential vorticity and the diabatic generation of potential vorticity. We compare model based simulation of many features such as precipitation, divergence and the divergent wind with those evaluated from the reanalysis fields. We have also examined the snow and ice cover data sets during and after these events. This modeling study supports our recent observational findings on the monsoonal link to the rapid Arctic ice melt of the Canadian Arctic. This numerical modeling suggests ways to interpret some recent episodes of rapid ice melts that may require a well-coordinated field experiment among atmosphere, ocean, ice and snow cover scientists. Such a well-coordinated study would sharpen our understanding of this one component of the ice melt, i.e. the monsoonal link, which appears to be fairly robust.

Decision tree and PCA-based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Sun, Weixiang; Chen, Jin; Li, Jiaqing

2007-04-01

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

NASA Astrophysics Data System (ADS)

Kemp, Z. D. C.

2018-04-01

Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.

Enhancing speech recognition using improved particle swarm optimization based hidden Markov model.

PubMed

Selvaraj, Lokesh; Ganesan, Balakrishnan

2014-01-01

Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

Mentoring and Training of Cancer-Related Health Disparities Researchers Committed to Community-Based Participatory Research.

PubMed

Felder, Tisha M; Braun, Kathryn L; Brandt, Heather M; Khan, Samira; Tanjasiri, Sora; Friedman, Daniela B; Armstead, Cheryl A; Okuyemi, Kolawole S; Hébert, James R

2015-01-01

The National Cancer Institute's (NCI) Community Networks Program Centers (CNPCs) provide community-based participatory research (CBPR)-oriented mentoring and training to prepare early-stage/midcareer investigators and student trainees (trainees) in disparities reduction. This paper describes the academic, mentoring, training, and work-life balance experiences of CNPC-affiliated trainees. We used a collaborative and iterative process to develop a 57-item, web-based questionnaire completed by trainees from the 23 CNPCs between August 2012 and February 2013. Their CNPC mentors completed a 47-item questionnaire. Descriptive statistics were calculated. The final analytic sample included 189 of 269 individuals (70%) identified as active participants in CNPC research or training/mentoring. Mentors (n=45) were mostly non-Hispanic White (77.8%) and 48.9% were male. Mentors published a median of 6 (interquartile range [IQR], 3-12) first-authored and 15 (IQR, 6-25) senior authored manuscripts, and secured 15 (IQR, 11-29) grants from the National Institutes of Health (NIH) and other sources in the previous 5 years. Most trainees (n=144) were female (79.2%), 43.7% were underrepresented racial/ethnic minorities, and 36.8% were first-generation college graduates. Over the previous 5 years, trainees reported a median of 4 (IQR, 1-6) publications as first author and 4 (IQR, 2-8) as co-author; 27.1% reported having one or more NIH R01s. Trainees reported satisfaction with their CNPC mentor (79.1%) and confidence in demonstrating most CBPR competencies. The CNPC training program consists of a scientifically productive pool of mentors and trainees. Trainees reported rates of scholarly productivity comparable to other national training programs and provided insights into relationships with mentors, academic pressures, and professional-personal life balance.

Mock ECHO: A Simulation-Based Medical Education Method.

PubMed

Fowler, Rebecca C; Katzman, Joanna G; Comerci, George D; Shelley, Brian M; Duhigg, Daniel; Olivas, Cynthia; Arnold, Thomas; Kalishman, Summers; Monnette, Rebecca; Arora, Sanjeev

2018-04-16

This study was designed to develop a deeper understanding of the learning and social processes that take place during the simulation-based medical education for practicing providers as part of the Project ECHO® model, known as Mock ECHO training. The ECHO model is utilized to expand access to care of common and complex diseases by supporting the education of primary care providers with an interprofessional team of specialists via videoconferencing networks. Mock ECHO trainings are conducted through a train the trainer model targeted at leaders replicating the ECHO model at their organizations. Trainers conduct simulated teleECHO clinics while participants gain skills to improve communication and self-efficacy. Three focus groups, conducted between May 2015 and January 2016 with a total of 26 participants, were deductively analyzed to identify common themes related to simulation-based medical education and interdisciplinary education. Principal themes generated from the analysis included (a) the role of empathy in community development, (b) the value of training tools as guides for learning, (c) Mock ECHO design components to optimize learning, (d) the role of interdisciplinary education to build community and improve care delivery, (e) improving care integration through collaboration, and (f) development of soft skills to facilitate learning. Mock ECHO trainings offer clinicians the freedom to learn in a noncritical environment while emphasizing real-time multidirectional feedback and encouraging knowledge and skill transfer. The success of the ECHO model depends on training interprofessional healthcare providers in behaviors needed to lead a teleECHO clinic and to collaborate in the educational process. While building a community of practice, Mock ECHO provides a safe opportunity for a diverse group of clinician experts to practice learned skills and receive feedback from coparticipants and facilitators.

Application of Artificial Neural Networks in the Design and Optimization of a Nanoparticulate Fingolimod Delivery System Based on Biodegradable Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate).

PubMed

Shahsavari, Shadab; Rezaie Shirmard, Leila; Amini, Mohsen; Abedin Dokoosh, Farid

2017-01-01

Formulation of a nanoparticulate Fingolimod delivery system based on biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) was optimized according to artificial neural networks (ANNs). Concentration of poly(3-hydroxybutyrate-co-3-hydroxyvalerate), PVA and amount of Fingolimod is considered as the input value, and the particle size, polydispersity index, loading capacity, and entrapment efficacy as output data in experimental design study. In vitro release study was carried out for best formulation according to statistical analysis. ANNs are employed to generate the best model to determine the relationships between various values. In order to specify the model with the best accuracy and proficiency for the in vitro release, a multilayer percepteron with different training algorithm has been examined. Three training model formulations including Levenberg-Marquardt (LM), gradient descent, and Bayesian regularization were employed for training the ANN models. It is demonstrated that the predictive ability of each training algorithm is in the order of LM > gradient descent > Bayesian regularization. Also, optimum formulation was achieved by LM training function with 15 hidden layers and 20 neurons. The transfer function of the hidden layer for this formulation and the output layer were tansig and purlin, respectively. Also, the optimization process was developed by minimizing the error among the predicted and observed values of training algorithm (about 0.0341). Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Improving temporal bone dissection using self-directed virtual reality simulation: results of a randomized blinded control trial.

PubMed

Zhao, Yi Chen; Kennedy, Gregor; Yukawa, Kumiko; Pyman, Brian; O'Leary, Stephen

2011-03-01

A significant benefit of virtual reality (VR) simulation is the ability to provide self-direct learning for trainees. This study aims to determine whether there are any differences in performance of cadaver temporal bone dissections between novices who received traditional teaching methods and those who received unsupervised self-directed learning in a VR temporal bone simulator. Randomized blinded control trial. Royal Victorian Eye and Ear Hospital. Twenty novice trainees. After receiving an hour lecture, participants were randomized into 2 groups to receive an additional 2 hours of training via traditional teaching methods or self-directed learning using a VR simulator with automated guidance. The simulation environment presented participants with structured training tasks, which were accompanied by real-time computer-generated feedback as well as real operative videos and photos. After the training, trainees were asked to perform a cortical mastoidectomy on a cadaveric temporal bone. The dissection was videotaped and assessed by 3 otologists blinded to participants' teaching group. The overall performance scores of the simulator-based training group were significantly higher than those of the traditional training group (67% vs 29%; P < .001), with an intraclass correlation coefficient of 0.93, indicating excellent interrater reliability. Using other assessments of performance, such as injury size, the VR simulator-based training group also performed better than the traditional group. This study indicates that self-directed learning on VR simulators can be used to improve performance on cadaver dissection in novice trainees compared with traditional teaching methods alone.

Training and career development in palliative care and end-of-life research: opportunities for development in the U.S.

PubMed

Aziz, Noreen M; Grady, Patricia A; Curtis, J Randall

2013-12-01

There has been a dramatic increase in attention to the field of palliative care and end-of-life (PCEOL) research over the past 20 years. This increase is particularly notable in the development of palliative care clinical and educational programs. However, there remain important shortcomings in the evidence base to ensure access to and delivery of effective palliative care for patients with life-limiting illness and their families. Development of this evidence base will require that we train the next generation of researchers to focus on issues in PCEOL. The purpose of this article was to explore the current status of the recruitment, training, and retention of future investigators in PCEOL research in the U.S. and propose recommendations to move us forward. Some key contextual issues for developing and supporting this research workforce are articulated, along with timely and important research areas that will need to be addressed during research training and career development. We provide targeted key recommendations to facilitate the nurturing and support of the future research workforce that is needed to ensure the development and implementation of the science necessary for providing high-quality, evidence-based palliative care to all who need and desire it. Copyright © 2013 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved.

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

Training public health superheroes: five talents for public health leadership.

PubMed

Day, Matthew; Shickle, Darren; Smith, Kevin; Zakariasen, Ken; Moskol, Jacob; Oliver, Thomas

2014-12-01

Public health leaders have been criticized for their policy stances, relationships with governments and failure to train the next generation. New approaches to the identification and training of public health leaders may be required. To inform these, lessons can be drawn from public health 'superheroes'; public health leaders perceived to be the most admired and effective by their peers. Members and Fellows of the UK Faculty of Public Health were contacted via e-newsletter and magazine and asked to nominate their 'Public Health Superhero'. Twenty-six responses were received, nominating 40 different people. Twelve semi-structured interviews were conducted. Thematic analysis, based on 'grounded theory', was conducted. Five leadership 'talents' for public health were identified: mentoring-nurturing, shaping-organizing, networking-connecting, knowing-interpreting and advocating-impacting. Talent-based approaches have been effective for leadership development in other sectors. These talents are the first specific to the practice of public health and align with some aspects of existing frameworks. An increased focus on identifying and developing talents during public health training, as opposed to 'competency'-based approaches, may be effective in strengthening public health leadership. Further research to understand the combination and intensity of talents across a larger sample of public health leaders is required. © The Author 2014. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Helpers Program: A Pilot Test of Brief Tobacco Intervention Training in Three Corporations

PubMed Central

Muramoto, Myra L.; Wassum, Ken; Connolly, Tim; Matthews, Eva; Floden, Lysbeth

2014-01-01

Background Quitlines and worksite-sponsored cessation programs are effective and highly accessible, but limited by low utilization. Efforts to encourage use of cessation aids have focused almost exclusively on the smoker, overlooking the potential for friends, family, coworkers and others in a tobacco user’s social network to influence quitting and use of effective treatment. Methods Longitudinal, observational pilot feasibility study with six-week follow-up survey. Setting/Participants Employees of three national corporations, with a combined target audience of 102,100 employees. Intervention The Helpers Program offers Web-based brief intervention (BI) training to activate social networks of tobacco users to encourage quitting and use of effective treatment. Helpers was offered from 1/10/08 to 3/31/08, as a treatment engagement strategy, together with Free and Clear’s (F&C) telephone/Web-based cessation services. Main outcome measures web-site utilization, training completion, post-training changes in knowledge and self-efficacy with delivery of BIs, referrals to F&C, and use of BI training. Results There were 19,109 unique visitors to the Helpers Web-site. Of these, 4727 created user accounts; 1427 registered for Helpers Training; 766 completed training. There were 445 visits to the referral page and 201 e-mail or letter referrals generated. There were 67 requests for technical support. Of follow-up survey respondents (n=289), 78.9% reported offering a BI. Conclusions Offering the Helpers Program Web-site to a large, diverse audience as part of an employer-sponsored worksite health promotion program is both feasible and well accepted by employees. Website users will participate in training, encourage quitting, and refer smokers to quitline services. PMID:20176303

Development of a diabetes care management curriculum in a family practice residency program.

PubMed

Nuovo, Jim; Balsbaugh, Thomas; Barton, Sue; Davidson, Ellen; Fox-Garcia, Jane; Gandolfo, Angela; Levich, Bridget; Seibles, Joann

2004-01-01

Improving the quality of care for patients with chronic illness has become a high priority. Implementing training programs in disease management (DM) so the next generation of physicians can manage chronic illness more effectively is challenging. Residency training programs have no specific mandate to implement DM training. Additional barriers at the training facility include: 1) lack of a population-based perspective for service delivery; 2) weak support for self-management of illness; 3) incomplete implementation due to physician resistance or inertia; and 4) few incentives to change practices and behaviors. In order to overcome these barriers, training programs must take the initiative to implement DM training that addresses each of these issues. We report the implementation of a chronic illness management curriculum based on the Improving Chronic Illness Care (ICIC) Model. Features of this process included both patient care and learner objectives. These were: development of a multidisciplinary diabetes DM team; development of a patient registry; development of diabetes teaching clinics in the family practice center (nutrition, general management classes, and one-on-one teaching); development of a group visit model; and training the residents in the elements of the ICIC Model, ie, the community, the health system, self-management support, delivery system design, decision support, and clinical information systems. Barriers to implementing these curricular changes were: the development of a patient registry; buy-in from faculty, residents, clinic leadership, staff, and patients for the chronic care model; the ability to bill for services and maintain clinical productivity; and support from the health system key stakeholders for sustainability. Unique features of each training site will dictate differences in emphasis and structure; however, the core principles of the ICIC Model in enhancing self-management may be generalized to all sites.

Human movement training with a cable driven ARm EXoskeleton (CAREX).

PubMed

Mao, Ying; Jin, Xin; Gera Dutta, Geetanjali; Scholz, John P; Agrawal, Sunil K

2015-01-01

In recent years, the authors have proposed lightweight exoskeleton designs for upper arm rehabilitation using multi-stage cable-driven parallel mechanism. Previously, the authors have demonstrated via experiments that it is possible to apply "assist-as-needed" forces in all directions at the end-effector with such an exoskeleton acting on an anthropomorphic machine arm. A human-exoskeleton interface was also presented to show the feasibility of CAREX on human subjects. The goals of this paper are to 1) further address issues when CAREX is mounted on human subjects, e.g., generation of continuous cable tension trajectories 2) demonstrate the feasibility and effectiveness of CAREX on movement training of healthy human subjects and a stroke patient. In this research, CAREX is rigidly attached to an arm orthosis worn by human subjects. The cable routing points are optimized to achieve a relatively large "tensioned" static workspace. A new cable tension planner based on quadratic programming is used to generate continuous cable tension trajectory for smooth motion. Experiments were carried out on eight healthy subjects. The experimental results show that CAREX can help the subjects move closer to a prescribed circular path using the force fields generated by the exoskeleton. The subjects also adapt to the path shortly after training. CAREX was also evaluated on a stroke patient to test the feasibility of its use on patients with neural impairment. The results show that the patient was able to move closer to a prescribed straight line path with the "assist-as-needed" force field.

Customizing a rangefinder for community-based wildlife conservation initiatives

USGS Publications Warehouse

Ransom, Jason I.

2011-01-01

Population size of many threatened and endangered species is relatively unknown because estimating animal abundance in remote parts of the world, without access to aircraft for surveying vast areas, is a scientific challenge with few proposed solutions. One option is to enlist local community members and train them in data collection for large line transect or point count surveys, but financial and sometimes technological constraints prevent access to the necessary equipment and training for accurately quantifying distance measurements. Such measurements are paramount for generating reliable estimates of animal density. This problem was overcome in a survey of Asiatic wild ass (Equus hemionus) in the Great Gobi B Strictly Protected Area, Mongolia, by converting an inexpensive optical sporting rangefinder into a species-specific rangefinder with visual-based categorical labels. Accuracy trials concluded 96.86% of 350 distance measures matched those from a laser rangefinder. This simple customized optic subsequently allowed for a large group of minimally-trained observers to simultaneously record quantitative measures of distance, despite language, education, and skill differences among the diverse group. The large community-based effort actively engaged local residents in species conservation by including them as the foundation for collecting scientific data.

The Multidisciplinary Translational Team (MTT) Model for Training and Development of Translational Research Investigators.

PubMed

Ameredes, Bill T; Hellmich, Mark R; Cestone, Christina M; Wooten, Kevin C; Ottenbacher, Kenneth J; Chonmaitree, Tasnee; Anderson, Karl E; Brasier, Allan R

2015-10-01

Multiinstitutional research collaborations now form the most rapid and productive project execution structures in the health sciences. Effective adoption of a multidisciplinary team research approach is widely accepted as one mechanism enabling rapid translation of new discoveries into interventions in human health. Although the impact of successful team-based approaches facilitating innovation has been well-documented, its utility for training a new generation of scientists has not been thoroughly investigated. We describe the characteristics of how multidisciplinary translational teams (MTTs) promote career development of translational research scholars through competency building, interprofessional integration, and team-based mentoring approaches. Exploratory longitudinal and outcome assessments from our experience show that MTT membership had a positive effect on the development of translational research competencies, as determined by a self-report survey of 32 scholars. We also observed that all trainees produced a large number of collaborative publications that appeared to be associated with their CTSA association and participation with MTTs. We conclude that the MTT model provides a unique training environment for translational and team-based learning activities, for investigators at early stages of career development. © 2015 Wiley Periodicals, Inc.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.

PubMed

Li, Yongcheng; Sun, Rong; Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.

NIRCam/NGST Education and Public Outreach: ``Linking Girls with the Sky"

NASA Astrophysics Data System (ADS)

McCarthy, D. W., Jr.; Lebofsky, L. A.; Slater, T. F.; Rieke, M. J.; Pompea, S. M.

2002-09-01

Astronomical images can inspire a new generation. The clarity of the Next Generation Space Telescope (NGST), combined with the near-infrared camera's (NIRCam) ability to see farther back in time and through murky regions of space, may unveil the ``First Light" from a newborn Universe and the origins of planetary systems. The NIRCam science team, led by Dr. Marcia Rieke, unites scientists from across the U.S., Canada, and Lockheed Martin's Advanced Technology Center with prominent science educators. The E/PO program especially targets K-14 girls in a partnership with the Girl Scouts of the USA, to address such specific needs as (1) the review of existing badge programs for younger girls, (2) new, community-based activities and research experiences for older girls, (3) interaction experiences in person and on-line with inspiring mentors and role-models, and (4) leadership and training experiences for adult trainers. New activities will be inquiry-based and appropriate in both formal and informal settings. They will also used for training future teachers of science. Topics such as ``Light pollution" can be related thematically to such NGST concepts as a ``low thermal background". The Astronomy Camp facilities on historic Mt. Lemmon will be used to ``train the trainers" by providing Girl Scouts and their adult leaders hands-on experiences with 8- to 60-inch telescopes, CCD and infrared cameras, and image processing techniques. NIRCam scientists will also be involved in developing authentic research-based projects using NIRCam datasets for in-class use by middle and high school teachers. The NIRCam E/PO program is funded by NASA under prime contract, NAS502105, with Goddard Space Flight Center to The University of Arizona.

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

PubMed Central

Wang, Yuechao; Li, Hongyi; Zheng, Xiongfei

2016-01-01

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks. PMID:27806074

Functional impacts of exoskeleton-based rehabilitation in chronic stroke: multi-joint versus single-joint robotic training

PubMed Central

2013-01-01

Stroke is a major cause of disability in the world. The activities of upper limb segments are often compromised following a stroke, impairing most daily tasks. Robotic training is now considered amongst the rehabilitation methods applied to promote functional recovery. However, the implementation of robotic devices remains a major challenge for the bioengineering and clinical community. Latest exoskeletons with multiple degrees of freedom (DOF) may become particularly attractive, because of their low apparent inertia, the multiple actuators generating large torques, and the fact that patients can move the arm in the normal wide workspace. A recent study published in JNER by Milot and colleagues underlines that training with a 6-DOF exoskeleton impacts positively on motor function in patients being in stable phase of recovery after a stroke. Also, multi-joint robotic training was not found to be superior to single-joint robotic training. Although it is often considered that rehabilitation should start from simple movements to complex functional movements as the recovery evolves, this study challenges this widespread notion whose scientific basis has remained uncertain. PMID:24354518

Statistical technique for analysing functional connectivity of multiple spike trains.

PubMed

Masud, Mohammad Shahed; Borisyuk, Roman

2011-03-15

A new statistical technique, the Cox method, used for analysing functional connectivity of simultaneously recorded multiple spike trains is presented. This method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an "influence function" is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages: it does not use bins (binless method); it is applicable to cases where the sample size is small; it is sufficiently sensitive such that it estimates weak influences; it supports the simultaneous analysis of multiple influences; it is able to identify a correct connectivity scheme in difficult cases of "common source" or "indirect" connectivity. The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections. The results suggest that this method is highly successful for analysing functional connectivity of simultaneously recorded multiple spike trains. Copyright © 2011 Elsevier B.V. All rights reserved.

Innovative web-based multimedia curriculum improves cardiac examination competency of residents.

PubMed

Criley, Jasminka M; Keiner, Jennifer; Boker, John R; Criley, Stuart R; Warde, Carole M

2008-03-01

Proper diagnosis of cardiac disorders is a core competency of internists. Yet numerous studies have documented that the cardiac examination (CE) skills of physicians have declined compared with those of previous generations of physicians, attributed variously to inadequate exposure to cardiac patients and lack of skilled bedside teaching. With growing concerns about ensuring patient safety and quality of care, public and professional organizations are calling for a renewed emphasis on the teaching and evaluation of clinical skills in residency training. The objective of the study was to determine whether Web training improves CE competency, whether residents retain what they learn, and whether a Web-based curriculum plus clinical training is better than clinical training alone. Journal of Hospital Medicine 2008;3:124-133. (c) 2008 Society of Hospital Medicine. This was a controlled intervention study. The intervention group (34 internal and family medicine interns) participated in self-directed use of a Web-based tutorial and three 1-hour teaching sessions taught by a hospitalist. Twenty-five interns from the prior year served as controls. We assessed overall CE competency and 4 subcategories of CE competency: knowledge, audio skills, visual skills, and audio-visual integration. The over mean score of the intervention group significantly improved, from 54 to 66 (P = .002). This improvement was retained (63.5, P = .05). When compared with end-of-year controls, the intervention group had significantly higher end-of-year CE scores (57 vs. 63.5, P = .05), knowledge (P = .04), and audio skills (P = .01). At the end of the academic year, all improvements were retained (P

An integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features.

PubMed

Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup

2017-07-25

Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

Effects of 6-week sling-based training of the external-rotator muscles on the shoulder profile in elite female high school handball players.

PubMed

Genevois, Cyril; Berthier, Philippe; Guidou, Vincent; Muller, Franck; Thiebault, Boris; Rogowski, Isabelle

2014-11-01

In women's handball, the large numbers of throws and passes make the shoulder region vulnerable to overuse injuries. Repetitive throwing motions generate imbalance between shoulder internal- and external-rotator muscles. It has not yet been established whether sling-based training can improve shoulder external-rotator muscle strength. This study investigated the effectiveness of a 6-wk strengthening program in improving shoulder functional profile in elite female high school handball players. Crossover study. National elite handball training center. 25 elite female high school handball players. The program, completed twice per week for 6 wk, included sling-based strengthening exercises using a suspension trainer for external rotation with scapular retraction and scapular retraction alone. Maximal shoulder external- and internal-rotation strength, shoulder external- and internal-rotation range of motion (ROM), and maximal throwing velocity were assessed preintervention and postintervention for dominant and nondominant sides. After sling training, external- and internal-rotation strength increased significantly for both sides (P ≤ .001, and P = .004, respectively), with the result that there was no significant change in external- and internal-rotation strength ratios for either the dominant or the nondominant shoulder. No significant differences were observed for external-rotation ROM, while internal-rotation ROM decreased moderately, in particular in the dominant shoulder (P = .005). Maximal throwing velocity remained constant for the dominant arm, whereas a significant increase was found for the nondominant arm (P = .017). This 6-wk strengthening program was effective in improving shoulder external-rotator muscle strength but resulted in a decrease in the ROM in shoulder internal rotation, while throwing velocity remained stable. Adding a stretching program to this type of sling-based training program might help avoid potential detrimental effects on shoulder ROM.

Attention Training and the Threat Bias: An ERP Study

PubMed Central

O’Toole, Laura; Dennis, Tracy A.

2011-01-01

Anxiety is characterized by exaggerated attention to threat. Several studies suggest that this threat bias plays a causal role in the development and maintenance of anxiety disorders. Furthermore, although the threat bias can be reduced in anxious individuals and induced in non-anxious individual, the attentional mechanisms underlying these changes remain unclear. To address this issue, 49 non-anxious adults were randomly assigned to either attentional training toward or training away from threat using a modified version of the dot probe task. Behavioral measures of attentional biases were also generated pre- and post-training using the dot probe task. Event-related potentials (ERPs) were generated to threat and non-threat face pairs and probes during pre- and post-training assessments. Effects of training on behavioral measures of the threat bias were significant, but only for those participants showing pre-training biases. Attention training also influenced early spatial attention, as measured by post-training P1 amplitudes to cues. Results illustrate the importance of taking pre-training attention biases in non-anxious individuals into account when evaluating the effects of attention training and tracking physiological changes in attention following training. PMID:22083026

Are we training pit bulls to review our manuscripts?

PubMed

Walbot, Virginia

2009-01-01

Good early training of graduate students and postdocs is needed to prevent them turning into future generations of manuscript-savaging reviewers. How can we intercalate typical papers into our training?

SU-G-JeP3-09: Tumor Location Prediction Using Natural Respiratory Volume for Respiratory Gated Radiation Therapy (RGRT): System Verification Study

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

Kim, M; Jung, J; Yoon, D

Purpose: Respiratory gated radiation therapy (RGRT) gives accurate results when a patient’s breathing is stable and regular. Thus, the patient should be fully aware during respiratory pattern training before undergoing the RGRT treatment. In order to bypass the process of respiratory pattern training, we propose a target location prediction system for RGRT that uses only natural respiratory volume, and confirm its application. Methods: In order to verify the proposed target location prediction system, an in-house phantom set was used. This set involves a chest phantom including target, external markers, and motion generator. Natural respiratory volume signals were generated using themore » random function in MATLAB code. In the chest phantom, the target takes a linear motion based on the respiratory signal. After a four-dimensional computed tomography (4DCT) scan of the in-house phantom, the motion trajectory was derived as a linear equation. The accuracy of the linear equation was compared with that of the motion algorithm used by the operating motion generator. In addition, we attempted target location prediction using random respiratory volume values. Results: The correspondence rate of the linear equation derived from the 4DCT images with the motion algorithm of the motion generator was 99.41%. In addition, the average error rate of target location prediction was 1.23% for 26 cases. Conclusion: We confirmed the applicability of our proposed target location prediction system for RGRT using natural respiratory volume. If additional clinical studies can be conducted, a more accurate prediction system can be realized without requiring respiratory pattern training.« less

The Development of Dispatcher Training Simulator in a Thermal Energy Generation System

NASA Astrophysics Data System (ADS)

Hakim, D. L.; Abdullah, A. G.; Mulyadi, Y.; Hasan, B.

2018-01-01

A dispatcher training simulator (DTS) is a real-time Human Machine Interface (HMI)-based control tool that is able to visualize industrial control system processes. The present study was aimed at developing a simulator tool for boilers in a thermal power station. The DTS prototype was designed using technical data of thermal power station boilers in Indonesia. It was then designed and implemented in Wonderware Intouch 10. The resulting simulator came with component drawing, animation, control display, alarm system, real-time trend, historical trend. This application used 26 tagnames and was equipped with a security system. The test showed that the principles of real-time control worked well. It is expected that this research could significantly contribute to the development of thermal power station, particularly in terms of its application as a training simulator for beginning dispatchers.

Generative Modeling for Machine Learning on the D-Wave

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

Thulasidasan, Sunil

These are slides on Generative Modeling for Machine Learning on the D-Wave. The following topics are detailed: generative models; Boltzmann machines: a generative model; restricted Boltzmann machines; learning parameters: RBM training; practical ways to train RBM; D-Wave as a Boltzmann sampler; mapping RBM onto the D-Wave; Chimera restricted RBM; mapping binary RBM to Ising model; experiments; data; D-Wave effective temperature, parameters noise, etc.; experiments: contrastive divergence (CD) 1 step; after 50 steps of CD; after 100 steps of CD; D-Wave (experiments 1, 2, 3); D-Wave observations.

Apparatus for millimeter-wave signal generation

DOEpatents

Vawter, G. Allen; Hietala, Vincent M.; Zolper, John C.; Mar, Alan; Hohimer, John P.

1999-01-01

An opto-electronic integrated circuit (OEIC) apparatus is disclosed for generating an electrical signal at a frequency .gtoreq.10 GHz. The apparatus, formed on a single substrate, includes a semiconductor ring laser for generating a continuous train of mode-locked lasing pulses and a high-speed photodetector for detecting the train of lasing pulses and generating the electrical signal therefrom. Embodiments of the invention are disclosed with an active waveguide amplifier coupling the semiconductor ring laser and the high-speed photodetector. The invention has applications for use in OEICs and millimeter-wave monolithic integrated circuits (MMICs).

Coherent Transition Radiation Generated from Transverse Electron Density Modulation

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

Halavanau, A.; Piot, P.; Tyukhtin, A. V.

Coherent Transition radiation (CTR) of a given frequency is commonly generated with longitudinal electron bunch trains. In this paper, we present a study of CTR properties produced from simultaneous electron transverse and longitudinal density modulation. We demonstrate via numerical simulations a simple technique to generate THz-scale frequencies from mm-scale transversely separated electron beamlets formed into a ps-scale bunch train. The results and a potential experimental setup are discussed.

Adaptive rehabilitation games.

PubMed

Barzilay, Ouriel; Wolf, Alon

2013-02-01

In conventional neuromuscular rehabilitation, patients are required to perform biomechanical exercises to recover their neuromotor abilities. These physiotherapeutic tasks are defined by the physiotherapist, according to his estimate of the patient's pathologic neuromotor function. The definition of the task is mainly qualitative and it is often merely demonstrated to the patient as a gesture to reproduce. Success of the treatment relies then on the accuracy and repetition of the motor training. We propose a novel approach to neuromotor training by combining the advantages of a virtual reality platform with biofeedback information on the training subject from biometric equipment and with the computational power of artificial neural networks. In a calibration stage, the subject performs motor training on a known task to train the network. Once trained, the tuned network generates a new patient-specific task, based on the definition of the subject's expected performance dictated by the therapist. The system was tested for upper limb rehabilitation on healthy subjects. We measured a 33% improvement in the triceps performance (p = 0.027). The novelty of the proposed approach lies in its use of learning systems to the estimation of biological models. Copyright © 2012 Elsevier Ltd. All rights reserved.

Tomography and generative training with quantum Boltzmann machines

NASA Astrophysics Data System (ADS)

Kieferová, Mária; Wiebe, Nathan

2017-12-01

The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has made their development an aspirational goal for quantum machine learning and quantum computing in general. Here we provide methods of training quantum Boltzmann machines. Our work generalizes existing methods and provides additional approaches for training quantum neural networks that compare favorably to existing methods. We further demonstrate that quantum Boltzmann machines enable a form of partial quantum state tomography that further provides a generative model for the input quantum state. Classical Boltzmann machines are incapable of this. This verifies the long-conjectured connection between tomography and quantum machine learning. Finally, we prove that classical computers cannot simulate our training process in general unless BQP=BPP , provide lower bounds on the complexity of the training procedures and numerically investigate training for small nonstoquastic Hamiltonians.

Technology to Support Motivational Interviewing.

PubMed

Gance-Cleveland, Bonnie; Ford, Loretta C; Aldrich, Heather; Oetzel, Keri Bolton; Cook, Paul; Schmiege, Sarah; Wold, Mary

This paper reports the findings of motivational interviewing (MI) training with and without technology support on school-based health center (SBHC) providers' satisfaction with MI training, providers' self-report of behavioral counseling related to childhood overweight/obesity, and parents' perception of care after training. The effects of training and technology on MI is part of a larger comparative effectiveness, cluster randomized trial. Twenty-four SBHCs in six states received virtual training on MI. Half the sites received HeartSmartKids™, a bilingual (English/Spanish), decision-support technology. The technology generated tailored patient education materials. Standard growth charts were plotted and health risks were highlighted to support MI counseling. The results of the MI training included provider satisfaction with MI training and parent assessment of the components of MI in their child's care. Providers and parents were surveyed at baseline, after training, and six months after training. Providers were satisfied with training and reported improvements in counseling proficiency (p<0.0007) and psychological/emotional assessment (p=0.0004) after training. Parents in the technology group reported significant improvement in provider support for healthy eating (p=0.04). Virtual training has the potential of preparing providers to use MI to address childhood obesity. Technology improved parent support for healthy eating. Future research should evaluate the impact of technology to support MI on patient outcomes. Childhood obesity guidelines emphasize that MI should be used to promote healthy weight in children. Training providers on MI may help more providers incorporate obesity guidelines in their practice. Copyright © 2017 Elsevier Inc. All rights reserved.

Superconducting Qubit with Integrated Single Flux Quantum Controller Part II: Experimental Characterization

NASA Astrophysics Data System (ADS)

Leonard, Edward, Jr.; Beck, Matthew; Thorbeck, Ted; Zhu, Shaojiang; Howington, Caleb; Nelson, Jj; Plourde, Britton; McDermott, Robert

We describe the characterization of a single flux quantum (SFQ) pulse generator cofabricated with a superconducting quantum circuit on a single chip. Resonant trains of SFQ pulses are used to induce coherent qubit rotations on the Bloch sphere. We describe the SFQ drive characteristics of the qubit at the fundamental transition frequency and at subharmonics (ω01 / n , n = 2 , 3 , 4 , ⋯). We address the issue of quasiparticle poisoning due to the proximal SFQ pulse generator, and we characterize the fidelity of SFQ-based rotations using randomized benchmarking. Present address: IBM T.J. Watson Research Center.

Self-Motion Perception: Assessment by Real-Time Computer Generated Animations

NASA Technical Reports Server (NTRS)

Parker, Donald E.

1999-01-01

Our overall goal is to develop materials and procedures for assessing vestibular contributions to spatial cognition. The specific objective of the research described in this paper is to evaluate computer-generated animations as potential tools for studying self-orientation and self-motion perception. Specific questions addressed in this study included the following. First, does a non- verbal perceptual reporting procedure using real-time animations improve assessment of spatial orientation? Are reports reliable? Second, do reports confirm expectations based on stimuli to vestibular apparatus? Third, can reliable reports be obtained when self-motion description vocabulary training is omitted?

Where is Leadership Training Being Taught in U.S. Dental Schools

PubMed Central

Taichman, Russell S.; Parkinson, Joseph W.

2013-01-01

Leadership is vital in all professions and organizations. Our purpose was to determine where in dental schools leadership is taught, and to what degree it is emphasized so that we could establish a base line from which to generate recommendations for best practices. Therefore we surveyed all US Deans of Academic Affairs in Dental Schools to determine where in the curriculum leadership is taught and emphasized. Our results showed that leadership training is delivered in many different parts of the curriculum, and at various levels. Generally, respondents indicated that leadership education is delivered either in the setting of practice management, community outreach or in public health settings. In some cases, specific training programs are dedicated specifically to leadership development. Thus several models for leadership development were identified showing design and flexibility to address regional and national needs. In the future it would be of value to assess the effectiveness of the different models and whether single or multiple pathways for leadership training are most beneficial. PMID:22659699

Forensic facial comparison in South Africa: State of the science.

PubMed

Steyn, M; Pretorius, M; Briers, N; Bacci, N; Johnson, A; Houlton, T M R

2018-06-01

Forensic facial comparison (FFC) is a scientific technique used to link suspects to a crime scene based on the analysis of photos or video recordings from that scene. While basic guidelines on practice and training are provided by the Facial Identification Scientific Working Group, details of how these are applied across the world are scarce. FFC is frequently used in South Africa, with more than 700 comparisons conducted in the last two years alone. In this paper the standards of practice are outlined, with new proposed levels of agreement/conclusions. We outline three levels of training that were established, with training in facial anatomy, terminology, principles of image comparison, image science, facial recognition and computer skills being aimed at developing general competency. Training in generating court charts and understanding court case proceedings are being specifically developed for the South African context. Various shortcomings still exist, specifically with regard to knowledge of the reliability of the technique. These need to be addressed in future research. Copyright © 2018 Elsevier B.V. All rights reserved.

Advanced manned space flight simulation and training: An investigation of simulation host computer system concepts

NASA Technical Reports Server (NTRS)

Montag, Bruce C.; Bishop, Alfred M.; Redfield, Joe B.

1989-01-01

The findings of a preliminary investigation by Southwest Research Institute (SwRI) in simulation host computer concepts is presented. It is designed to aid NASA in evaluating simulation technologies for use in spaceflight training. The focus of the investigation is on the next generation of space simulation systems that will be utilized in training personnel for Space Station Freedom operations. SwRI concludes that NASA should pursue a distributed simulation host computer system architecture for the Space Station Training Facility (SSTF) rather than a centralized mainframe based arrangement. A distributed system offers many advantages and is seen by SwRI as the only architecture that will allow NASA to achieve established functional goals and operational objectives over the life of the Space Station Freedom program. Several distributed, parallel computing systems are available today that offer real-time capabilities for time critical, man-in-the-loop simulation. These systems are flexible in terms of connectivity and configurability, and are easily scaled to meet increasing demands for more computing power.

Novel transformation-based response prediction of shear building using interval neural network

NASA Astrophysics Data System (ADS)

Chakraverty, S.; Sahoo, Deepti Moyi

2017-04-01

Present paper uses powerful technique of interval neural network (INN) to simulate and estimate structural response of multi-storey shear buildings subject to earthquake motion. The INN is first trained for a real earthquake data, viz., the ground acceleration as input and the numerically generated responses of different floors of multi-storey buildings as output. Till date, no model exists to handle positive and negative data in the INN. As such here, the bipolar data in [ -1, 1] are converted first to unipolar form, i.e., to [0, 1] by means of a novel transformation for the first time to handle the above training patterns in normalized form. Once the training is done, again the unipolar data are converted back to its bipolar form by using the inverse transformation. The trained INN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by INN model are good for practical purposes.

Shared virtual environments for aerospace training

NASA Technical Reports Server (NTRS)

Loftin, R. Bowen; Voss, Mark

1994-01-01

Virtual environments have the potential to significantly enhance the training of NASA astronauts and ground-based personnel for a variety of activities. A critical requirement is the need to share virtual environments, in real or near real time, between remote sites. It has been hypothesized that the training of international astronaut crews could be done more cheaply and effectively by utilizing such shared virtual environments in the early stages of mission preparation. The Software Technology Branch at NASA's Johnson Space Center has developed the capability for multiple users to simultaneously share the same virtual environment. Each user generates the graphics needed to create the virtual environment. All changes of object position and state are communicated to all users so that each virtual environment maintains its 'currency.' Examples of these shared environments will be discussed and plans for the utilization of the Department of Defense's Distributed Interactive Simulation (DIS) protocols for shared virtual environments will be presented. Finally, the impact of this technology on training and education in general will be explored.

Contemporary social network sites: Relevance in anesthesiology teaching, training, and research

PubMed Central

Haldar, Rudrashish; Kaushal, Ashutosh; Samanta, Sukhen; Ambesh, Paurush; Srivastava, Shashi; Singh, Prabhat K.

2016-01-01

Objective: The phenomenal popularity of social networking sites has been used globally by medical professionals to boost professional associations and scientific developments. They have tremendous potential to forge professional liaisons, generate employment,upgrading skills and publicizing scientific achievements. We highlight the role of social networking mediums in influencing teaching, training and research in anaesthesiology. Background: The growth of social networking sites have been prompted by the limitations of previous facilities in terms of ease of data and interface sharing and the amalgamation of audio visual aids on common platforms in the newer facilities. Review: Contemporary social networking sites like Facebook, Twitter, Tumblr,Linkedn etc and their respective features based on anaesthesiology training or practice have been discussed. A host of advantages which these sites confer are also discussed. Likewise the potential pitfalls and drawbacks of these facilities have also been addressed. Conclusion: Social networking sites have immense potential for development of training and research in Anaesthesiology. However responsible and cautious utilization is advocated. PMID:27625491

Contemporary social network sites: Relevance in anesthesiology teaching, training, and research.

PubMed

Haldar, Rudrashish; Kaushal, Ashutosh; Samanta, Sukhen; Ambesh, Paurush; Srivastava, Shashi; Singh, Prabhat K

2016-01-01

The phenomenal popularity of social networking sites has been used globally by medical professionals to boost professional associations and scientific developments. They have tremendous potential to forge professional liaisons, generate employment,upgrading skills and publicizing scientific achievements. We highlight the role of social networking mediums in influencing teaching, training and research in anaesthesiology. The growth of social networking sites have been prompted by the limitations of previous facilities in terms of ease of data and interface sharing and the amalgamation of audio visual aids on common platforms in the newer facilities. Contemporary social networking sites like Facebook, Twitter, Tumblr,Linkedn etc and their respective features based on anaesthesiology training or practice have been discussed. A host of advantages which these sites confer are also discussed. Likewise the potential pitfalls and drawbacks of these facilities have also been addressed. Social networking sites have immense potential for development of training and research in Anaesthesiology. However responsible and cautious utilization is advocated.

Network-centric decision architecture for financial or 1/f data models

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Massey, Stoney; Case, Carl T.; Songy, Claude G.

2002-12-01

This paper presents a decision architecture algorithm for training neural equation based networks to make autonomous multi-goal oriented, multi-class decisions. These architectures make decisions based on their individual goals and draw from the same network centric feature set. Traditionally, these architectures are comprised of neural networks that offer marginal performance due to lack of convergence of the training set. We present an approach for autonomously extracting sample points as I/O exemplars for generation of multi-branch, multi-node decision architectures populated by adaptively derived neural equations. To test the robustness of this architecture, open source data sets in the form of financial time series were used, requiring a three-class decision space analogous to the lethal, non-lethal, and clutter discrimination problem. This algorithm and the results of its application are presented here.

eLearning in education and advanced training in neuroradiology: introduction of a web-based teaching and learning application.

PubMed

Zajaczek, J E W; Götz, F; Kupka, T; Behrends, M; Haubitz, B; Donnerstag, F; Rodt, T; Walter, G F; Matthies, H K; Becker, H

2006-09-01

New information technologies offer the possibility of major improvements in the professional education and advanced training of physicians. The web-based, multimedia teaching and learning application Schoolbook has been created and utilized for neuroradiology. Schoolbook is technically based as a content management system and is realized in a LAMP environment. The content is generated with the help of the developed system and stored in a database. The layout is defined by a PHP application, and the webpages are generated from the system. Schoolbook is realized as an authoring tool so that it can be integrated into daily practice. This enables the teacher to autonomously process the content into the web-based application which is used for lectures, seminars and self-study. A multimedia case library is the central building block of Schoolbook for neuroradiology, whereby the learner is provided with original diagnostic and therapeutic data from numerous individual cases. The user can put individual emphasis on key learning points as there are various ways to work with the case histories. Besides the case-based way of teaching and learning, a systematically structured way of dealing with the content is available. eLearning offers various opportunities for teaching and learning in academic and scientific as well as in economic contexts. Web-based applications such as Schoolbook may be beneficial not only for basic university education but also for the realization of international educational programmes such as the European Master of Medical Science with a major in neuroradiology.

Improving Patient Safety through Simulation Training in Anesthesiology: Where Are We?

PubMed Central

2016-01-01

There have been colossal technological advances in the use of simulation in anesthesiology in the past 2 decades. Over the years, the use of simulation has gone from low fidelity to high fidelity models that mimic human responses in a startlingly realistic manner, extremely life-like mannequin that breathes, generates E.K.G, and has pulses, heart sounds, and an airway that can be programmed for different degrees of obstruction. Simulation in anesthesiology is no longer a research fascination but an integral part of resident education and one of ACGME requirements for resident graduation. Simulation training has been objectively shown to increase the skill-set of anesthesiologists. Anesthesiology is leading the movement in patient safety. It is rational to assume a relationship between simulation training and patient safety. Nevertheless there has not been a demonstrable improvement in patient outcomes with simulation training. Larger prospective studies that evaluate the improvement in patient outcomes are needed to justify the integration of simulation training in resident education but ample number of studies in the past 5 years do show a definite benefit of using simulation in anesthesiology training. This paper gives a brief overview of the history and evolution of use of simulation in anesthesiology and highlights some of the more recent studies that have advanced simulation-based training. PMID:26949389

Video Games as a Training Tool to Prepare the Next Generation of Cyber Warriors

DTIC Science & Technology

2014-10-01

2. REPORT TYPE N/A 3. DATES COVERED - 4 . TITLE AND SUBTITLE Video Games as a Training Tool to Prepare the Next Generation of Cyber Warriors...CYBERSECURITY WORKFORCE SHORTAGE .......................................................................... 3 4 1.1 GREATER CYBERSECURITY EDUCATION IS... 4 6 2.1 HOW VIDEO GAMES CAN BE EFFECTIVE LEARNING TOOLS

Accelerating Successful IT Adoption: The Role of User Assessment in Training Design

ERIC Educational Resources Information Center

Tyranowski, Theresa M.

2009-01-01

Technological change has become the mode of operation for today's organizations, with organizations in need of constantly training employees on their latest technological changes. There are now four generations working in the same workforce, but only the youngest generation has grown up with technology tools integrated in their daily processes. If…

The Fifth Generation and Training Strategies

ERIC Educational Resources Information Center

Ennals, Richard

2008-01-01

Fifth Generation computers should not simply be regarded as an enhancement of current computer technology: the intention is that a fresh approach should be taken to computer science and to the use of computers. The argument of this paper is that the fresh approach must encompass education and training, with implications that extend far beyond the…

Digital Literacies and Generational Micro-Cultures: Email Feedback in Lebanon

ERIC Educational Resources Information Center

De Coursey, Christina; Dandashly, Nadine

2015-01-01

This study reports on the introduction of email feedback, in a private university in Lebanon with marked generational differences and a traditional instructor culture focused on grammar correction. The instructor profile showed insufficient ELT training and a disjuncture between those with low and those with long service. Instructors were trained,…

Old Wine in Old Bottles: The Neglected Role of Vocational Training Centres in Innovation

ERIC Educational Resources Information Center

Porto Gómez, Igone; Zabala-Iturriagagoitia, Jon Mikel; Aguirre Larrakoetxea, Urko

2018-01-01

Vocational training centres are conceptually regarded as key players in the knowledge generation and dissemination processes that take place within innovation systems. However, the literature does not provide conclusive evidence of their influence on the generation, development and dissemination of innovations. The goal of this paper is to analyse…

Maintaining Interest in Operator Requal Training.

ERIC Educational Resources Information Center

Lapp, H. J., Jr.

A study reviewed operator training programs at Oyster Creek Nuclear Generating Station to determine their interface with plant operations and to devise new ways of maintaining interest in requalification (requal) training. The operator training review committee that was formed to implement the review documented over 100 issues and concerns…

Alpha neurofeedback training improves SSVEP-based BCI performance.

PubMed

Wan, Feng; da Cruz, Janir Nuno; Nan, Wenya; Wong, Chi Man; Vai, Mang I; Rosa, Agostinho

2016-06-01

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can provide relatively easy, reliable and high speed communication. However, the performance is still not satisfactory, especially in some users who are not able to generate strong enough SSVEP signals. This work aims to strengthen a user's SSVEP by alpha down-regulating neurofeedback training (NFT) and consequently improve the performance of the user in using SSVEP-based BCIs. An experiment with two steps was designed and conducted. The first step was to investigate the relationship between the resting alpha activity and the SSVEP-based BCI performance, in order to determine the training parameter for the NFT. Then in the second step, half of the subjects with 'low' performance (i.e. BCI classification accuracy <80%) were randomly assigned to a NFT group to perform a real-time NFT, and the rest half to a non-NFT control group for comparison. The first step revealed a significant negative correlation between the BCI performance and the individual alpha band (IAB) amplitudes in the eyes-open resting condition in a total of 33 subjects. In the second step, it was found that during the IAB down-regulating NFT, on average the subjects were able to successfully decrease their IAB amplitude over training sessions. More importantly, the NFT group showed an average increase of 16.5% in the SSVEP signal SNR (signal-to-noise ratio) and an average increase of 20.3% in the BCI classification accuracy, which was significant compared to the non-NFT control group. These findings indicate that the alpha down-regulating NFT can be used to improve the SSVEP signal quality and the subjects' performance in using SSVEP-based BCIs. It could be helpful to the SSVEP related studies and would contribute to more effective SSVEP-based BCI applications.

a Cloud Boundary Detection Scheme Combined with Aslic and Cnn Using ZY-3, GF-1/2 Satellite Imagery

NASA Astrophysics Data System (ADS)

Guo, Z.; Li, C.; Wang, Z.; Kwok, E.; Wei, X.

2018-04-01

Remote sensing optical image cloud detection is one of the most important problems in remote sensing data processing. Aiming at the information loss caused by cloud cover, a cloud detection method based on convolution neural network (CNN) is presented in this paper. Firstly, a deep CNN network is used to extract the multi-level feature generation model of cloud from the training samples. Secondly, the adaptive simple linear iterative clustering (ASLIC) method is used to divide the detected images into superpixels. Finally, the probability of each superpixel belonging to the cloud region is predicted by the trained network model, thereby generating a cloud probability map. The typical region of GF-1/2 and ZY-3 were selected to carry out the cloud detection test, and compared with the traditional SLIC method. The experiment results show that the average accuracy of cloud detection is increased by more than 5 %, and it can detected thin-thick cloud and the whole cloud boundary well on different imaging platforms.

Learning without labeling: domain adaptation for ultrasound transducer localization.

PubMed

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

2013-01-01

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

Behavior Knowledge Space-Based Fusion for Copy-Move Forgery Detection.

PubMed

Ferreira, Anselmo; Felipussi, Siovani C; Alfaro, Carlos; Fonseca, Pablo; Vargas-Munoz, John E; Dos Santos, Jefersson A; Rocha, Anderson

2016-07-20

The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. Afterwards, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Experimental results on complex datasets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature show the effectiveness of the proposed method and its suitability for real-world applications.

Parameter calibration for synthesizing realistic-looking variability in offline handwriting

NASA Astrophysics Data System (ADS)

Cheng, Wen; Lopresti, Dan

2011-01-01

Motivated by the widely accepted principle that the more training data, the better a recognition system performs, we conducted experiments asking human subjects to do evaluate a mixture of real English handwritten text lines and text lines altered from existing handwriting with various distortion degrees. The idea of generating synthetic handwriting is based on a perturbation method by T. Varga and H. Bunke that distorts an entire text line. There are two purposes of our experiments. First, we want to calibrate distortion parameter settings for Varga and Bunke's perturbation model. Second, we intend to compare the effects of parameter settings on different writing styles: block, cursive and mixed. From the preliminary experimental results, we determined appropriate ranges for parameter amplitude, and found that parameter settings should be altered for different handwriting styles. With the proper parameter settings, it should be possible to generate large amount of training and testing data for building better off-line handwriting recognition systems.

Spatial training promotes short-term survival and neuron-like differentiation of newborn cells in Aβ1-42-injected rats.

PubMed

Zeng, Juan; Jiang, Xia; Hu, Xian-Feng; Ma, Rong-Hong; Chai, Gao-Shang; Sun, Dong-Sheng; Xu, Zhi-Peng; Li, Li; Bao, Jian; Feng, Qiong; Hu, Yu; Chu, Jiang; Chai, Da-Min; Hong, Xiao-Yue; Wang, Jian-Zhi; Liu, Gong-Ping

2016-09-01

Neurogenesis plays a role in hippocampus-dependent learning and impaired neurogenesis may correlate with cognitive deficits in Alzheimer's disease. Spatial training influences the production and fate of newborn cells in hippocampus of normal animals, whereas the effects on neurogenesis in Alzheimer-like animal are not reported until now. Here, for the first time, we investigated the effect of Morris water maze training on proliferation, survival, apoptosis, migration, and differentiation of newborn cells in β-amyloid-treated Alzheimer-like rats. We found that spatial training could preserve a short-term survival of newborn cells generated before training, during the early phase, and the late phase of training. However, the training had no effect on the long-term survival of mature newborn cells generated at previously mentioned 3 different phases. We also demonstrated that spatial training promoted newborn cell differentiation preferentially to the neuron direction. These findings suggest a time-independent neurogenesis induced by spatial training, which may be indicative for the cognitive stimulation in Alzheimer's disease therapy. Copyright © 2016 Elsevier Inc. All rights reserved.