GeoSciML and EarthResourceML Update, 2012

Science.gov (United States)

Richard, S. M.; Commissionthe Management; Application Inte, I.

2012-12-01

CGI Interoperability Working Group activities during 2012 include deployment of services using the GeoSciML-Portrayal schema, addition of new vocabularies to support properties added in version 3.0, improvements to server software for deploying services, introduction of EarthResourceML v.2 for mineral resources, and collaboration with the IUSS on a markup language for soils information. GeoSciML and EarthResourceML have been used as the basis for the INSPIRE Geology and Mineral Resources specifications respectively. GeoSciML-Portrayal is an OGC GML simple-feature application schema for presentation of geologic map unit, contact, and shear displacement structure (fault and ductile shear zone) descriptions in web map services. Use of standard vocabularies for geologic age and lithology enables map services using shared legends to achieve visual harmonization of maps provided by different services. New vocabularies have been added to the collection of CGI vocabularies provided to support interoperable GeoSciML services, and can be accessed through http://resource.geosciml.org. Concept URIs can be dereferenced to obtain SKOS rdf or html representations using the SISSVoc vocabulary service. New releases of the FOSS GeoServer application greatly improve support for complex XML feature schemas like GeoSciML, and the ArcGIS for INSPIRE extension implements similar complex feature support for ArcGIS Server. These improved server implementations greatly facilitate deploying GeoSciML services. EarthResourceML v2 adds features for information related to mining activities. SoilML provides an interchange format for soil material, soil profile, and terrain information. Work is underway to add GeoSciML to the portfolio of Open Geospatial Consortium (OGC) specifications.

Machine learning methods to predict child posttraumatic stress: a proof of concept study.

Science.gov (United States)

Saxe, Glenn N; Ma, Sisi; Ren, Jiwen; Aliferis, Constantin

2017-07-10

The care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD. ML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains. Seven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable. In this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to

Advanced methods in NDE using machine learning approaches

Science.gov (United States)

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

2018-04-01

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

Machine learning methods for clinical forms analysis in mental health.

Science.gov (United States)

Strauss, John; Peguero, Arturo Martinez; Hirst, Graeme

2013-01-01

In preparation for a clinical information system implementation, the Centre for Addiction and Mental Health (CAMH) Clinical Information Transformation project completed multiple preparation steps. An automated process was desired to supplement the onerous task of manual analysis of clinical forms. We used natural language processing (NLP) and machine learning (ML) methods for a series of 266 separate clinical forms. For the investigation, documents were represented by feature vectors. We used four ML algorithms for our examination of the forms: cluster analysis, k-nearest neigh-bours (kNN), decision trees and support vector machines (SVM). Parameters for each algorithm were optimized. SVM had the best performance with a precision of 64.6%. Though we did not find any method sufficiently accurate for practical use, to our knowledge this approach to forms has not been used previously in mental health.

New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0.

Science.gov (United States)

Guindon, Stéphane; Dufayard, Jean-François; Lefort, Vincent; Anisimova, Maria; Hordijk, Wim; Gascuel, Olivier

2010-05-01

PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasonable starting tree topology. Since the original publication (Guindon S., Gascuel O. 2003. A simple, fast and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52:696-704), PhyML has been widely used (>2500 citations in ISI Web of Science) because of its simplicity and a fair compromise between accuracy and speed. In the meantime, research around PhyML has continued, and this article describes the new algorithms and methods implemented in the program. First, we introduce a new algorithm to search the tree space with user-defined intensity using subtree pruning and regrafting topological moves. The parsimony criterion is used here to filter out the least promising topology modifications with respect to the likelihood function. The analysis of a large collection of real nucleotide and amino acid data sets of various sizes demonstrates the good performance of this method. Second, we describe a new test to assess the support of the data for internal branches of a phylogeny. This approach extends the recently proposed approximate likelihood-ratio test and relies on a nonparametric, Shimodaira-Hasegawa-like procedure. A detailed analysis of real alignments sheds light on the links between this new approach and the more classical nonparametric bootstrap method. Overall, our tests show that the last version (3.0) of PhyML is fast, accurate, stable, and ready to use. A Web server and binary files are available from http://www.atgc-montpellier.fr/phyml/.

Intra-articular sodium hyaluronate 2 mL versus physiological saline 20 mL versus physiological saline 2 mL for painful knee osteoarthritis: a randomized clinical trial

DEFF Research Database (Denmark)

Lundsgaard, C.; Dufour, N.; Fallentin, E.

2008-01-01

, Knee Injury and Osteoarthritis Outcome Score (KOOS), Osteoarthritis Research Society International (OARSI) criteria, and global assessment of the patient's condition. Results: The mean age of the patients was 69.4 years; 55% were women. The effects of hyaluronate 2 mL, physiological saline 20 m......Objective: Methodological constraints weaken previous evidence on intra-articular viscosupplementation and physiological saline distention for osteoarthritis. We conducted a randomized, patient- and observer-blind trial to evaluate these interventions in patients with painful knee osteoarthritis....... Methods: We centrally randomized 251 patients with knee ostcoarthritis to four weekly intra-articular injections of sodium hyaluronate 2 mL (Hyalgan(R) 10.3 mg/mL) versus physiological saline 20 mL (distention) versus physiological saline 2 mL (placebo) and followed patients for 26 weeks. Inclusion...

Distinguishing butchery cut marks from crocodile bite marks through machine learning methods.

Science.gov (United States)

Domínguez-Rodrigo, Manuel; Baquedano, Enrique

2018-04-10

All models of evolution of human behaviour depend on the correct identification and interpretation of bone surface modifications (BSM) on archaeofaunal assemblages. Crucial evolutionary features, such as the origin of stone tool use, meat-eating, food-sharing, cooperation and sociality can only be addressed through confident identification and interpretation of BSM, and more specifically, cut marks. Recently, it has been argued that linear marks with the same properties as cut marks can be created by crocodiles, thereby questioning whether secure cut mark identifications can be made in the Early Pleistocene fossil record. Powerful classification methods based on multivariate statistics and machine learning (ML) algorithms have previously successfully discriminated cut marks from most other potentially confounding BSM. However, crocodile-made marks were marginal to or played no role in these comparative analyses. Here, for the first time, we apply state-of-the-art ML methods on crocodile linear BSM and experimental butchery cut marks, showing that the combination of multivariate taphonomy and ML methods provides accurate identification of BSM, including cut and crocodile bite marks. This enables empirically-supported hominin behavioural modelling, provided that these methods are applied to fossil assemblages.

Improving the accuracy of myocardial perfusion scintigraphy results by machine learning method

International Nuclear Information System (INIS)

Groselj, C.; Kukar, M.

2002-01-01

Full text: Machine learning (ML) as rapidly growing artificial intelligence subfield has already proven in last decade to be a useful tool in many fields of decision making, also in some fields of medicine. Its decision accuracy usually exceeds the human one. To assess applicability of ML in interpretation the results of stress myocardial perfusion scintigraphy for CAD diagnosis. The 327 patient's data of planar stress myocardial perfusion scintigraphy were reevaluated in usual way. Comparing them with the results of coronary angiography the sensitivity, specificity and accuracy for the investigation was computed. The data were digitized and the decision procedure repeated by ML program 'Naive Bayesian classifier'. As the ML is able to simultaneously manipulate of whatever number of data, all reachable disease connected data (regarding history, habitus, risk factors, stress results) were added. The sensitivity, specificity and accuracy for scintigraphy were expressed in this way. The results of both decision procedures were compared. With ML method 19 patients more out of 327 (5.8 %) were correctly diagnosed by stress myocardial perfusion scintigraphy. ML could be an important tool for decision making in myocardial perfusion scintigraphy. (author)

ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

KAUST Repository

Wu, Baoyuan

2015-12-07

This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some of their labels are missing). To handle missing labels, we propose a unified model of label dependencies by constructing a mixed graph, which jointly incorporates (i) instance-level similarity and class co-occurrence as undirected edges and (ii) semantic label hierarchy as directed edges. Unlike most MLML methods, We formulate this learning problem transductively as a convex quadratic matrix optimization problem that encourages training label consistency and encodes both types of label dependencies (i.e. undirected and directed edges) using quadratic terms and hard linear constraints. The alternating direction method of multipliers (ADMM) can be used to exactly and efficiently solve this problem. To evaluate our proposed method, we consider two popular applications (image and video annotation), where the label hierarchy can be derived from Wordnet. Experimental results show that our method achieves a significant improvement over state-of-the-art methods in performance and robustness to missing labels.

Modeling Music Emotion Judgments Using Machine Learning Methods

Directory of Open Access Journals (Sweden)

Naresh N. Vempala

2018-01-01

Full Text Available Emotion judgments and five channels of physiological data were obtained from 60 participants listening to 60 music excerpts. Various machine learning (ML methods were used to model the emotion judgments inclusive of neural networks, linear regression, and random forests. Input for models of perceived emotion consisted of audio features extracted from the music recordings. Input for models of felt emotion consisted of physiological features extracted from the physiological recordings. Models were trained and interpreted with consideration of the classic debate in music emotion between cognitivists and emotivists. Our models supported a hybrid position wherein emotion judgments were influenced by a combination of perceived and felt emotions. In comparing the different ML approaches that were used for modeling, we conclude that neural networks were optimal, yielding models that were flexible as well as interpretable. Inspection of a committee machine, encompassing an ensemble of networks, revealed that arousal judgments were predominantly influenced by felt emotion, whereas valence judgments were predominantly influenced by perceived emotion.

The difference of contrast effects of myelography in normal dogs: Comparison of iohexol (180 mgI/ml), iohexol (240 mgI/ml) and iotrolan (240 mgI/ml)

International Nuclear Information System (INIS)

Shimizu, J.; Yamada, K.; Kishimoto, M.; Iwasaki, T.; Miyake, Y.

2008-01-01

The contrast effects of three different contrast media preparations (iohexol 180 mgI/ml, iohexol 240 mgI/ml and iotrolan 240 mgI/ml) in conventional and CT myelography were compared. Three beagle dogs were used and the study employed a cross-over method (total of 9) for each contrast media. The result of CT myelography showed that the contrast effect of iohexol (180 mgI/ml), which had low viscosity, was highest in cranial sites, and the contrast effect of high-viscosity iotrolan (240 mgI/ml) was highest in caudal sites 5 min after injection of the contrast media preparations. This shows that the diffusion of contrast media preparations in the subarachnoid space is influenced by viscosity. The results of conventional myelography also showed that the diffusion of contrast media preparations is influenced by viscosity. Therefore, it is important to identify the location of spinal lesions in veterinary practice, and low viscosity contrast medium preparation with wide spread contrast effects is considered suitable for myelography

Quantum Machine Learning

OpenAIRE

Romero García, Cristian

2017-01-01

[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...

Organizational Learning Supported by Machine Learning Models Coupled with General Explanation Methods: A Case of B2B Sales Forecasting

Directory of Open Access Journals (Sweden)

Bohanec Marko

2017-08-01

Full Text Available Background and Purpose: The process of business to business (B2B sales forecasting is a complex decision-making process. There are many approaches to support this process, but mainly it is still based on the subjective judgment of a decision-maker. The problem of B2B sales forecasting can be modeled as a classification problem. However, top performing machine learning (ML models are black boxes and do not support transparent reasoning. The purpose of this research is to develop an organizational model using ML model coupled with general explanation methods. The goal is to support the decision-maker in the process of B2B sales forecasting.

qcML

DEFF Research Database (Denmark)

Walzer, Mathias; Pernas, Lucia Espona; Nasso, Sara

2014-01-01

provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible...... use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml....

ML Confidential : machine learning on encrypted data

NARCIS (Netherlands)

Graepel, T.; Lauter, K.; Naehrig, M.; Kwon, T.; Lee, M.-K.; Kwon, D.

2013-01-01

We demonstrate that, by using a recently proposed leveled homomorphic encryption scheme, it is possible to delegate the execution of a machine learning algorithm to a computing service while retaining con¿dentiality of the training and test data. Since the computational complexity of the homomorphic

Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.

Science.gov (United States)

Luo, Gang; Stone, Bryan L; Johnson, Michael D; Tarczy-Hornoch, Peter; Wilcox, Adam B; Mooney, Sean D; Sheng, Xiaoming; Haug, Peter J; Nkoy, Flory L

2017-08-29

To improve health outcomes and cut health care costs, we often need to conduct prediction/classification using large clinical datasets (aka, clinical big data), for example, to identify high-risk patients for preventive interventions. Machine learning has been proposed as a key technology for doing this. Machine learning has won most data science competitions and could support many clinical activities, yet only 15% of hospitals use it for even limited purposes. Despite familiarity with data, health care researchers often lack machine learning expertise to directly use clinical big data, creating a hurdle in realizing value from their data. Health care researchers can work with data scientists with deep machine learning knowledge, but it takes time and effort for both parties to communicate effectively. Facing a shortage in the United States of data scientists and hiring competition from companies with deep pockets, health care systems have difficulty recruiting data scientists. Building and generalizing a machine learning model often requires hundreds to thousands of manual iterations by data scientists to select the following: (1) hyper-parameter values and complex algorithms that greatly affect model accuracy and (2) operators and periods for temporally aggregating clinical attributes (eg, whether a patient's weight kept rising in the past year). This process becomes infeasible with limited budgets. This study's goal is to enable health care researchers to directly use clinical big data, make machine learning feasible with limited budgets and data scientist resources, and realize value from data. This study will allow us to achieve the following: (1) finish developing the new software, Automated Machine Learning (Auto-ML), to automate model selection for machine learning with clinical big data and validate Auto-ML on seven benchmark modeling problems of clinical importance; (2) apply Auto-ML and novel methodology to two new modeling problems crucial for care

Machine learning in geosciences and remote sensing

Directory of Open Access Journals (Sweden)

David J. Lary

2016-01-01

Full Text Available Learning incorporates a broad range of complex procedures. Machine learning (ML is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc. that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Adductor Canal Block With 10 mL Versus 30 mL Local Anesthetics and Quadriceps Strength

DEFF Research Database (Denmark)

Jæger, Pia; Koscielniak-Nielsen, Zbigniew J; Hilsted, Karen Lisa

2015-01-01

weakness. METHODS: We performed a paired, blinded, randomized trial including healthy men. All subjects received bilateral ACBs with ropivacaine 0.1%; 10 mL in 1 leg and 30 mL in the other leg. The primary outcome was the difference in number of subjects with quadriceps strength reduced by more than 25...... of the predefined time points or in sensory block. The only statistically significant difference between volumes was found in the 30-Second Chair Stand Test at 2 hours (P = 0.02), but this difference had disappeared at 4 hours (P = 0.06). CONCLUSIONS: Varying the volume of ropivacaine 0.1% used for ACB between 10...

QuakeML 2.0: Recent developments

Science.gov (United States)

Euchner, Fabian; Kästli, Philipp; Heiniger, Lukas; Saul, Joachim; Schorlemmer, Danijel; Clinton, John

2016-04-01

QuakeML is a community-backed data model for seismic event parameter description. Its current version 1.2, released in 2013, has become the gold standard for parametric data dissemination at seismological data centers, and has been adopted as an FDSN standard. It is supported by several popular software products and data services, such as FDSN event web services, QuakePy, and SeisComP3. Work on the successor version 2.0 is under way since 2015. The scope of QuakeML has been expanded beyond event parameter description. Thanks to a modular architecture, many thematic packages have been added, which cover peak ground motion, site and station characterization, hydraulic parameters of borehole injection processes, and macroseismics. The first three packages can be considered near final and implementations of program codes and SQL databases are in productive use at various institutions. A public community review process has been initiated in order to turn them into community-approved standards. The most recent addition is a package for single station quake location, which allows a detailed probabilistic description of event parameters recorded at a single station. This package adds some information elements such as angle of incidence, frequency-dependent phase picks, and dispersion relations. The package containing common data types has been extended with a generic type for probability density functions. While on Earth, single station methods are niche applications, they are of prominent interest in planetary seismology, e.g., the NASA InSight mission to Mars. So far, QuakeML is lacking a description of seismic instrumentation (inventory). There are two existing standards of younger age (FDSN StationXML and SeisComP3 Inventory XML). We discuss their respective strengths, differences, and how they could be combined into an inventory package for QuakeML, thus allowing full interoperability with other QuakeML data types. QuakeML is accompanied by QuakePy, a Python package

White cell labeling: 20 ML VS 4 ML of blood volume-case reports

International Nuclear Information System (INIS)

Imam, S.K.

1998-01-01

Full text: Some times, it becomes difficult to draw 20 mL blood from a patient with bad veins. On two occasions, we could collect only about 4 mL of blood, that too with a great deal of struggle, and then we carried out the routine labelling procedure. A labelling efficiency of 98.2% and 95.6% was achieved. The white cell scan was negative in one patient, but positive in the next one. In a third patient, a comparison of labelling efficiency was done between 5 and 20 mLs of blood volumes separately and the results were found to be identical, 98.5% and 98.4%, respectively. As we have achieved the usual pattern of white cell scan with as low as 4-5 mL of blood, it appears that enough number of white cells is present even in the 4-5 mL of blood that is capable of generating a white cell scan and so, it seems rational to reduce the blood volume from 20 mL to 4 or 5 mL. However, further studies are warranted before adopting this modification. The procedure appears to carry the following advantages: ease of blood collection, handling and re-injection and less risk to the patient

The jmzQuantML programming interface and validator for the mzQuantML data standard.

Science.gov (United States)

Qi, Da; Krishna, Ritesh; Jones, Andrew R

2014-03-01

The mzQuantML standard from the HUPO Proteomics Standards Initiative has recently been released, capturing quantitative data about peptides and proteins, following analysis of MS data. We present a Java application programming interface (API) for mzQuantML called jmzQuantML. The API provides robust bridges between Java classes and elements in mzQuantML files and allows random access to any part of the file. The API provides read and write capabilities, and is designed to be embedded in other software packages, enabling mzQuantML support to be added to proteomics software tools (http://code.google.com/p/jmzquantml/). The mzQuantML standard is designed around a multilevel validation system to ensure that files are structurally and semantically correct for different proteomics quantitative techniques. In this article, we also describe a Java software tool (http://code.google.com/p/mzquantml-validator/) for validating mzQuantML files, which is a formal part of the data standard. © 2014 The Authors. Proteomics published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

A practical guide to SysML the systems modeling language

CERN Document Server

Friendenthal,Sanford; Steiner, Rick

2009-01-01

This book is the bestselling, authoritative guide to SysML for systems and software engineers, providing a comprehensive and practical resource for modeling systems with SysML. Fully updated to cover newly released version 1.3, it includes a full description of the modeling language along with a quick reference guide, and shows how an organization or project can transition to model-based systems engineering using SysML, with considerations for processes, methods, tools, and training. Numerous examples help readers understand how SysML can be used in practice, while reference material facilitates studying for the OMG Systems Modeling Professional (OCSMP) Certification Program, designed to test candidates' knowledge of SysML and their ability to use models to represent real-world systems.

Machine learning methods for planning

CERN Document Server

Minton, Steven

1993-01-01

Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

jmzML, an open-source Java API for mzML, the PSI standard for MS data.

Science.gov (United States)

Côté, Richard G; Reisinger, Florian; Martens, Lennart

2010-04-01

We here present jmzML, a Java API for the Proteomics Standards Initiative mzML data standard. Based on the Java Architecture for XML Binding and XPath-based XML indexer random-access XML parser, jmzML can handle arbitrarily large files in minimal memory, allowing easy and efficient processing of mzML files using the Java programming language. jmzML also automatically resolves internal XML references on-the-fly. The library (which includes a viewer) can be downloaded from http://jmzml.googlecode.com.

Comparison of Machine Learning methods for incipient motion in gravel bed rivers

Science.gov (United States)

Valyrakis, Manousos

2013-04-01

Soil erosion and sediment transport of natural gravel bed streams are important processes which affect both the morphology as well as the ecology of earth's surface. For gravel bed rivers at near incipient flow conditions, particle entrainment dynamics are highly intermittent. This contribution reviews the use of modern Machine Learning (ML) methods implemented for short term prediction of entrainment instances of individual grains exposed in fully developed near boundary turbulent flows. Results obtained by network architectures of variable complexity based on two different ML methods namely the Artificial Neural Network (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are compared in terms of different error and performance indices, computational efficiency and complexity as well as predictive accuracy and forecast ability. Different model architectures are trained and tested with experimental time series obtained from mobile particle flume experiments. The experimental setup consists of a Laser Doppler Velocimeter (LDV) and a laser optics system, which acquire data for the instantaneous flow and particle response respectively, synchronously. The first is used to record the flow velocity components directly upstream of the test particle, while the later tracks the particle's displacements. The lengthy experimental data sets (millions of data points) are split into the training and validation subsets used to perform the corresponding learning and testing of the models. It is demonstrated that the ANFIS hybrid model, which is based on neural learning and fuzzy inference principles, better predicts the critical flow conditions above which sediment transport is initiated. In addition, it is illustrated that empirical knowledge can be extracted, validating the theoretical assumption that particle ejections occur due to energetic turbulent flow events. Such a tool may find application in management and regulation of stream flows downstream of dams for stream

Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology.

Science.gov (United States)

Li, Xin; Bai, Yanqin; Peng, Yaxin; Du, Shaoyi; Ying, Shihui

2018-03-01

Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods.

Introduction to Machine Learning: Class Notes 67577

OpenAIRE

Shashua, Amnon

2009-01-01

Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).

ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines

Science.gov (United States)

2014-05-16

Huawei , Intel, Microsoft, NetApp, Pivotal, Splunk, Virdata, VMware, WANdisco and Yahoo!. ML-o-scope: a diagnostic visualization system for deep machine...Facebook, GameOnTalis, Guavus, HP, Huawei , Intel, Microsoft, NetApp, Pivotal, Splunk, Virdata, VMware, WANdisco and Yahoo!. References [1] Bruna, J., and

Using Machine Learning in Adversarial Environments.

Energy Technology Data Exchange (ETDEWEB)

Davis, Warren Leon [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2016-02-01

Intrusion/anomaly detection systems are among the first lines of cyber defense. Commonly, they either use signatures or machine learning (ML) to identify threats, but fail to account for sophisticated attackers trying to circumvent them. We propose to embed machine learning within a game theoretic framework that performs adversarial modeling, develops methods for optimizing operational response based on ML, and integrates the resulting optimization codebase into the existing ML infrastructure developed by the Hybrid LDRD. Our approach addresses three key shortcomings of ML in adversarial settings: 1) resulting classifiers are typically deterministic and, therefore, easy to reverse engineer; 2) ML approaches only address the prediction problem, but do not prescribe how one should operationalize predictions, nor account for operational costs and constraints; and 3) ML approaches do not model attackers’ response and can be circumvented by sophisticated adversaries. The principal novelty of our approach is to construct an optimization framework that blends ML, operational considerations, and a model predicting attackers reaction, with the goal of computing optimal moving target defense. One important challenge is to construct a realistic model of an adversary that is tractable, yet realistic. We aim to advance the science of attacker modeling by considering game-theoretic methods, and by engaging experimental subjects with red teaming experience in trying to actively circumvent an intrusion detection system, and learning a predictive model of such circumvention activities. In addition, we will generate metrics to test that a particular model of an adversary is consistent with available data.

A new method for species identification via protein-coding and non-coding DNA barcodes by combining machine learning with bioinformatic methods.

Science.gov (United States)

Zhang, Ai-bing; Feng, Jie; Ward, Robert D; Wan, Ping; Gao, Qiang; Wu, Jun; Zhao, Wei-zhong

2012-01-01

Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI) region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS) genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF) to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish) and two representing non-coding ITS barcodes (rust fungi and brown algae). Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ) and Maximum likelihood (ML) methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI) of 99.75-100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62-98.40%) for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60-99.37%) for 1094 brown algae queries, both using ITS barcodes.

A new method for species identification via protein-coding and non-coding DNA barcodes by combining machine learning with bioinformatic methods.

Directory of Open Access Journals (Sweden)

Ai-bing Zhang

Full Text Available Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish and two representing non-coding ITS barcodes (rust fungi and brown algae. Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ and Maximum likelihood (ML methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI of 99.75-100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62-98.40% for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60-99.37% for 1094 brown algae queries, both using ITS barcodes.

EnzML: multi-label prediction of enzyme classes using InterPro signatures

Directory of Open Access Journals (Sweden)

De Ferrari Luna

2012-04-01

Full Text Available Abstract Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN.

Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology.

Science.gov (United States)

Min, Hua; Mobahi, Hedyeh; Irvin, Katherine; Avramovic, Sanja; Wojtusiak, Janusz

2017-09-16

Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. This retrospective study included 723 cancer patients from the SEER-MHOS dataset. Two ML methods were applied to create predictive models for ADL disabilities for the first year after a patient's cancer diagnosis. The first method is a standard rule learning algorithm; the second is that same algorithm additionally equipped with methods for reasoning with ontologies. The models showed that a patient's race, ethnicity, smoking preference, treatment plan and tumor characteristics including histology, staging, cancer site, and morphology were predictors for ADL performance levels one year after cancer diagnosis. The ontology-guided ML method was more accurate at predicting ADL performance levels (P ontologies. This study demonstrated that bio-ontologies can be harnessed to provide medical knowledge for ML algorithms. The presented method demonstrates that encoding specific types of hierarchical relationships to guide rule learning is possible, and can be extended to other types of semantic relationships present in biomedical ontologies. The ontology-guided ML method achieved better performance than the method without ontologies. The presented method can also be used to promote the effectiveness and efficiency of ML in healthcare, in which use of background knowledge and consistency with existing clinical expertise is critical.

Cooperative Learning as a Democratic Learning Method

Science.gov (United States)

Erbil, Deniz Gökçe; Kocabas, Ayfer

2018-01-01

In this study, the effects of applying the cooperative learning method on the students' attitude toward democracy in an elementary 3rd-grade life studies course was examined. Over the course of 8 weeks, the cooperative learning method was applied with an experimental group, and traditional methods of teaching life studies in 2009, which was still…

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

Science.gov (United States)

Dipnall, J F; Pasco, J A; Berk, M; Williams, L J; Dodd, S; Jacka, F N; Meyer, D

2017-01-01

Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, Pdepression was found, with significant interactions with those married/living with partner (P=0.001). Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Predictive ability of machine learning methods for massive crop yield prediction

Directory of Open Access Journals (Sweden)

Alberto Gonzalez-Sanchez

2014-04-01

Full Text Available An important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS, root relative square error (RRSE, normalized mean absolute error (MAE, and correlation factor (R. Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5-Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91, the lowest RRSE errors (79.46% and 79.78%, the lowest average MAE errors (18.12% and 19.42%, and the highest average correlation factors (0.41 and 0.42. Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning.

Qualitative methods in workplace learning

OpenAIRE

Fabritius, Hannele

2015-01-01

Methods of learning in the workplace will be introduced. The methods are connect to competence development and to the process of conducting development discussions in a dialogical way. The tools developed and applied are a fourfold table, a cycle of work identity, a plan of personal development targets, a learning meeting and a learning map. The methods introduced will aim to better learning at work.

Improving orbit prediction accuracy through supervised machine learning

Science.gov (United States)

Peng, Hao; Bai, Xiaoli

2018-05-01

Due to the lack of information such as the space environment condition and resident space objects' (RSOs') body characteristics, current orbit predictions that are solely grounded on physics-based models may fail to achieve required accuracy for collision avoidance and have led to satellite collisions already. This paper presents a methodology to predict RSOs' trajectories with higher accuracy than that of the current methods. Inspired by the machine learning (ML) theory through which the models are learned based on large amounts of observed data and the prediction is conducted without explicitly modeling space objects and space environment, the proposed ML approach integrates physics-based orbit prediction algorithms with a learning-based process that focuses on reducing the prediction errors. Using a simulation-based space catalog environment as the test bed, the paper demonstrates three types of generalization capability for the proposed ML approach: (1) the ML model can be used to improve the same RSO's orbit information that is not available during the learning process but shares the same time interval as the training data; (2) the ML model can be used to improve predictions of the same RSO at future epochs; and (3) the ML model based on a RSO can be applied to other RSOs that share some common features.

Aggressiveness of powdery mildew on 'ml-o'- resistant barley

International Nuclear Information System (INIS)

Andersen, Lars

1990-01-01

The ml-o genes in barley are important sources in breeding for resistance against the barley powdery mildew fungus (Erysiphe graminis). The resistance mechanism is a rapid formation of a large callose containing cell wall apposition at the site of the pathogen's infection attempt. This reduces the chances of infection to almost nil in all epidermal cells, except in the small subsidiary cells, in which appositions are rarely formed. Small mildew colonies from infections in subsidiary cells may be seen on the otherwise resistant leaf. This is described by the infection type 0/(4). Mildew isolate HL 3 selected by SCHWARZBACH has increased aggressiveness. No ml-o-virulent isolates are known. However, ml-o-resistant varieties when grown extensively in Europe, will introduce field selection for mildew pathotypes with aggressiveness or virulence to ml-o resistance. Studies on increased aggressiveness require new methods. The material comprises two powdery mildew isolates: GE 3 without ml-o aggressiveness and the aggressive HL 3/5; and two near-isogenic barley lines in Carlsberg II: Riso 5678(R) with the recessive mutant resistance gene ml-o5 and Riso 5678(S) with the wild-type gene for susceptibility. Latent period and disease efficiency show no significant differences between the two isolates on the susceptible barley line (S) but the isolates differ from each other on the resistant barley line

jqcML: an open-source java API for mass spectrometry quality control data in the qcML format.

Science.gov (United States)

Bittremieux, Wout; Kelchtermans, Pieter; Valkenborg, Dirk; Martens, Lennart; Laukens, Kris

2014-07-03

The awareness that systematic quality control is an essential factor to enable the growth of proteomics into a mature analytical discipline has increased over the past few years. To this aim, a controlled vocabulary and document structure have recently been proposed by Walzer et al. to store and disseminate quality-control metrics for mass-spectrometry-based proteomics experiments, called qcML. To facilitate the adoption of this standardized quality control routine, we introduce jqcML, a Java application programming interface (API) for the qcML data format. First, jqcML provides a complete object model to represent qcML data. Second, jqcML provides the ability to read, write, and work in a uniform manner with qcML data from different sources, including the XML-based qcML file format and the relational database qcDB. Interaction with the XML-based file format is obtained through the Java Architecture for XML Binding (JAXB), while generic database functionality is obtained by the Java Persistence API (JPA). jqcML is released as open-source software under the permissive Apache 2.0 license and can be downloaded from https://bitbucket.org/proteinspector/jqcml .

The abstract geometry modeling language (AgML): experience and road map toward eRHIC

International Nuclear Information System (INIS)

Webb, Jason; Lauret, Jerome; Perevoztchikov, Victor

2014-01-01

The STAR experiment has adopted an Abstract Geometry Modeling Language (AgML) as the primary description of our geometry model. AgML establishes a level of abstraction, decoupling the definition of the detector from the software libraries used to create the concrete geometry model. Thus, AgML allows us to support both our legacy GEANT 3 simulation application and our ROOT/TGeo based reconstruction software from a single source, which is demonstrably self- consistent. While AgML was developed primarily as a tool to migrate away from our legacy FORTRAN-era geometry codes, it also provides a rich syntax geared towards the rapid development of detector models. AgML has been successfully employed by users to quickly develop and integrate the descriptions of several new detectors in the RHIC/STAR experiment including the Forward GEM Tracker (FGT) and Heavy Flavor Tracker (HFT) upgrades installed in STAR for the 2012 and 2013 runs. AgML has furthermore been heavily utilized to study future upgrades to the STAR detector as it prepares for the eRHIC era. With its track record of practical use in a live experiment in mind, we present the status, lessons learned and future of the AgML language as well as our experience in bringing the code into our production and development environments. We will discuss the path toward eRHIC and pushing the current model to accommodate for detector miss-alignment and high precision physics.

Malignant lymphomas (ML and HIV infection in Tanzania

Directory of Open Access Journals (Sweden)

Mwakigonja Amos R

2008-06-01

Full Text Available Abstract Background HIV infection is reported to be associated with some malignant lymphomas (ML so called AIDS-related lymphomas (ARL, with an aggressive behavior and poor prognosis. The ML frequency, pathogenicity, clinical patterns and possible association with AIDS in Tanzania, are not well documented impeding the development of preventive and therapeutic strategies. Methods Sections of 176 archival formalin-fixed paraffin-embedded biopsies of ML patients at Muhimbili National Hospital (MNH/Muhimbili University of Health and Allied Sciences (MUHAS, Tanzania from 1996–2001 were stained for hematoxylin and eosin and selected (70 cases for expression of pan-leucocytic (CD45, B-cell (CD20, T-cell (CD3, Hodgkin/RS cell (CD30, histiocyte (CD68 and proliferation (Ki-67 antigen markers. Corresponding clinical records were also evaluated. Available sera from 38 ML patients were screened (ELISA for HIV antibodies. Results The proportion of ML out of all diagnosed tumors at MNH during the 6 year period was 4.2% (176/4200 comprising 77.84% non-Hodgkin (NHL including 19.32% Burkitt's (BL and 22.16% Hodgkin's disease (HD. The ML tumors frequency increased from 0.42% (1997 to 0.70% (2001 and 23.7% of tested sera from these patients were HIV positive. The mean age for all ML was 30, age-range 3–91 and peak age was 1–20 years. The male:female ratio was 1.8:1. Supra-diaphragmatic presentation was commonest and histological sub-types were mostly aggressive B-cell lymphomas however, no clear cases of primary effusion lymphoma (PEL and primary central nervous system lymphoma (PCNSL were diagnosed. Conclusion Malignant lymphomas apparently, increased significantly among diagnosed tumors at MNH between 1996 and 2001, predominantly among the young, HIV infected and AIDS patients. The frequent aggressive clinical and histological presentation as well as the dominant B-immunophenotype and the HIV serology indicate a pathogenic association with AIDS. Therefore

Machine learning for medical ultrasound: status, methods, and future opportunities.

Science.gov (United States)

Brattain, Laura J; Telfer, Brian A; Dhyani, Manish; Grajo, Joseph R; Samir, Anthony E

2018-04-01

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

ML 3.1 developer's guide.

Energy Technology Data Exchange (ETDEWEB)

Sala, Marzio; Hu, Jonathan Joseph (Sandia National Laboratories, Livermore, CA); Tuminaro, Raymond Stephen (Sandia National Laboratories, Livermore, CA)

2004-05-01

ML development was started in 1997 by Ray Tuminaro and Charles Tong. Currently, there are several full- and part-time developers. The kernel of ML is written in ANSI C, and there is a rich C++ interface for Trilinos users and developers. ML can be customized to run geometric and algebraic multigrid; it can solve a scalar or a vector equation (with constant number of equations per grid node), and it can solve a form of Maxwell's equations. For a general introduction to ML and its applications, we refer to the Users Guide [SHT04], and to the ML web site, http://software.sandia.gov/ml.

Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

Science.gov (United States)

Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

2017-01-01

Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

mzML2ISA & nmrML2ISA: generating enriched ISA-Tab metadata files from metabolomics XML data.

Science.gov (United States)

Larralde, Martin; Lawson, Thomas N; Weber, Ralf J M; Moreno, Pablo; Haug, Kenneth; Rocca-Serra, Philippe; Viant, Mark R; Steinbeck, Christoph; Salek, Reza M

2017-08-15

Submission to the MetaboLights repository for metabolomics data currently places the burden of reporting instrument and acquisition parameters in ISA-Tab format on users, who have to do it manually, a process that is time consuming and prone to user input error. Since the large majority of these parameters are embedded in instrument raw data files, an opportunity exists to capture this metadata more accurately. Here we report a set of Python packages that can automatically generate ISA-Tab metadata file stubs from raw XML metabolomics data files. The parsing packages are separated into mzML2ISA (encompassing mzML and imzML formats) and nmrML2ISA (nmrML format only). Overall, the use of mzML2ISA & nmrML2ISA reduces the time needed to capture metadata substantially (capturing 90% of metadata on assay and sample levels), is much less prone to user input errors, improves compliance with minimum information reporting guidelines and facilitates more finely grained data exploration and querying of datasets. mzML2ISA & nmrML2ISA are available under version 3 of the GNU General Public Licence at https://github.com/ISA-tools. Documentation is available from http://2isa.readthedocs.io/en/latest/. reza.salek@ebi.ac.uk or isatools@googlegroups.com. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Case-Based Reasoning in Mixed Paradigm Settings and with Learning

Science.gov (United States)

1994-04-30

Learning Prototypical Cases OFF-BROADWAY, MCI and RMHC -* are three CBR-ML systems that learn case prototypes. We feel that methods that enable the...at Irvine Machine Learning Repository, including heart disease and breast cancer databases. OFF-BROADWAY, MCI and RMHC -* made the following notable

Allelism of Genes in the Ml-a locus

DEFF Research Database (Denmark)

Giese, Nanna Henriette; Jensen, Hans Peter; Jørgensen, Jørgen Helms

1980-01-01

Seven barley lines or varieties, each with a different gene at the Ml-a locus for resistance to Erysiphe graminis were intercrossed. Progeny testing of the F2s using two different fungal isolates per cross provided evidence that there are two or more loci in the Ml-a region. Apparent recombinants...... were also screened for recombination between the Hor1 and Hor2 loci which are situated either side of the Ml-a locus. The cross between Ricardo and Iso42R (Rupee) yielded one possible recombinant, with Ml-a3 and Ml-a(Rul) in the coupling phase; other recombinants had wild-type genes in the coupling...... phase. Iso20R, derived from Hordeum spontaneum 'H204', carrying Ml-a6, had an additional gene, in close coupling with Ml-a6, tentatively named Ml-aSp2 or Reglv, causing an intermediate infection type with isolate EmA30. It is suggested that Ml-a(Ar) in Emir and Ml-a(Rul), shown to differ from other Ml...

Gating-ML: XML-based gating descriptions in flow cytometry.

Science.gov (United States)

Spidlen, Josef; Leif, Robert C; Moore, Wayne; Roederer, Mario; Brinkman, Ryan R

2008-12-01

The lack of software interoperability with respect to gating due to lack of a standardized mechanism for data exchange has traditionally been a bottleneck, preventing reproducibility of flow cytometry (FCM) data analysis and the usage of multiple analytical tools. To facilitate interoperability among FCM data analysis tools, members of the International Society for the Advancement of Cytometry (ISAC) Data Standards Task Force (DSTF) have developed an XML-based mechanism to formally describe gates (Gating-ML). Gating-ML, an open specification for encoding gating, data transformations and compensation, has been adopted by the ISAC DSTF as a Candidate Recommendation. Gating-ML can facilitate exchange of gating descriptions the same way that FCS facilitated for exchange of raw FCM data. Its adoption will open new collaborative opportunities as well as possibilities for advanced analyses and methods development. The ISAC DSTF is satisfied that the standard addresses the requirements for a gating exchange standard.

Overview of deep learning in medical imaging.

Science.gov (United States)

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

Machine Learning from Garden Path Sentences: The Application of Computational Linguistics

Directory of Open Access Journals (Sweden)

Jiali Du

2014-12-01

Full Text Available This paper discusses the application of computational linguistics in the machine learning (ML system for the processing of garden path sentences. ML is closely related to artificial intelligence and linguistic cognition. The rapid and efficient processing of the complex structures is an effective method to test the system. By means of parsing the garden path sentence, we draw a conclusion that the integration of theoretical and statistical methods is helpful for the development of ML system.

Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

NARCIS (Netherlands)

Cheplygina, Veronika; de Bruijne, Marleen; Pluim, Josien P. W.

2018-01-01

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods which can learn

MPS and ML

Science.gov (United States)

... individuals about MPS and ML, the National MPS Society has created a central location for more information on MPS. Click here to go to the MPS Library. Share Tweet Our Mission The National MPS Society exists to cure, support and advocate for MPS ...

Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

Directory of Open Access Journals (Sweden)

Dwi Nur Rachmah

2017-12-01

Full Text Available Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas Lambung Mangkurat. The method used in the study is the experimental method using one group pre-test and post-test design. The results of the measurements before and after the use of jigsaw learning method were compared using paired samples t-test. The results showed that there is a difference in students’ self-efficacy and motivation to learn before and after subjected to the treatments; therefore, it can be said that jigsaw learning method had significant effects on self-efficacy and motivation to learn. The application of jigsaw learning model in a classroom with large number of students was the discussion of this study.

Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!

Science.gov (United States)

Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

2017-04-01

Machine learning (ML) is considered to be a promising approach to hydrological processes forecasting. We conduct a comparison between several stochastic and ML point estimation methods by performing large-scale computational experiments based on simulations. The purpose is to provide generalized results, while the respective comparisons in the literature are usually based on case studies. The stochastic methods used include simple methods, models from the frequently used families of Autoregressive Moving Average (ARMA), Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Exponential Smoothing models. The ML methods used are Random Forests (RF), Support Vector Machines (SVM) and Neural Networks (NN). The comparison refers to the multi-step ahead forecasting properties of the methods. A total of 20 methods are used, among which 9 are the ML methods. 12 simulation experiments are performed, while each of them uses 2 000 simulated time series of 310 observations. The time series are simulated using stochastic processes from the families of ARMA and ARFIMA models. Each time series is split into a fitting (first 300 observations) and a testing set (last 10 observations). The comparative assessment of the methods is based on 18 metrics, that quantify the methods' performance according to several criteria related to the accurate forecasting of the testing set, the capturing of its variation and the correlation between the testing and forecasted values. The most important outcome of this study is that there is not a uniformly better or worse method. However, there are methods that are regularly better or worse than others with respect to specific metrics. It appears that, although a general ranking of the methods is not possible, their classification based on their similar or contrasting performance in the various metrics is possible to some extent. Another important conclusion is that more sophisticated methods do not necessarily provide better forecasts

The XBabelPhish MAGE-ML and XML translator.

Science.gov (United States)

Maier, Don; Wymore, Farrell; Sherlock, Gavin; Ball, Catherine A

2008-01-18

MAGE-ML has been promoted as a standard format for describing microarray experiments and the data they produce. Two characteristics of the MAGE-ML format compromise its use as a universal standard: First, MAGE-ML files are exceptionally large - too large to be easily read by most people, and often too large to be read by most software programs. Second, the MAGE-ML standard permits many ways of representing the same information. As a result, different producers of MAGE-ML create different documents describing the same experiment and its data. Recognizing all the variants is an unwieldy software engineering task, resulting in software packages that can read and process MAGE-ML from some, but not all producers. This Tower of MAGE-ML Babel bars the unencumbered exchange of microarray experiment descriptions couched in MAGE-ML. We have developed XBabelPhish - an XQuery-based technology for translating one MAGE-ML variant into another. XBabelPhish's use is not restricted to translating MAGE-ML documents. It can transform XML files independent of their DTD, XML schema, or semantic content. Moreover, it is designed to work on very large (> 200 Mb.) files, which are common in the world of MAGE-ML. XBabelPhish provides a way to inter-translate MAGE-ML variants for improved interchange of microarray experiment information. More generally, it can be used to transform most XML files, including very large ones that exceed the capacity of most XML tools.

Video Quality Assessment and Machine Learning: Performance and Interpretability

DEFF Research Database (Denmark)

Søgaard, Jacob; Forchhammer, Søren; Korhonen, Jari

2015-01-01

In this work we compare a simple and a complex Machine Learning (ML) method used for the purpose of Video Quality Assessment (VQA). The simple ML method chosen is the Elastic Net (EN), which is a regularized linear regression model and easier to interpret. The more complex method chosen is Support...... Vector Regression (SVR), which has gained popularity in VQA research. Additionally, we present an ML-based feature selection method. Also, it is investigated how well the methods perform when tested on videos from other datasets. Our results show that content-independent cross-validation performance...... on a single dataset can be misleading and that in the case of very limited training and test data, especially in regards to different content as is the case for many video datasets, a simple ML approach is the better choice....

An Ontology for State Analysis: Formalizing the Mapping to SysML

Science.gov (United States)

Wagner, David A.; Bennett, Matthew B.; Karban, Robert; Rouquette, Nicolas; Jenkins, Steven; Ingham, Michel

2012-01-01

State Analysis is a methodology developed over the last decade for architecting, designing and documenting complex control systems. Although it was originally conceived for designing robotic spacecraft, recent applications include the design of control systems for large ground-based telescopes. The European Southern Observatory (ESO) began a project to design the European Extremely Large Telescope (E-ELT), which will require coordinated control of over a thousand articulated mirror segments. The designers are using State Analysis as a methodology and the Systems Modeling Language (SysML) as a modeling and documentation language in this task. To effectively apply the State Analysis methodology in this context it became necessary to provide ontological definitions of the concepts and relations in State Analysis and greater flexibility through a mapping of State Analysis into a practical extension of SysML. The ontology provides the formal basis for verifying compliance with State Analysis semantics including architectural constraints. The SysML extension provides the practical basis for applying the State Analysis methodology with SysML tools. This paper will discuss the method used to develop these formalisms (the ontology), the formalisms themselves, the mapping to SysML and approach to using these formalisms to specify a control system and enforce architectural constraints in a SysML model.

CLAS App ML

NARCIS (Netherlands)

Maher, Bridget; Hartkopf, Kathleen; Stieger, Lina; Schroeder, Hanna; Sopka, Sasa; Orrego, Carola; Drachsler, Hendrik

2014-01-01

This is a multi-language (ML) update of the CLAS App original design by Bridget Maher from the School of Medicine at University College Cork, Ireland. The current version has an improve counting mechanism and has been translated from English to Spanish, Catalan and German languages within the

Learning Science, Learning about Science, Doing Science: Different Goals Demand Different Learning Methods

Science.gov (United States)

Hodson, Derek

2014-01-01

This opinion piece paper urges teachers and teacher educators to draw careful distinctions among four basic learning goals: learning science, learning about science, doing science and learning to address socio-scientific issues. In elaboration, the author urges that careful attention is paid to the selection of teaching/learning methods that…

Absorption kinetics of two highly concentrated preparations of growth hormone: 12 IU/ml compared to 56 IU/ml

DEFF Research Database (Denmark)

Laursen, Torben; Susgaard, Søren; Jensen, Flemming Steen

1994-01-01

was to compare the relative bioavailability of two highly concentrated (12 IU/ml versus 56 IU/ml) formulations of biosynthetic human growth hormone administered subcutaneously. After pretreatment with growth hormone for at least four weeks, nine growth hormone deficient patients with a mean age of 26.2 years......AbstractSend to: Pharmacol Toxicol. 1994 Jan;74(1):54-7. Absorption kinetics of two highly concentrated preparations of growth hormone: 12 IU/ml compared to 56 IU/ml. Laursen T1, Susgaard S, Jensen FS, Jørgensen JO, Christiansen JS. Author information Abstract The purpose of this study...... (range 17-43) were studied two times in a randomized design, the two studies being separated by at least one week. At the start of each study period (7 p.m.), growth hormone was injected subcutaneously in a dosage of 3 IU/m2. The 12 IU/ml preparation of growth hormone was administered on one occasion...

Statistical learning methods: Basics, control and performance

Energy Technology Data Exchange (ETDEWEB)

Zimmermann, J. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de

2006-04-01

The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms.

Statistical learning methods: Basics, control and performance

International Nuclear Information System (INIS)

Zimmermann, J.

2006-01-01

The basics of statistical learning are reviewed with a special emphasis on general principles and problems for all different types of learning methods. Different aspects of controlling these methods in a physically adequate way will be discussed. All principles and guidelines will be exercised on examples for statistical learning methods in high energy and astrophysics. These examples prove in addition that statistical learning methods very often lead to a remarkable performance gain compared to the competing classical algorithms

Description of the sodium loop ML-3

International Nuclear Information System (INIS)

Torre, de la M.; Melches, I; Lapena, J.; Martinez, T.A.; Miguel, de D.; Duran, F.

1979-01-01

The sodium loop ML-3 is described. The main objective of this facility is to obtain mechanical property data for LMFBR materials in creep and low cycle fatigue testing in flowing sodium. ML-3 includes 10 test stations for creep and two for fatigue. It is possible to operate simultaneously at three different temperature levels. The maximum operating temperature is 650 deg C at flow velocities up to 5 m/s. The ML-3 loop has been located in a manner that permits the fill/dump tank cover gas and security systems to be shared with an earlier circuit, the ML-1. (author)

Effects of Jigsaw Learning Method on Students’ Self-Efficacy and Motivation to Learn

OpenAIRE

Dwi Nur Rachmah

2017-01-01

Jigsaw learning as a cooperative learning method, according to the results of some studies, can improve academic skills, social competence, behavior in learning, and motivation to learn. However, in some other studies, there are different findings regarding the effect of jigsaw learning method on self-efficacy. The purpose of this study is to examine the effects of jigsaw learning method on self-efficacy and motivation to learn in psychology students at the Faculty of Medicine, Universitas La...

RuleML-Based Learning Object Interoperability on the Semantic Web

Science.gov (United States)

Biletskiy, Yevgen; Boley, Harold; Ranganathan, Girish R.

2008-01-01

Purpose: The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions. Design/methodology/approach: As proof of concept, a rule…

Geometrical methods in learning theory

International Nuclear Information System (INIS)

Burdet, G.; Combe, Ph.; Nencka, H.

2001-01-01

The methods of information theory provide natural approaches to learning algorithms in the case of stochastic formal neural networks. Most of the classical techniques are based on some extremization principle. A geometrical interpretation of the associated algorithms provides a powerful tool for understanding the learning process and its stability and offers a framework for discussing possible new learning rules. An illustration is given using sequential and parallel learning in the Boltzmann machine

QuakeML - An XML Schema for Seismology

Science.gov (United States)

Wyss, A.; Schorlemmer, D.; Maraini, S.; Baer, M.; Wiemer, S.

2004-12-01

We propose an extensible format-definition for seismic data (QuakeML). Sharing data and seismic information efficiently is one of the most important issues for research and observational seismology in the future. The eXtensible Markup Language (XML) is playing an increasingly important role in the exchange of a variety of data. Due to its extensible definition capabilities, its wide acceptance and the existing large number of utilities and libraries for XML, a structured representation of various types of seismological data should in our opinion be developed by defining a 'QuakeML' standard. Here we present the QuakeML definitions for parameter databases and further efforts, e.g. a central QuakeML catalog database and a web portal for exchanging codes and stylesheets.

Deep learning versus traditional machine learning methods for aggregated energy demand prediction

NARCIS (Netherlands)

Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

2018-01-01

In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

Proceedings ML Family/OCaml Users and Developers workshops

OpenAIRE

Kiselyov, Oleg; Garrigue, Jacques

2015-01-01

This volume collects the extended versions of selected papers originally presented at the two ACM SIGPLAN workshops: ML Family Workshop 2014 and OCaml 2014. Both were affiliated with ICFP 2014 and took place on two consecutive days, on September 4 and 5, 2014 in Gothenburg, Sweden. The ML Family workshop aims to recognize the entire extended family of ML and ML-like languages: languages that are Higher-order, Typed, Inferred, and Strict. It provides the forum to discuss common issues, both pr...

Effect of Methods of Learning and Self Regulated Learning toward Outcomes of Learning Social Studies

Science.gov (United States)

Tjalla, Awaluddin; Sofiah, Evi

2015-01-01

This research aims to reveal the influence of learning methods and self-regulated learning on students learning scores for Social Studies object. The research was done in Islamic Junior High School (MTs Manba'ul Ulum), Batuceper City Tangerang using quasi-experimental method. The research employed simple random technique to 28 students. Data were…

The method of global learning in teaching foreign languages

Directory of Open Access Journals (Sweden)

Tatjana Dragovič

2001-12-01

Full Text Available The authors describe the method of global learning of foreign languages, which is based on the principles of neurolinguistic programming (NLP. According to this theory, the educator should use the method of the so-called periphery learning, where students learn relaxation techniques and at the same time they »incidentally « or subconsciously learn a foreign language. The method of global learning imitates successful strategies of learning in early childhood and therefore creates a relaxed attitude towards learning. Global learning is also compared with standard methods.

Do students’ styles of learning affect how they adapt to learning methods and to the learning environment?

OpenAIRE

Topal, Kenan; Sarıkaya, Özlem; Basturk, Ramazan; Buke, Akile

2015-01-01

Objectives: The process of development and evaluation of undergraduate medical education programs should include analysis of learners’ characteristics, needs, and perceptions about learning methods. This study aims to evaluate medical students’ perceptions about problem-based learning methods and to compare these results with their individual learning styles.Materials and Methods: The survey was conducted at Marmara University Medical School where problem-based learning was implemented in the...

Quantum machine learning: a classical perspective

Science.gov (United States)

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed. PMID:29434508

Quantum machine learning: a classical perspective.

Science.gov (United States)

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

Quantum machine learning: a classical perspective

Science.gov (United States)

Ciliberto, Carlo; Herbster, Mark; Ialongo, Alessandro Davide; Pontil, Massimiliano; Rocchetto, Andrea; Severini, Simone; Wossnig, Leonard

2018-01-01

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning (ML) techniques to impressive results in regression, classification, data generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets is motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed up classical ML algorithms. Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.

Reflexive Learning through Visual Methods

DEFF Research Database (Denmark)

Frølunde, Lisbeth

2014-01-01

What. This chapter concerns how visual methods and visual materials can support visually oriented, collaborative, and creative learning processes in education. The focus is on facilitation (guiding, teaching) with visual methods in learning processes that are designerly or involve design. Visual...... methods are exemplified through two university classroom cases about collaborative idea generation processes. The visual methods and materials in the cases are photo elicitation using photo cards, and modeling with LEGO Serious Play sets. Why. The goal is to encourage the reader, whether student...... or professional, to facilitate with visual methods in a critical, reflective, and experimental way. The chapter offers recommendations for facilitating with visual methods to support playful, emergent designerly processes. The chapter also has a critical, situated perspective. Where. This chapter offers case...

An Innovative Teaching Method To Promote Active Learning: Team-Based Learning

Science.gov (United States)

Balasubramanian, R.

2007-12-01

Traditional teaching practice based on the textbook-whiteboard- lecture-homework-test paradigm is not very effective in helping students with diverse academic backgrounds achieve higher-order critical thinking skills such as analysis, synthesis, and evaluation. Consequently, there is a critical need for developing a new pedagogical approach to create a collaborative and interactive learning environment in which students with complementary academic backgrounds and learning skills can work together to enhance their learning outcomes. In this presentation, I will discuss an innovative teaching method ('Team-Based Learning (TBL)") which I recently developed at National University of Singapore to promote active learning among students in the environmental engineering program with learning abilities. I implemented this new educational activity in a graduate course. Student feedback indicates that this pedagogical approach is appealing to most students, and promotes active & interactive learning in class. Data will be presented to show that the innovative teaching method has contributed to improved student learning and achievement.

ML at ATLAS&CMS : setting the stage

CERN Multimedia

CERN. Geneva

2015-01-01

In the early days of the LHC the canonical problems of classification and regression were mostly addressed using simple cut-based techniques. Today, ML techniques (some already pioneered in pre-LHC or non collider experiments) play a fundamental role in the toolbox of any experimentalist. The talk will introduce, through a representative collection of examples, the problems addressed with ML techniques at the LHC. The goal of the talk is to set the stage for a constructive discussion with non-HEP ML practitioners, focusing on the specificities of HEP applications.

Emerging Paradigms in Machine Learning

CERN Document Server

Jain, Lakhmi; Howlett, Robert

2013-01-01

This  book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The  multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.  The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.  Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary ...

Learning Method, Facilities And Infrastructure, And Learning Resources In Basic Networking For Vocational School

OpenAIRE

Pamungkas, Bian Dwi

2017-01-01

This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...

iML1515, a knowledgebase that computes Escherichia coli traits

DEFF Research Database (Denmark)

Monk, Jonathan M.; Lloyd, Colton J.; Brunk, Elizabeth

2017-01-01

To the Editor: Extracting knowledge from the many types of big data produced by high-throughput methods remains a challenge, even when data are from Escherichia coli, the best characterized bacterial species. Here, we present iML1515, the most complete genome-scale reconstruction of the metabolic...

Implementation of a Goal-Based Systems Engineering Process Using the Systems Modeling Language (SysML)

Science.gov (United States)

Breckenridge, Jonathan T.; Johnson, Stephen B.

2013-01-01

Building upon the purpose, theoretical approach, and use of a Goal-Function Tree (GFT) being presented by Dr. Stephen B. Johnson, described in a related Infotech 2013 ISHM abstract titled "Goal-Function Tree Modeling for Systems Engineering and Fault Management", this paper will describe the core framework used to implement the GFTbased systems engineering process using the Systems Modeling Language (SysML). These two papers are ideally accepted and presented together in the same Infotech session. Statement of problem: SysML, as a tool, is currently not capable of implementing the theoretical approach described within the "Goal-Function Tree Modeling for Systems Engineering and Fault Management" paper cited above. More generally, SysML's current capabilities to model functional decompositions in the rigorous manner required in the GFT approach are limited. The GFT is a new Model-Based Systems Engineering (MBSE) approach to the development of goals and requirements, functions, and its linkage to design. As a growing standard for systems engineering, it is important to develop methods to implement GFT in SysML. Proposed Method of Solution: Many of the central concepts of the SysML language are needed to implement a GFT for large complex systems. In the implementation of those central concepts, the following will be described in detail: changes to the nominal SysML process, model view definitions and examples, diagram definitions and examples, and detailed SysML construct and stereotype definitions.

Model-driven Service Engineering with SoaML

Science.gov (United States)

Elvesæter, Brian; Carrez, Cyril; Mohagheghi, Parastoo; Berre, Arne-Jørgen; Johnsen, Svein G.; Solberg, Arnor

This chapter presents a model-driven service engineering (MDSE) methodology that uses OMG MDA specifications such as BMM, BPMN and SoaML to identify and specify services within a service-oriented architecture. The methodology takes advantage of business modelling practices and provides a guide to service modelling with SoaML. The presentation is case-driven and illuminated using the telecommunication example. The chapter focuses in particular on the use of the SoaML modelling language as a means for expressing service specifications that are aligned with business models and can be realized in different platform technologies.

New e-learning method using databases

Directory of Open Access Journals (Sweden)

Andreea IONESCU

2012-10-01

Full Text Available The objective of this paper is to present a new e-learning method that use databases. The solution could pe implemented for any typeof e-learning system in any domain. The article will purpose a solution to improve the learning process for virtual classes.

Machine Learning of Musical Gestures

OpenAIRE

Caramiaux, Baptiste; Tanaka, Atau

2013-01-01

We present an overview of machine learning (ML) techniques and theirapplication in interactive music and new digital instruments design. We firstgive to the non-specialist reader an introduction to two ML tasks,classification and regression, that are particularly relevant for gesturalinteraction. We then present a review of the literature in current NIMEresearch that uses ML in musical gesture analysis and gestural sound control.We describe the ways in which machine learning is useful for cre...

Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods.

Science.gov (United States)

Legrain, Fleur; Carrete, Jesús; van Roekeghem, Ambroise; Madsen, Georg K H; Mingo, Natalio

2018-01-18

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71 178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high-throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasiharmonic contributions.

Active learning methods for interactive image retrieval.

Science.gov (United States)

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

Adaptive e-learning methods and IMS Learning Design. An integrated approach

NARCIS (Netherlands)

Burgos, Daniel; Specht, Marcus

2006-01-01

Please, cite this publication as: Burgos, D., & Specht, M. (2006). Adaptive e-learning methods and IMS Learning Design. In Kinshuk, R. Koper, P. Kommers, P. Kirschner, D. G. Sampson & W. Didderen (Eds.), Proceedings of the 6th IEEE International Conference on Advanced Learning Technologies (pp.

The application of machine learning to the modelling of percutaneous absorption: an overview and guide.

Science.gov (United States)

Ashrafi, P; Moss, G P; Wilkinson, S C; Davey, N; Sun, Y

2015-01-01

Machine learning (ML) methods have been applied to the analysis of a range of biological systems. This paper reviews the application of these methods to the problem domain of skin permeability and addresses critically some of the key issues. Specifically, ML methods offer great potential in both predictive ability and their ability to provide mechanistic insight to, in this case, the phenomena of skin permeation. However, they are beset by perceptions of a lack of transparency and, often, once a ML or related method has been published there is little impetus from other researchers to adopt such methods. This is usually due to the lack of transparency in some methods and the lack of availability of specific coding for running advanced ML methods. This paper reviews critically the application of ML methods to percutaneous absorption and addresses the key issue of transparency by describing in detail - and providing the detailed coding for - the process of running a ML method (in this case, a Gaussian process regression method). Although this method is applied here to the field of percutaneous absorption, it may be applied more broadly to any biological system.

Student Achievement in Basic College Mathematics: Its Relationship to Learning Style and Learning Method

Science.gov (United States)

Gunthorpe, Sydney

2006-01-01

From the assumption that matching a student's learning style with the learning method best suited for the student, it follows that developing courses that correlate learning method with learning style would be more successful for students. Albuquerque Technical Vocational Institute (TVI) in New Mexico has attempted to provide students with more…

Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective.

Science.gov (United States)

Kang, John; Schwartz, Russell; Flickinger, John; Beriwal, Sushil

2015-12-01

Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, "spam" filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the "barrier to entry" for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods--logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)--and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation. Copyright © 2015 Elsevier Inc. All rights reserved.

MLitB: machine learning in the browser

Directory of Open Access Journals (Sweden)

Edward Meeds

2015-07-01

Full Text Available With few exceptions, the field of Machine Learning (ML research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticated ML learning and prediction to the public at large. This paper introduces MLitB, a prototype ML framework written entirely in Javascript, capable of performing large-scale distributed computing with heterogeneous classes of devices. The development of MLitB has been driven by several underlying objectives whose aim is to make ML learning and usage ubiquitous (by using ubiquitous compute devices, cheap and effortlessly distributed, and collaborative. This is achieved by allowing every internet capable device to run training algorithms and predictive models with no software installation and by saving models in universally readable formats. Our prototype library is capable of training deep neural networks with synchronized, distributed stochastic gradient descent. MLitB offers several important opportunities for novel ML research, including: development of distributed learning algorithms, advancement of web GPU algorithms, novel field and mobile applications, privacy preserving computing, and green grid-computing. MLitB is available as open source software.

Supervised Machine Learning for Population Genetics: A New Paradigm

Science.gov (United States)

Schrider, Daniel R.; Kern, Andrew D.

2018-01-01

As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics. PMID:29331490

jmzIdentML API: A Java interface to the mzIdentML standard for peptide and protein identification data.

Science.gov (United States)

Reisinger, Florian; Krishna, Ritesh; Ghali, Fawaz; Ríos, Daniel; Hermjakob, Henning; Vizcaíno, Juan Antonio; Jones, Andrew R

2012-03-01

We present a Java application programming interface (API), jmzIdentML, for the Human Proteome Organisation (HUPO) Proteomics Standards Initiative (PSI) mzIdentML standard for peptide and protein identification data. The API combines the power of Java Architecture of XML Binding (JAXB) and an XPath-based random-access indexer to allow a fast and efficient mapping of extensible markup language (XML) elements to Java objects. The internal references in the mzIdentML files are resolved in an on-demand manner, where the whole file is accessed as a random-access swap file, and only the relevant piece of XMLis selected for mapping to its corresponding Java object. The APIis highly efficient in its memory usage and can handle files of arbitrary sizes. The APIfollows the official release of the mzIdentML (version 1.1) specifications and is available in the public domain under a permissive licence at http://www.code.google.com/p/jmzidentml/. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Machine learning improves the accuracy of myocardial perfusion scintigraphy results

International Nuclear Information System (INIS)

Groselj, C.; Kukar, M.

2002-01-01

Objective: Machine learning (ML) an artificial intelligence method has in last decade proved to be an useful tool in many fields of decision making, also in some fields of medicine. By reports, its decision accuracy usually exceeds the human one. Aim: To assess applicability of ML in interpretation of the stress myocardial perfusion scintigraphy results in coronary artery disease diagnostic process. Patients and methods: The 327 patient's data of planar stress myocardial perfusion scintigraphy were reevaluated in usual way. Comparing them with the results of coronary angiography the sensitivity, specificity and accuracy of the investigation were computed. The data were digitized and the decision procedure repeated by ML program 'Naive Bayesian classifier'. As the ML is able to simultaneously manipulate with whatever number of data, all reachable disease connected data (regarding history, habitus, risk factors, stress results) were added. The sensitivity, specificity and accuracy of scintigraphy were expressed in this way. The results of both decision procedures were compared. Conclusion: Using ML method, 19 more patients out of 327 (5.8%) were correctly diagnosed by stress myocardial perfusion scintigraphy. In this way ML could be an important tool for myocardial perfusion scintigraphy decision making

The evolution of the CUAHSI Water Markup Language (WaterML)

Science.gov (United States)

Zaslavsky, I.; Valentine, D.; Maidment, D.; Tarboton, D. G.; Whiteaker, T.; Hooper, R.; Kirschtel, D.; Rodriguez, M.

2009-04-01

available for spatial and semantics-based queries. The main component of interoperability across hydrologic data repositories in CUAHSI HIS is mapping different repository schemas and semantics to a shared community information model for observations made at stationary points. This information model has been implemented as both a relational schema (ODM) and an XML schema (WaterML). Its main design drivers have been data storage and data interchange needs of hydrology researchers, a series of community reviews of the ODM, and the practices of hydrologic data modeling and presentation adopted by federal agencies as observed in agency online data access applications, such as NWISWeb and USEPA STORET. The goal of the first version of WaterML was to encode the semantics of hydrologic observations discovery and retrieval and implement water data services in a way that is generic across different data providers. In particular, this implied maintaining a single common representation for the key constructs returned to web service calls, related to observations, features of interest, observation procedures, observation series, etc. Another WaterML design consideration was to create (in version 1 of CUAHSI HIS in particular) a fairly rigid, compact, and simple XML schema which was easy to generate and parse, thus creating the least barrier for adoption by hydrologists. Each of the three main request methods in the water data web services - GetSiteInfo, GetVariableInfo, and GetValues - has a corresponding response element in WaterML: SiteResponse, VariableResponse, and TimeSeriesResponse. The strictness and compactness of the first version of WaterML supported its community adoption. Over the last two years, several ODM and WaterML implementations for various platforms have emerged, and several Water Data Services client applications have been created by outside groups in both industry and academia. In a significant development, the WaterML specification has been adopted by federal

A Swarm-Based Learning Method Inspired by Social Insects

Science.gov (United States)

He, Xiaoxian; Zhu, Yunlong; Hu, Kunyuan; Niu, Ben

Inspired by cooperative transport behaviors of ants, on the basis of Q-learning, a new learning method, Neighbor-Information-Reference (NIR) learning method, is present in the paper. This is a swarm-based learning method, in which principles of swarm intelligence are strictly complied with. In NIR learning, the i-interval neighbor's information, namely its discounted reward, is referenced when an individual selects the next state, so that it can make the best decision in a computable local neighborhood. In application, different policies of NIR learning are recommended by controlling the parameters according to time-relativity of concrete tasks. NIR learning can remarkably improve individual efficiency, and make swarm more "intelligent".

Methods for control over learning individual trajectory

Science.gov (United States)

Mitsel, A. A.; Cherniaeva, N. V.

2015-09-01

The article discusses models, methods and algorithms of determining student's optimal individual educational trajectory. A new method of controlling the learning trajectory has been developed as a dynamic model of learning trajectory control, which uses score assessment to construct a sequence of studied subjects.

PepArML: A Meta-Search Peptide Identification Platform for Tandem Mass Spectra.

Science.gov (United States)

Edwards, Nathan J

2013-12-01

The PepArML meta-search peptide identification platform for tandem mass spectra provides a unified search interface to seven search engines; a robust cluster, grid, and cloud computing scheduler for large-scale searches; and an unsupervised, model-free, machine-learning-based result combiner, which selects the best peptide identification for each spectrum, estimates false-discovery rates, and outputs pepXML format identifications. The meta-search platform supports Mascot; Tandem with native, k-score and s-score scoring; OMSSA; MyriMatch; and InsPecT with MS-GF spectral probability scores—reformatting spectral data and constructing search configurations for each search engine on the fly. The combiner selects the best peptide identification for each spectrum based on search engine results and features that model enzymatic digestion, retention time, precursor isotope clusters, mass accuracy, and proteotypic peptide properties, requiring no prior knowledge of feature utility or weighting. The PepArML meta-search peptide identification platform often identifies two to three times more spectra than individual search engines at 10% FDR.

AllerML: markup language for allergens.

Science.gov (United States)

Ivanciuc, Ovidiu; Gendel, Steven M; Power, Trevor D; Schein, Catherine H; Braun, Werner

2011-06-01

Many concerns have been raised about the potential allergenicity of novel, recombinant proteins into food crops. Guidelines, proposed by WHO/FAO and EFSA, include the use of bioinformatics screening to assess the risk of potential allergenicity or cross-reactivities of all proteins introduced, for example, to improve nutritional value or promote crop resistance. However, there are no universally accepted standards that can be used to encode data on the biology of allergens to facilitate using data from multiple databases in this screening. Therefore, we developed AllerML a markup language for allergens to assist in the automated exchange of information between databases and in the integration of the bioinformatics tools that are used to investigate allergenicity and cross-reactivity. As proof of concept, AllerML was implemented using the Structural Database of Allergenic Proteins (SDAP; http://fermi.utmb.edu/SDAP/) database. General implementation of AllerML will promote automatic flow of validated data that will aid in allergy research and regulatory analysis. Copyright © 2011 Elsevier Inc. All rights reserved.

The Guided Autobiography Method: A Learning Experience

Science.gov (United States)

Thornton, James E.

2008-01-01

This article discusses the proposition that learning is an unexplored feature of the guided autobiography method and its developmental exchange. Learning, conceptualized and explored as the embedded and embodied processes, is essential in narrative activities of the guided autobiography method leading to psychosocial development and growth in…

Introducing the Collaborative E-Learning Design Method (CoED)

DEFF Research Database (Denmark)

Ryberg, Thomas; Buus, Lillian; Nyvang, Tom

2015-01-01

In this chapter, a specific learning design method is introduced and explained, namely the Collaborative E-learning Design method (CoED), which has been developed through various projects in “e-Learning Lab – Centre for User Driven Innovation, Learning and Design” (Nyvang & Georgsen, 2007). We br...

Are students' impressions of improved learning through active learning methods reflected by improved test scores?

Science.gov (United States)

Everly, Marcee C

2013-02-01

To report the transformation from lecture to more active learning methods in a maternity nursing course and to evaluate whether student perception of improved learning through active-learning methods is supported by improved test scores. The process of transforming a course into an active-learning model of teaching is described. A voluntary mid-semester survey for student acceptance of the new teaching method was conducted. Course examination results, from both a standardized exam and a cumulative final exam, among students who received lecture in the classroom and students who had active learning activities in the classroom were compared. Active learning activities were very acceptable to students. The majority of students reported learning more from having active-learning activities in the classroom rather than lecture-only and this belief was supported by improved test scores. Students who had active learning activities in the classroom scored significantly higher on a standardized assessment test than students who received lecture only. The findings support the use of student reflection to evaluate the effectiveness of active-learning methods and help validate the use of student reflection of improved learning in other research projects. Copyright © 2011 Elsevier Ltd. All rights reserved.

Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective

International Nuclear Information System (INIS)

Kang, John; Schwartz, Russell; Flickinger, John; Beriwal, Sushil

2015-01-01

Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, “spam” filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the “barrier to entry” for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods—logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)—and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation.

A Scale Development for Teacher Competencies on Cooperative Learning Method

Science.gov (United States)

Kocabas, Ayfer; Erbil, Deniz Gokce

2017-01-01

Cooperative learning method is a learning method studied both in Turkey and in the world for long years as an active learning method. Although cooperative learning method takes place in training programs, it cannot be implemented completely in the direction of its principles. The results of the researches point out that teachers have problems with…

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions.

Science.gov (United States)

Kassahun, Yohannes; Yu, Bingbin; Tibebu, Abraham Temesgen; Stoyanov, Danail; Giannarou, Stamatia; Metzen, Jan Hendrik; Vander Poorten, Emmanuel

2016-04-01

Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

Science.gov (United States)

S K, Somasundaram; P, Alli

2017-11-09

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed. Recently, few research works have been designed for analyzing texture discrimination capacity in FI to distinguish the healthy images. However, the feature extraction (FE) process was not performed well, due to the high dimensionality. Therefore, to identify retinal features for DR disease diagnosis and early detection using Machine Learning and Ensemble Classification method, called, Machine Learning Bagging Ensemble Classifier (ML-BEC) is designed. The ML-BEC method comprises of two stages. The first stage in ML-BEC method comprises extraction of the candidate objects from Retinal Images (RI). The candidate objects or the features for DR disease diagnosis include blood vessels, optic nerve, neural tissue, neuroretinal rim, optic disc size, thickness and variance. These features are initially extracted by applying Machine Learning technique called, t-distributed Stochastic Neighbor Embedding (t-SNE). Besides, t-SNE generates a probability distribution across high-dimensional images where the images are separated into similar and dissimilar pairs. Then, t-SNE describes a similar probability distribution across the points in the low-dimensional map. This lessens the Kullback-Leibler divergence among two distributions regarding the locations of the points on the map. The second stage comprises of application of ensemble classifiers to the extracted features for providing accurate analysis of digital FI using machine learning. In this stage, an automatic detection

Introduction of active learning method in learning physiology by MBBS students.

Science.gov (United States)

Gilkar, Suhail Ahmad; Lone, Shabiruddin; Lone, Riyaz Ahmad

2016-01-01

Active learning has received considerable attention over the past several years, often presented or perceived as a radical change from traditional instruction methods. Current research on learning indicates that using a variety of teaching strategies in the classroom increases student participation and learning. To introduce active learning methodology, i.e., "jigsaw technique" in undergraduate medical education and assess the student and faculty response to it. This study was carried out in the Department of Physiology in a Medical College of North India. A topic was chosen and taught using one of the active learning methods (ALMs), i.e., jigsaw technique. An instrument (questionnaire) was developed in English through an extensive review of literature and was properly validated. The students were asked to give their response on a five-point Likert scale. The feedback was kept anonymous. Faculty also provided their feedback in a separately provided feedback proforma. The data were collected, compiled, and analyzed. Of 150 students of MBBS-first year batch 2014, 142 participated in this study along with 14 faculty members of the Physiology Department. The majority of the students (>90%) did welcome the introduction of ALM and strongly recommended the use of such methods in teaching many more topics in future. 100% faculty members were of the opinion that many more topics shall be taken up using ALMs. This study establishes the fact that both the medical students and faculty want a change from the traditional way of passive, teacher-centric learning, to the more active teaching-learning techniques.

Implementing Collaborative Learning Methods in the Political Science Classroom

Science.gov (United States)

Wolfe, Angela

2012-01-01

Collaborative learning is one, among other, active learning methods, widely acclaimed in higher education. Consequently, instructors in fields that lack pedagogical training often implement new learning methods such as collaborative learning on the basis of trial and error. Moreover, even though the benefits in academic circles are broadly touted,…

Body surface area adapted iopromide 300 mg/ml versus 370 mg/ml contrast medium injection protocol: Influence on quantitative and clinical assessment in combined PET/CT

Energy Technology Data Exchange (ETDEWEB)

Verburg, Frederik A., E-mail: fverburg@ukaachen.de [RWTH Aachen University Hospital, Department of Nuclear Medicine, Pauwelsstraße 30, 52074 Aachen (Germany); Maastricht University Medical Center, Department of Nuclear Medicine, P. Debyelaan 25, 6229 HX Maastricht (Netherlands); Apitzsch, Jonas [RWTH Aachen University Hospital, Department of Diagnostic and Interventional Radiology, Pauwelsstraße 30, 52074 Aachen (Germany); Lensing, Carina [RWTH Aachen University Hospital, Department of Nuclear Medicine, Pauwelsstraße 30, 52074 Aachen (Germany); Kuhl, Christiane K. [RWTH Aachen University Hospital, Department of Diagnostic and Interventional Radiology, Pauwelsstraße 30, 52074 Aachen (Germany); Pietsch, Hubertus [Bayer Pharma AG, Müllerstrasse 178, 13353 Berlin (Germany); Mottaghy, Felix M. [RWTH Aachen University Hospital, Department of Nuclear Medicine, Pauwelsstraße 30, 52074 Aachen (Germany); Maastricht University Medical Center, Department of Nuclear Medicine, P. Debyelaan 25, 6229 HX Maastricht (Netherlands); Behrendt, Florian F. [RWTH Aachen University Hospital, Department of Nuclear Medicine, Pauwelsstraße 30, 52074 Aachen (Germany)

2013-12-01

Purpose: To investigate the quantitative and qualitative differences between combined positron emission tomography and computed X-ray tomography (PET/CT) enhanced with contrast medium with either an iodine concentration 300 mg/ml or 370 mg/ml. Materials and methods: 120 consecutive patients scheduled for F-18-Fluorodeoxyglucose (FDG) PET/CT were included. The first (second) 60 patients received contrast medium with 300 (370) mg iodine/ml. Intravenous injection protocols were adapted for an identical iodine delivery rate (1.3 mg/s) and body surface area (BSA) adapted iodine dose (22.26 g I/m{sup 2}). Maximum and mean standardized uptake values (SUV{sub max}; SUV{sub mean}) and contrast enhancement (HU) were determined in the ascending aorta, the abdominal aorta, the inferior vena cava, the portal vein, the liver and the right kidney in the venous contrast medium phase. PET data were evaluated visually for the presence of malignancy and image quality. Results: Both media caused significantly higher values for HU, SUV{sub mean} and SUV{sub max} for the enhanced PET/CT than the non-enhanced one (all p < 0.01). There were no significant differences in the degree of increase of HU, SUV{sub mean} and SUV{sub max} between the two contrast media at any anatomic site (all p > 0.05). Visual evaluation of lesions showed no differences between contrast and non-contrast PET/CT or between the two different contrast media (p = 0.77). Conclusion: When using a constant iodine delivery rate and total iodine dose in a BSA adapted injection protocol, there are no quantitative or qualitative differences in either CT or PET between contrast media with an iodine concentration of 300 mg/ml and 370 mg/ml, respectively.

Lower tidal volume strategy (?3?ml/kg) combined with extracorporeal CO2 removal versus ?conventional? protective ventilation (6?ml/kg) in severe ARDS

OpenAIRE

Bein, Thomas; Weber-Carstens, Steffen; Goldmann, Anton; M?ller, Thomas; Staudinger, Thomas; Brederlau, J?rg; Muellenbach, Ralf; Dembinski, Rolf; Graf, Bernhard M.; Wewalka, Marlene; Philipp, Alois; Wernecke, Klaus-Dieter; Lubnow, Matthias; Slutsky, Arthur S.

2013-01-01

Background Acute respiratory distress syndrome is characterized by damage to the lung caused by various insults, including ventilation itself, and tidal hyperinflation can lead to ventilator induced lung injury (VILI). We investigated the effects of a low tidal volume (V T) strategy (V T???3?ml/kg/predicted body weight [PBW]) using pumpless extracorporeal lung assist in established ARDS. Methods Seventy-nine patients were enrolled after a ?stabilization period? (24?h with optimized therapy an...

Learning styles: The learning methods of air traffic control students

Science.gov (United States)

Jackson, Dontae L.

In the world of aviation, air traffic controllers are an integral part in the overall level of safety that is provided. With a number of controllers reaching retirement age, the Air Traffic Collegiate Training Initiative (AT-CTI) was created to provide a stronger candidate pool. However, AT-CTI Instructors have found that a number of AT-CTI students are unable to memorize types of aircraft effectively. This study focused on the basic learning styles (auditory, visual, and kinesthetic) of students and created a teaching method to try to increase memorization in AT-CTI students. The participants were asked to take a questionnaire to determine their learning style. Upon knowing their learning styles, participants attended two classroom sessions. The participants were given a presentation in the first class, and divided into a control and experimental group for the second class. The control group was given the same presentation from the first classroom session while the experimental group had a group discussion and utilized Middle Tennessee State University's Air Traffic Control simulator to learn the aircraft types. Participants took a quiz and filled out a survey, which tested the new teaching method. An appropriate statistical analysis was applied to determine if there was a significant difference between the control and experimental groups. The results showed that even though the participants felt that the method increased their learning, there was no significant difference between the two groups.

[Which learning methods are expected for ultrasound training? Blended learning on trial].

Science.gov (United States)

Röhrig, S; Hempel, D; Stenger, T; Armbruster, W; Seibel, A; Walcher, F; Breitkreutz, R

2014-10-01

Current teaching methods in graduate and postgraduate training often include frontal presentations. Especially in ultrasound education not only knowledge but also sensomotory and visual skills need to be taught. This requires new learning methods. This study examined which types of teaching methods are preferred by participants in ultrasound training courses before, during and after the course by analyzing a blended learning concept. It also investigated how much time trainees are willing to spend on such activities. A survey was conducted at the end of a certified ultrasound training course. Participants were asked to complete a questionnaire based on a visual analogue scale (VAS) in which three categories were defined: category (1) vote for acceptance with a two thirds majority (VAS 67-100%), category (2) simple acceptance (50-67%) and category (3) rejection (learning program with interactive elements, short presentations (less than 20 min), incorporating interaction with the audience, hands-on sessions in small groups, an alternation between presentations and hands-on-sessions, live demonstrations and quizzes. For post-course learning, interactive and media-assisted approaches were preferred, such as e-learning, films of the presentations and the possibility to stay in contact with instructors in order to discuss the results. Participants also voted for maintaining a logbook for documentation of results. The results of this study indicate the need for interactive learning concepts and blended learning activities. Directors of ultrasound courses may consider these aspects and are encouraged to develop sustainable learning pathways.

Is a volume of 3.6 mL better than 1.8 mL for inferior alveolar nerve blocks in patients with symptomatic irreversible pulpitis?

Science.gov (United States)

Fowler, Sara; Reader, Al

2013-08-01

The purpose of this retrospective study was to determine the success of the inferior alveolar nerve (IAN) block using either 3.6 mL or 1.8 mL 2% lidocaine with 1:100,000 epinephrine in patients presenting with symptomatic irreversible pulpitis. As part of 7 previously published studies, 319 emergency patients presenting with symptomatic irreversible pulpitis received either a 1.8-mL volume or 3.6-mL volume of 2% lidocaine with 1:100,000 epinephrine in an IAN block. One hundred ninety patients received a 1.8-mL volume, and 129 received a 3.6-mL volume. Endodontic emergency treatment was completed on each subject. Success was defined as the ability to access and instrument the tooth without pain (visual analog scale score of 0) or mild pain (VAS rating ≤54 mm). Success of the 1.8-mL volume was 28%, and for the 3.6-mL volume it was 39%. There was no statistically significant difference between the 2 volumes. In conclusion, for patients presenting with irreversible pulpitis, success was not significantly different between a 3.6-mL volume and a 1.8-mL volume of 2% lidocaine with 1:100,000 epinephrine. The success rates (28%-39%) with either volume were not high enough to ensure complete pulpal anesthesia. Copyright © 2013 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

ML-MG: Multi-label Learning with Missing Labels Using a Mixed Graph

KAUST Repository

Wu, Baoyuan; Lyu, Siwei; Ghanem, Bernard

2015-01-01

This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e. some

Decomposition methods for unsupervised learning

DEFF Research Database (Denmark)

Mørup, Morten

2008-01-01

This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...

Effect of Chemistry Triangle Oriented Learning Media on Cooperative, Individual and Conventional Method on Chemistry Learning Result

Science.gov (United States)

Latisma D, L.; Kurniawan, W.; Seprima, S.; Nirbayani, E. S.; Ellizar, E.; Hardeli, H.

2018-04-01

The purpose of this study was to see which method are well used with the Chemistry Triangle-oriented learning media. This quasi experimental research involves first grade of senior high school students in six schools namely each two SMA N in Solok city, in Pasaman and two SMKN in Pariaman. The sampling technique was done by Cluster Random Sampling. Data were collected by test and analyzed by one-way anova and Kruskall Wallish test. The results showed that the high school students in Solok learning taught by cooperative method is better than the results of student learning taught by conventional and Individual methods, both for students who have high initial ability and low-ability. Research in SMK showed that the overall student learning outcomes taught by conventional method is better than the student learning outcomes taught by cooperative and individual methods. Student learning outcomes that have high initial ability taught by individual method is better than student learning outcomes that are taught by cooperative method and for students who have low initial ability, there is no difference in student learning outcomes taught by cooperative, individual and conventional methods. Learning in high school in Pasaman showed no significant difference in learning outcomes of the three methods undertaken.

Efficient model learning methods for actor-critic control.

Science.gov (United States)

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

Statistical learning methods in high-energy and astrophysics analysis

Energy Technology Data Exchange (ETDEWEB)

Zimmermann, J. [Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de; Kiesling, C. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)

2004-11-21

We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application.

Statistical learning methods in high-energy and astrophysics analysis

International Nuclear Information System (INIS)

Zimmermann, J.; Kiesling, C.

2004-01-01

We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application

Malignant lymphomas (ML) and HIV infection in Tanzania.

Science.gov (United States)

Mwakigonja, Amos R; Kaaya, Ephata E; Mgaya, Edward M

2008-06-10

HIV infection is reported to be associated with some malignant lymphomas (ML) so called AIDS-related lymphomas (ARL), with an aggressive behavior and poor prognosis. The ML frequency, pathogenicity, clinical patterns and possible association with AIDS in Tanzania, are not well documented impeding the development of preventive and therapeutic strategies. Sections of 176 archival formalin-fixed paraffin-embedded biopsies of ML patients at Muhimbili National Hospital (MNH)/Muhimbili University of Health and Allied Sciences (MUHAS), Tanzania from 1996-2001 were stained for hematoxylin and eosin and selected (70) cases for expression of pan-leucocytic (CD45), B-cell (CD20), T-cell (CD3), Hodgkin/RS cell (CD30), histiocyte (CD68) and proliferation (Ki-67) antigen markers. Corresponding clinical records were also evaluated. Available sera from 38 ML patients were screened (ELISA) for HIV antibodies. The proportion of ML out of all diagnosed tumors at MNH during the 6 year period was 4.2% (176/4200) comprising 77.84% non-Hodgkin (NHL) including 19.32% Burkitt's (BL) and 22.16% Hodgkin's disease (HD). The ML tumors frequency increased from 0.42% (1997) to 0.70% (2001) and 23.7% of tested sera from these patients were HIV positive. The mean age for all ML was 30, age-range 3-91 and peak age was 1-20 years. The male:female ratio was 1.8:1. Supra-diaphragmatic presentation was commonest and histological sub-types were mostly aggressive B-cell lymphomas however, no clear cases of primary effusion lymphoma (PEL) and primary central nervous system lymphoma (PCNSL) were diagnosed. Malignant lymphomas apparently, increased significantly among diagnosed tumors at MNH between 1996 and 2001, predominantly among the young, HIV infected and AIDS patients. The frequent aggressive clinical and histological presentation as well as the dominant B-immunophenotype and the HIV serology indicate a pathogenic association with AIDS. Therefore, routine HIV screening of all malignant lymphoma

Active teaching methods, studying responses and learning

DEFF Research Database (Denmark)

Christensen, Hans Peter; Vigild, Martin Etchells; Thomsen, Erik Vilain

2010-01-01

Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed.......Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed....

Enriching behavioral ecology with reinforcement learning methods.

Science.gov (United States)

Frankenhuis, Willem E; Panchanathan, Karthik; Barto, Andrew G

2018-02-13

This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Post treatment PSA nadirs support continuing dose escalation study in patients with pretreatment PSA levels >10 ng/ml, but not in those with PSA <10 NG/ML

International Nuclear Information System (INIS)

Herold, D.H.; Hanlon, A.L.; Movsas, B.; Hanks, G.E.

1996-01-01

Purpose: We have recently shown that ICRU reporting point radiation doses above 71 Gy are not associated with improved bNED survival in prostate cancer patients with pretreatment PSA level 20 ng/ml we found a strong correlation between dose and nadir values < 1.0 ng/ml (p=.003) as well as for nadir's < 0.5 ng/ml (p=.04). This dose/nadir effect held at several dose levels, but 74 Gy for nadir values < 1.0 ng/ml and 72 Gy for nadir's < 0.5 ng/ml remained the most significant. 32% of these patients achieved a nadir < 1.0ng/ml and 15% < 0.5ng/ml. Conclusions: This analysis provides strong additional support that patients with pretreatment PSA values of < 10 ng/ml do not benefit from dose escalation beyond an ICRU reporting point dose of 71 Gy. For patients with pretreatment PSA's of 10-19.9 ng/ml there is no dose/nadir response evaluated at a nadir of 1.0 ng/ml; however, there is a borderline effect observed at a nadir of 0.5 ng/ml. Patients with pretreatment PSA's of 20 ng/ml or greater clearly benefit from higher doses as evaluated by PSA nadirs of 1.0 ng/ml, and 0.5 ng/ml. These studies support the continued investigation of dose escalation in treating patients with PSA levels over 10 ng/ml, they do not support continued investigation of dose escalation beyond 71 Gy in patients with pretreatment PSA levels < 10 ng/ml. The failure to demonstrate any dose response for the low PSA group and the finding of only a borderline effect for the intermediate PSA group may be influenced by the relatively small number of patients in our series treated to doses < 70 Gy and the fact that none of our patients were treated to doses below 65.98 Gy. The lower limit of acceptible dose has yet to be defined

Deep learning methods for protein torsion angle prediction.

Science.gov (United States)

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

FLIPPED CLASSROOM LEARNING METHOD TO IMPROVE CARING AND LEARNING OUTCOME IN FIRST YEAR NURSING STUDENT

Directory of Open Access Journals (Sweden)

Ni Putu Wulan Purnama Sari

2017-08-01

Full Text Available Background and Purpose: Caring is the essence of nursing profession. Stimulation of caring attitude should start early. Effective teaching methods needed to foster caring attitude and improve learning achievement. This study aimed to explain the effect of applying flipped classroom learning method for improving caring attitude and learning achievement of new student nurses at nursing institutions in Surabaya. Method: This is a pre-experimental study using the one group pretest posttest and posttest only design. Population was all new student nurses on nursing institutions in Surabaya. Inclusion criteria: female, 18-21 years old, majoring in nursing on their own volition and being first choice during students selection process, status were active in the even semester of 2015/2016 academic year. Sample size was 67 selected by total sampling. Variables: 1 independent: application of flipped classroom learning method; 2 dependent: caring attitude, learning achievement. Instruments: teaching plan, assignment descriptions, presence list, assignment assessment rubrics, study materials, questionnaires of caring attitude. Data analysis: paired and one sample t test. Ethical clearance was available. Results: Most respondents were 20 years old (44.8%, graduated from high school in Surabaya (38.8%, living with parents (68.7% in their homes (64.2%. All data were normally distributed. Flipped classroom learning method could improve caring attitude by 4.13%. Flipped classroom learning method was proved to be effective for improving caring attitude (p=0.021 and learning achievement (p=0.000. Conclusion and Recommendation: Flipped classroom was effective for improving caring attitude and learning achievement of new student nurse. It is recommended to use mix-method and larger sample for further study.

Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial.

Science.gov (United States)

McCabe, Jessica; Monkiewicz, Michelle; Holcomb, John; Pundik, Svetlana; Daly, Janis J

2015-06-01

To compare response to upper-limb treatment using robotics plus motor learning (ML) versus functional electrical stimulation (FES) plus ML versus ML alone, according to a measure of complex functional everyday tasks for chronic, severely impaired stroke survivors. Single-blind, randomized trial. Medical center. Enrolled subjects (N=39) were >1 year postsingle stroke (attrition rate=10%; 35 completed the study). All groups received treatment 5d/wk for 5h/d (60 sessions), with unique treatment as follows: ML alone (n=11) (5h/d partial- and whole-task practice of complex functional tasks), robotics plus ML (n=12) (3.5h/d of ML and 1.5h/d of shoulder/elbow robotics), and FES plus ML (n=12) (3.5h/d of ML and 1.5h/d of FES wrist/hand coordination training). Primary measure: Arm Motor Ability Test (AMAT), with 13 complex functional tasks; secondary measure: upper-limb Fugl-Meyer coordination scale (FM). There was no significant difference found in treatment response across groups (AMAT: P≥.584; FM coordination: P≥.590). All 3 treatment groups demonstrated clinically and statistically significant improvement in response to treatment (AMAT and FM coordination: P≤.009). A group treatment paradigm of 1:3 (therapist/patient) ratio proved feasible for provision of the intensive treatment. No adverse effects. Severely impaired stroke survivors with persistent (>1y) upper-extremity dysfunction can make clinically and statistically significant gains in coordination and functional task performance in response to robotics plus ML, FES plus ML, and ML alone in an intensive and long-duration intervention; no group differences were found. Additional studies are warranted to determine the effectiveness of these methods in the clinical setting. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Superconducting magnet for 'ML-100'

Energy Technology Data Exchange (ETDEWEB)

Saito, R; Fujinaga, T; Tada, N; Kimura, H

1974-07-01

A magneticaly levitated experimental vehicle (Ml-100) was designed and constructed in commemoration of the centenary of the Japanese National Railways. For magnetic levitation the vehicle is provided with two superconducting magnets. In the test operation of the vehicle, these superconducting magnets showed stable performance in levitating vehicle body.

Challenges in the Verification of Reinforcement Learning Algorithms

Science.gov (United States)

Van Wesel, Perry; Goodloe, Alwyn E.

2017-01-01

Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the specific challenges of verifying reinforcement learning algorithms. These are highlighted using a specific example. Ultimately, we do not offer a solution to the complex problem of ML verification, but point out possible approaches for verification and interesting research opportunities.

A Comparison between the Effect of Cooperative Learning Teaching Method and Lecture Teaching Method on Students' Learning and Satisfaction Level

Science.gov (United States)

Mohammadjani, Farzad; Tonkaboni, Forouzan

2015-01-01

The aim of the present research is to investigate a comparison between the effect of cooperative learning teaching method and lecture teaching method on students' learning and satisfaction level. The research population consisted of all the fourth grade elementary school students of educational district 4 in Shiraz. The statistical population…

A cross-benchmark comparison of 87 learning to rank methods

NARCIS (Netherlands)

Tax, N.; Bockting, S.; Hiemstra, D.

2015-01-01

Learning to rank is an increasingly important scientific field that comprises the use of machine learning for the ranking task. New learning to rank methods are generally evaluated on benchmark test collections. However, comparison of learning to rank methods based on evaluation results is hindered

Machine learning for epigenetics and future medical applications.

Science.gov (United States)

Holder, Lawrence B; Haque, M Muksitul; Skinner, Michael K

2017-07-03

Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.

Modelling CRM implementation services with SysML

OpenAIRE

Bibiano, Luis H.; Pastor Collado, Juan Antonio; Mayol Sarroca, Enric

2009-01-01

CRM information systems are valuable tools for enterprises. But CRM implementation projects are risky and present a high failure rate. In this paper we regard CRM implementation projects as services that could be greatly improved by addressing them in a methodological way that can be designed with the help of tools such as SysML. Here we introduce and comment on our first experience on the use of SysML language, not very well known, for modelling the elements involved in the CRM implementatio...

Color image definition evaluation method based on deep learning method

Science.gov (United States)

Liu, Di; Li, YingChun

2018-01-01

In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

Effect of normalization methods on the performance of supervised learning algorithms applied to HTSeq-FPKM-UQ data sets: 7SK RNA expression as a predictor of survival in patients with colon adenocarcinoma.

Science.gov (United States)

Shahriyari, Leili

2017-11-03

One of the main challenges in machine learning (ML) is choosing an appropriate normalization method. Here, we examine the effect of various normalization methods on analyzing FPKM upper quartile (FPKM-UQ) RNA sequencing data sets. We collect the HTSeq-FPKM-UQ files of patients with colon adenocarcinoma from TCGA-COAD project. We compare three most common normalization methods: scaling, standardizing using z-score and vector normalization by visualizing the normalized data set and evaluating the performance of 12 supervised learning algorithms on the normalized data set. Additionally, for each of these normalization methods, we use two different normalization strategies: normalizing samples (files) or normalizing features (genes). Regardless of normalization methods, a support vector machine (SVM) model with the radial basis function kernel had the maximum accuracy (78%) in predicting the vital status of the patients. However, the fitting time of SVM depended on the normalization methods, and it reached its minimum fitting time when files were normalized to the unit length. Furthermore, among all 12 learning algorithms and 6 different normalization techniques, the Bernoulli naive Bayes model after standardizing files had the best performance in terms of maximizing the accuracy as well as minimizing the fitting time. We also investigated the effect of dimensionality reduction methods on the performance of the supervised ML algorithms. Reducing the dimension of the data set did not increase the maximum accuracy of 78%. However, it leaded to discovery of the 7SK RNA gene expression as a predictor of survival in patients with colon adenocarcinoma with accuracy of 78%. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A Learning Method for Neural Networks Based on a Pseudoinverse Technique

Directory of Open Access Journals (Sweden)

Chinmoy Pal

1996-01-01

Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician's Perspective

Energy Technology Data Exchange (ETDEWEB)

Kang, John [Medical Scientist Training Program, University of Pittsburgh-Carnegie Mellon University, Pittsburgh, Pennsylvania (United States); Schwartz, Russell [Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania (United States); Flickinger, John [Departments of Radiation Oncology and Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (United States); Beriwal, Sushil, E-mail: beriwals@upmc.edu [Department of Radiation Oncology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania (United States)

2015-12-01

Radiation oncology has always been deeply rooted in modeling, from the early days of isoeffect curves to the contemporary Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) initiative. In recent years, medical modeling for both prognostic and therapeutic purposes has exploded thanks to increasing availability of electronic data and genomics. One promising direction that medical modeling is moving toward is adopting the same machine learning methods used by companies such as Google and Facebook to combat disease. Broadly defined, machine learning is a branch of computer science that deals with making predictions from complex data through statistical models. These methods serve to uncover patterns in data and are actively used in areas such as speech recognition, handwriting recognition, face recognition, “spam” filtering (junk email), and targeted advertising. Although multiple radiation oncology research groups have shown the value of applied machine learning (ML), clinical adoption has been slow due to the high barrier to understanding these complex models by clinicians. Here, we present a review of the use of ML to predict radiation therapy outcomes from the clinician's point of view with the hope that it lowers the “barrier to entry” for those without formal training in ML. We begin by describing 7 principles that one should consider when evaluating (or creating) an ML model in radiation oncology. We next introduce 3 popular ML methods—logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN)—and critique 3 seminal papers in the context of these principles. Although current studies are in exploratory stages, the overall methodology has progressively matured, and the field is ready for larger-scale further investigation.

IP-MLI: An Independency of Learning Materials from Platforms in a Mobile Learning using Intelligent Method

Directory of Open Access Journals (Sweden)

Mohammed Abdallh Otair

2006-06-01

Full Text Available Attempting to deliver a monolithic mobile learning system is too inflexible in view of the heterogeneous mixture of hardware and services available and the desirability of facility blended approaches to learning delivery, and how to build learning materials to run on all platforms[1]. This paper proposes a framework of mobile learning system using an intelligent method (IP-MLI . A fuzzy matching method is used to find suitable learning material design. It will provide a best matching for each specific platform type for each learner. The main contribution of the proposed method is to use software layer to insulate learning materials from device-specific features. Consequently, many versions of learning materials can be designed to work on many platform types.

Machine learning applications in cancer prognosis and prediction.

Science.gov (United States)

Kourou, Konstantina; Exarchos, Themis P; Exarchos, Konstantinos P; Karamouzis, Michalis V; Fotiadis, Dimitrios I

2015-01-01

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

IrML – a gene encoding a new member of the ML protein family from the hard tick, Ixodes ricinus

Czech Academy of Sciences Publication Activity Database

Horáčková, J.; Rudenko, Natalia; Golovchenko, Maryna; Havlíková, S.; Grubhoffer, Libor

2010-01-01

Roč. 35, č. 2 (2010), s. 410-418 ISSN 1081-1710 R&D Projects: GA ČR(CZ) GA524/06/1479; GA MŠk(CZ) LC06009 Institutional research plan: CEZ:AV0Z60220518 Keywords : Ixodes ricinus * tick * ML-domain containing protein * in situ hybridization * gene expression * ML (MD-2-related lipid-recognition) domain Subject RIV: GJ - Animal Vermins ; Diseases, Veterinary Medicine Impact factor: 1.256, year: 2010

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

Directory of Open Access Journals (Sweden)

Li Zhen

2008-05-01

Full Text Available Abstract Background Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex in vitro/in vivo datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML methods. Results The classification performance of Artificial Neural Networks (ANN, K-Nearest Neighbors (KNN, Linear Discriminant Analysis (LDA, Naïve Bayes (NB, Recursive Partitioning and Regression Trees (RPART, and Support Vector Machines (SVM in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated in vitro assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5 were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA. Conclusion We have developed a novel simulation model to evaluate machine learning methods for the

Interactive machine learning for health informatics: when do we need the human-in-the-loop?

Science.gov (United States)

Holzinger, Andreas

2016-06-01

Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as "algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human." This "human-in-the-loop" can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.

ML Arvutite aktsiakapital suurenes / Anne Oja

Index Scriptorium Estoniae

Oja, Anne, 1970-

2006-01-01

ML Arvutid omanik Aivar Paalberg tõstis aktsiakapitali seniselt 10 miljonilt 24 miljonile eesmärgiga tugevdada oma positsioone Eesti turul ja kasvada kiiremas tempos kui kogu turg. Diagramm: Majandusnäitajad

Pragmatics of Contemporary Teaching and Learning Methods

Directory of Open Access Journals (Sweden)

Ryszard Józef Panfil

2013-09-01

Full Text Available The dynamics of the environment in which educational institutions operate have a significant influence on the basic activity of these institutions, i.e. the process of educating, and particularly teaching and learning methods used during that process: traditional teaching, tutoring, mentoring and coaching. The identity of an educational institution and the appeal of its services depend on how flexible, diverse and adaptable is the educational process it offers as a core element of its services. Such a process is determined by how its pragmatism is displayed in the operational relativism of methods, their applicability, as well as practical dimension of achieved results and values. Based on the above premises, this publication offers a pragmatic-systemic identification of contemporary teaching and learning methods, while taking into account the differences between them and the scope of their compatibility. Secondly, using the case of sport coaches’ education, the author exemplifies the pragmatic theory of perception of contemporary teaching and learning methods.

The Method of High School English Word Learning

Institute of Scientific and Technical Information of China (English)

吴博涵

2016-01-01

Most Chinese students are not interested in English learning, especially English words. In this paper, I focus on English vocabulary learning, for example, the study of high school students English word learning method, and also introduce several ways to make vocabulary memory becomes more effective. The purpose is to make high school students grasp more English word learning skills.

e-Learning Business Research Methods

Science.gov (United States)

Cowie, Jonathan

2004-01-01

This paper outlines the development of a generic Business Research Methods course from a simple name in a box to a full e-Learning web based module. It highlights particular issues surrounding the nature of the discipline and the integration of a large number of cross faculty subject specific research methods courses into a single generic module.…

Question presentation methods for paired-associate learning

NARCIS (Netherlands)

Engel, F.L.; Geerings, M.P.W.

1988-01-01

Four different methods of question presentation, in interactive computeraided learning of Dutch-English word pairs are evaluated experimentally. These methods are: 1) the 'open-question method', 2) the 'multiple-choice method', 3) the 'sequential method' and 4) the 'true/ false method'. When

Think Pair Share (TPS as Method to Improve Student’s Learning Motivation and Learning Achievement

Directory of Open Access Journals (Sweden)

Hetika Hetika

2018-03-01

Full Text Available This research aims to find out the application of Think Pair Share (TPS learning method in improving learning motivation and learning achievement in the subject of Introduction to Accounting I of the Accounting Study Program students of Politeknik Harapan Bersama. The Method of data collection in this study used observation method, test method, and documentation method. The research instruments used observation sheet, questionnaire and test question. This research used Class Action Research Design which is an action implementation oriented research, with the aim of improving quality or problem solving in a group by carefully and observing the success rate due to the action. The method of analysis used descriptive qualitative and quantitative analysis method. The results showed that the application of Think Pair Share Learning (TPS Method can improve the Learning Motivation and Achievement. Before the implementation of the action, the obtained score is 67% then in the first cycle increases to 72%, and in the second cycle increasws to 80%. In addition, based on questionnaires distributed to students, it also increases the score of Accounting Learning Motivation where the score in the first cycle of 76% increases to 79%. In addition, in the first cycle, the score of pre test and post test of the students has increased from 68.86 to 76.71 while in the second cycle the score of pre test and post test of students has increased from 79.86 to 84.86.

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter.

Science.gov (United States)

Choi, Jihoon; Shin, Joonho; Song, Choonggeun; Han, Suyong; Park, Doo Il

2017-09-13

This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

Leak Detection and Location of Water Pipes Using Vibration Sensors and Modified ML Prefilter

Directory of Open Access Journals (Sweden)

Jihoon Choi

2017-09-01

Full Text Available This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs and the cross-spectral density (CSD, the proposed method employs a modified maximum-likelihood (ML prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

CytometryML and other data formats

Science.gov (United States)

Leif, Robert C.

2006-02-01

Cytology automation and research will be enhanced by the creation of a common data format. This data format would provide the pathology and research communities with a uniform way for annotating and exchanging images, flow cytometry, and associated data. This specification and/or standard will include descriptions of the acquisition device, staining, the binary representations of the image and list-mode data, the measurements derived from the image and/or the list-mode data, and descriptors for clinical/pathology and research. An international, vendor-supported, non-proprietary specification will allow pathologists, researchers, and companies to develop and use image capture/analysis software, as well as list-mode analysis software, without worrying about incompatibilities between proprietary vendor formats. Presently, efforts to create specifications and/or descriptions of these formats include the Laboratory Digital Imaging Project (LDIP) Data Exchange Specification; extensions to the Digital Imaging and Communications in Medicine (DICOM); Open Microscopy Environment (OME); Flowcyt, an extension to the present Flow Cytometry Standard (FCS); and CytometryML. The feasibility of creating a common data specification for digital microscopy and flow cytometry in a manner consistent with its use for medical devices and interoperability with both hospital information and picture archiving systems has been demonstrated by the creation of the CytometryML schemas. The feasibility of creating a software system for digital microscopy has been demonstrated by the OME. CytometryML consists of schemas that describe instruments and their measurements. These instruments include digital microscopes and flow cytometers. Optical components including the instruments' excitation and emission parts are described. The description of the measurements made by these instruments includes the tagged molecule, data acquisition subsystem, and the format of the list-mode and/or image data. Many

Machine learning for epigenetics and future medical applications

OpenAIRE

Holder, Lawrence B.; Haque, M. Muksitul; Skinner, Michael K.

2017-01-01

ABSTRACT Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems w...

PeakML/mzMatch: a file format, Java library, R library, and tool-chain for mass spectrometry data analysis.

Science.gov (United States)

Scheltema, Richard A; Jankevics, Andris; Jansen, Ritsert C; Swertz, Morris A; Breitling, Rainer

2011-04-01

The recent proliferation of high-resolution mass spectrometers has generated a wealth of new data analysis methods. However, flexible integration of these methods into configurations best suited to the research question is hampered by heterogeneous file formats and monolithic software development. The mzXML, mzData, and mzML file formats have enabled uniform access to unprocessed raw data. In this paper we present our efforts to produce an equally simple and powerful format, PeakML, to uniformly exchange processed intermediary and result data. To demonstrate the versatility of PeakML, we have developed an open source Java toolkit for processing, filtering, and annotating mass spectra in a customizable pipeline (mzMatch), as well as a user-friendly data visualization environment (PeakML Viewer). The PeakML format in particular enables the flexible exchange of processed data between software created by different groups or companies, as we illustrate by providing a PeakML-based integration of the widely used XCMS package with mzMatch data processing tools. As an added advantage, downstream analysis can benefit from direct access to the full mass trace information underlying summarized mass spectrometry results, providing the user with the means to rapidly verify results. The PeakML/mzMatch software is freely available at http://mzmatch.sourceforge.net, with documentation, tutorials, and a community forum.

A best-first tree-searching approach for ML decoding in MIMO system

KAUST Repository

Shen, Chung-An

2012-07-28

In MIMO communication systems maximum-likelihood (ML) decoding can be formulated as a tree-searching problem. This paper presents a tree-searching approach that combines the features of classical depth-first and breadth-first approaches to achieve close to ML performance while minimizing the number of visited nodes. A detailed outline of the algorithm is given, including the required storage. The effects of storage size on BER performance and complexity in terms of search space are also studied. Our result demonstrates that with a proper choice of storage size the proposed method visits 40% fewer nodes than a sphere decoding algorithm at signal to noise ratio (SNR) = 20dB and by an order of magnitude at 0 dB SNR.

Improving Nursing Students' Learning Outcomes in Fundamentals of Nursing Course through Combination of Traditional and e-Learning Methods.

Science.gov (United States)

Sheikhaboumasoudi, Rouhollah; Bagheri, Maryam; Hosseini, Sayed Abbas; Ashouri, Elaheh; Elahi, Nasrin

2018-01-01

Fundamentals of nursing course are prerequisite to providing comprehensive nursing care. Despite development of technology on nursing education, effectiveness of using e-learning methods in fundamentals of nursing course is unclear in clinical skills laboratory for nursing students. The aim of this study was to compare the effect of blended learning (combining e-learning with traditional learning methods) with traditional learning alone on nursing students' scores. A two-group post-test experimental study was administered from February 2014 to February 2015. Two groups of nursing students who were taking the fundamentals of nursing course in Iran were compared. Sixty nursing students were selected as control group (just traditional learning methods) and experimental group (combining e-learning with traditional learning methods) for two consecutive semesters. Both groups participated in Objective Structured Clinical Examination (OSCE) and were evaluated in the same way using a prepared checklist and questionnaire of satisfaction. Statistical analysis was conducted through SPSS software version 16. Findings of this study reflected that mean of midterm (t = 2.00, p = 0.04) and final score (t = 2.50, p = 0.01) of the intervention group (combining e-learning with traditional learning methods) were significantly higher than the control group (traditional learning methods). The satisfaction of male students in intervention group was higher than in females (t = 2.60, p = 0.01). Based on the findings, this study suggests that the use of combining traditional learning methods with e-learning methods such as applying educational website and interactive online resources for fundamentals of nursing course instruction can be an effective supplement for improving nursing students' clinical skills.

The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods

NARCIS (Netherlands)

Verpoorten, Dominique; Poumay, M; Leclercq, D

2006-01-01

Please, cite this publication as: Verpoorten, D., Poumay, M., & Leclercq, D. (2006). The 8 Learning Events Model: a Pedagogic Conceptual Tool Supporting Diversification of Learning Methods. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence

Less is more: Sampling chemical space with active learning

Science.gov (United States)

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.

Characterizing Reinforcement Learning Methods through Parameterized Learning Problems

Science.gov (United States)

2011-06-03

extraneous. The agent could potentially adapt these representational aspects by applying methods from feature selection ( Kolter and Ng, 2009; Petrik et al...611–616. AAAI Press. Kolter , J. Z. and Ng, A. Y. (2009). Regularization and feature selection in least-squares temporal difference learning. In A. P

Suppression of phase separation in $(AlAs)_{2ML} (InAs)_{2ML}$ superlattices using $Al_{0.48}In_{0.52}$ As monolayer insertions

CERN Document Server

Lee, S R; Follstaedt, D M

2001-01-01

Al/sub 0.48/In/sub 0.52/As monolayers (ML) are inserted at the binary-compound interfaces of (AlAs)/sub 2/ /sub ML/(InAs)/sub 2/ /sub ML/ short-period superlattices (SPSs) during growth on (001) In P. The insertion of Al/sub 0.48/In/sub 0.52/As interlayers greater than 2 ML thick tends to suppress the phase separation that normally occurs during molecular beam epitaxy of the SPS. The degree of suppression is a sensitive function of both the monolayer-scale thickness, and the intraperiod growth sequence, of the interlayers in the SPS. Given this sensitivity to monolayer-scale variations in the surface-region composition, we propose that cyclical phase transition of the reconstructed surface initiates SPS decomposition. (21 refs).

Non-Gaussian Methods for Causal Structure Learning.

Science.gov (United States)

Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

SED-ML web tools: generate, modify and export standard-compliant simulation studies.

Science.gov (United States)

Bergmann, Frank T; Nickerson, David; Waltemath, Dagmar; Scharm, Martin

2017-04-15

The Simulation Experiment Description Markup Language (SED-ML) is a standardized format for exchanging simulation studies independently of software tools. We present the SED-ML Web Tools, an online application for creating, editing, simulating and validating SED-ML documents. The Web Tools implement all current SED-ML specifications and, thus, support complex modifications and co-simulation of models in SBML and CellML formats. Ultimately, the Web Tools lower the bar on working with SED-ML documents and help users create valid simulation descriptions. http://sysbioapps.dyndns.org/SED-ML_Web_Tools/ . fbergman@caltech.edu . © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

SysML for systems engineering a model-based approach

CERN Document Server

Holt, Jon

2013-01-01

This new edition of this popular text has been fully updated to reflect SysML 1.3, the latest version of the standard, and the discussion has been extended to show the power of SysML as a tool for systems engineering in an MBSE context.

Deep kernel learning method for SAR image target recognition

Science.gov (United States)

Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

2017-10-01

With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

QuakeML: Status of the XML-based Seismological Data Exchange Format

Science.gov (United States)

Euchner, Fabian; Schorlemmer, Danijel; Kästli, Philipp; Quakeml Working Group

2010-05-01

QuakeML is an XML-based data exchange standard for seismology that is in its fourth year of active community-driven development. The current release (version 1.2) is based on a public Request for Comments process that included contributions from ETH, GFZ, USC, SCEC, USGS, IRIS DMC, EMSC, ORFEUS, GNS, ZAMG, BRGM, Nanometrics, and ISTI. QuakeML has mainly been funded through the EC FP6 infrastructure project NERIES, in which it was endorsed as the preferred data exchange format. Currently, QuakeML services are being installed at several institutions around the globe, including EMSC, ORFEUS, ETH, Geoazur (Europe), NEIC, ANSS, SCEC/SCSN (USA), and GNS Science (New Zealand). Some of these institutions already provide QuakeML earthquake catalog web services. Several implementations of the QuakeML data model have been made. QuakePy, an open-source Python-based seismicity analysis toolkit using the QuakeML data model, is being developed at ETH. QuakePy is part of the software stack used in the Collaboratory for the Study of Earthquake Predictability (CSEP) testing center installations, developed by SCEC. Furthermore, the QuakeML data model is part of the SeisComP3 package from GFZ Potsdam. QuakeML is designed as an umbrella schema under which several sub-packages are collected. The present scope of QuakeML 1.2 covers a basic description of seismic events including picks, arrivals, amplitudes, magnitudes, origins, focal mechanisms, and moment tensors. Work on additional packages (macroseismic information, seismic inventory, and resource metadata) has been started, but is at an early stage. Contributions from the community that help to widen the thematic coverage of QuakeML are highly welcome. Online resources: http://www.quakeml.org, http://www.quakepy.org

WaterML: an XML Language for Communicating Water Observations Data

Science.gov (United States)

Maidment, D. R.; Zaslavsky, I.; Valentine, D.

2007-12-01

One of the great impediments to the synthesis of water information is the plethora of formats used to publish such data. Each water agency uses its own approach. XML (eXtended Markup Languages) are generalizations of Hypertext Markup Language to communicate specific kinds of information via the internet. WaterML is an XML language for water observations data - streamflow, water quality, groundwater levels, climate, precipitation and aquatic biology data, recorded at fixed, point locations as a function of time. The Hydrologic Information System project of the Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) has defined WaterML and prepared a set of web service functions called WaterOneFLow that use WaterML to provide information about observation sites, the variables measured there and the values of those measurments. WaterML has been submitted to the Open GIS Consortium for harmonization with its standards for XML languages. Academic investigators at a number of testbed locations in the WATERS network are providing data in WaterML format using WaterOneFlow web services. The USGS and other federal agencies are also working with CUAHSI to similarly provide access to their data in WaterML through WaterOneFlow services.

Machine learning of parameters for accurate semiempirical quantum chemical calculations

International Nuclear Information System (INIS)

Dral, Pavlo O.; Lilienfeld, O. Anatole von; Thiel, Walter

2015-01-01

We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempirical OM2 method using a set of 6095 constitutional isomers C 7 H 10 O 2 , for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules

Desempenho de idosos brasileiros no teste de deglutição de 100 ml de água Performance of Brazilian elderly on the 100 ml water swallowing test

Directory of Open Access Journals (Sweden)

Graziela Maria Martins Moreira

2012-03-01

Full Text Available OBJETIVO: Comparar o desempenho de idosos brasileiros, residentes em uma instituição de longa permanência, no teste de deglutição de 100 ml de água com os resultados obtidos em idosos ingleses. MÉTODOS: Dezoito idosos residentes numa instituição de longa permanência, considerados normais para a função de deglutição (13 mulheres e cinco homens, com idade média de 83,46 anos foram solicitados a beber 100 ml de água de um copo plástico, reproduzindo o estudo inglês. O avaliador observou lateralmente o número de goles, tempo gasto e intercorrências, gerando três índices: volume por deglutição (ml, tempo por deglutição (s e capacidade de deglutição (ml/s. RESULTADOS: A capacidade de deglutição para homens foi menor do que a das mulheres, divergindo do estudo original. O tempo médio de cada deglutição e o volume médio por deglutição foi semelhante para ambos os gêneros. CONCLUSÃO: A capacidade de deglutição em idosos é inferior à de adultos normais, indicando lentificação da deglutição. A diferença entre gêneros encontrada no estudo original não foi reproduzida, entretanto nossa amostra foi mais idosa.PURPOSE: To compare the performance of Brazilian elderly patients living in a long-term care facility on the 100 ml water swallowing test with the results obtained with British elderly. METHODS: Eighteen elderly subjects (13 women and five men, mean age 83.46, residents in a long-term care facility and considered normal regarding the swallowing function, were selected to take part in this study. As in a British study, they were laterally observed by the examiner while swallowing 100 ml of water from a plastic cup. The examiner observed the number of sips, the time taken, and complications during the test, which generated the following indices: volume per swallow (ml, time per swallow (s, and swallowing capacity (ml/s. RESULTS: The elderly men had lower swallowing capacity than the women in the research

QuakeML: Recent Development and First Applications of the Community-Created Seismological Data Exchange Standard

Science.gov (United States)

Euchner, F.; Schorlemmer, D.; Kästli, P.; Quakeml Group, T

2008-12-01

QuakeML is an XML-based exchange format for seismological data which is being developed using a community-driven approach. It covers basic event description, including picks, arrivals, amplitudes, magnitudes, origins, focal mechanisms, and moment tensors. Contributions have been made from ETH, GFZ, USC, SCEC, USGS, IRIS DMC, EMSC, ORFEUS, GNS, ZAMG, BRGM, and ISTI. The current release (Version 1.1, Proposed Recommendation) reflects the results of a public Request for Comments process which has been documented online at http://quakeml.org/RFC_BED_1.0. QuakeML has recently been adopted as a distribution format for earthquake catalogs by GNS Science, New Zealand, and the European-Mediterranean Seismological Centre (EMSC). These institutions provide prototype QuakeML web services. Furthermore, integration of the QuakeML data model in the CSEP (Collaboratory for the Study of Earthquake Predictability, http://www.cseptesting.org) testing center software developed by SCEC is under way. QuakePy is a Python- based seismicity analysis toolkit which is based on the QuakeML data model. Recently, QuakePy has been used to implement the PMC method for calculating network recording completeness (Schorlemmer and Woessner 2008, in press). Completeness results for seismic networks in Southern California and Japan can be retrieved through the CompletenessWeb (http://completenessweb.org). Future QuakeML development will include an extension for macroseismic information. Furthermore, development on seismic inventory information, resource identifiers, and resource metadata is under way. Online resources: http://www.quakeml.org, http://www.quakepy.org

Choosing Learning Methods Suitable for Teaching and Learning in Computer Science

Science.gov (United States)

Taylor, Estelle; Breed, Marnus; Hauman, Ilette; Homann, Armando

2013-01-01

Our aim is to determine which teaching methods students in Computer Science and Information Systems prefer. There are in total 5 different paradigms (behaviorism, cognitivism, constructivism, design-based and humanism) with 32 models between them. Each model is unique and states different learning methods. Recommendations are made on methods that…

HepML, an XML-based format for describing simulated data in high energy physics

Science.gov (United States)

Belov, S.; Dudko, L.; Kekelidze, D.; Sherstnev, A.

2010-10-01

energy physics is divided into several stages. Various programs exist for these stages. In this article we are interested in interfacing different Monte Carlo event generators via data files, in particular, Matrix Element (ME) generators and Showering and Hadronization (SH) generators. There is a widely accepted format for data files for such interfaces - Les Houches Event Format (LHEF). Although information kept in an LHEF file is enough for proper working of SH generators, it is insufficient for understanding how events in the LHEF file have been prepared and which physical model has been applied. In this paper we propose an extension of the format for keeping additional information available in generators. We propose to add a new information block, marked up with XML tags, to the LHEF file. This block describes events in the file in more detail. In particular, it stores information about a physical model, kinematical cuts, generator, etc. This helps to make LHEF files self-documented. Certainly, HepML can be applied in more general context, not in LHEF files only. Solution method: In order to overcome drawbacks of the original LHEF accord we propose to add a new information block of HepML tags. HepML is an XML-based markup language. We designed several XML Schemas for all tags in the language. Any HepML document should follow rules of the Schemas. The language is equipped with a library for operation with HepML tags and documents. This C++ library, called libhepml, consists of classes for HepML objects, which represent a HepML document in computer memory, parsing classes, serializating classes, and some auxiliary classes. Restrictions: The software is adapted for solving problems, described in the article. There are no additional restrictions. Running time: Tests have been done on a computer with Intel(R) Core(TM)2 Solo, 1.4 GHz. Parsing of a HepML file: 6 ms (size of the HepML files is 12.5 Kb) Writing of a HepML block to file: 14 ms (file size 12.5 Kb) Merging of

An online supervised learning method based on gradient descent for spiking neurons.

Science.gov (United States)

Xu, Yan; Yang, Jing; Zhong, Shuiming

2017-09-01

The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by precise firing times of spikes. The gradient-descent-based (GDB) learning methods are widely used and verified in the current research. Although the existing GDB multi-spike learning (or spike sequence learning) methods have good performance, they work in an offline manner and still have some limitations. This paper proposes an online GDB spike sequence learning method for spiking neurons that is based on the online adjustment mechanism of real biological neuron synapses. The method constructs error function and calculates the adjustment of synaptic weights as soon as the neurons emit a spike during their running process. We analyze and synthesize desired and actual output spikes to select appropriate input spikes in the calculation of weight adjustment in this paper. The experimental results show that our method obviously improves learning performance compared with the offline learning manner and has certain advantage on learning accuracy compared with other learning methods. Stronger learning ability determines that the method has large pattern storage capacity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

Directory of Open Access Journals (Sweden)

Muneki Yasuda

2018-04-01

Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

Preparing for the future: opportunities for ML in ATLAS & CMS

CERN Multimedia

CERN. Geneva

2015-01-01

ML is an established tool in HEP and there are many examples which demonstrate its importance for the kind of classification and regression problem we have in our field. However, there is also a big potential for future applications in yet untapped areas. I will summarise these opportunities and highlight recent, ongoing and planned studies of novel ML applications in HEP. Certain aspects of the problems we are faced with in HEP are quite unique and represent interesting benchmark problems for the ML community as a whole. Hence, efficient communication and close interaction between the ML and HEP community is expected to lead to promising cross-fertilisation. This talk attempts to serve as a starting point for such a prospective collaboration.

Behavioral and functional neuroanatomical correlates of anterograde autobiographical memory in isolated retrograde amnesic patient M.L.

Science.gov (United States)

Levine, Brian; Svoboda, Eva; Turner, Gary R; Mandic, Marina; Mackey, Allison

2009-09-01

Patient M.L. [Levine, B., Black, S. E., Cabeza, R., Sinden, M., Mcintosh, A. R., Toth, J. P., et al. (1998). Episodic memory and the self in a case of isolated retrograde amnesia. Brain, 121, 1951-1973], lost memory for events occurring before his severe traumatic brain injury, yet his anterograde (post-injury) learning and memory appeared intact, a syndrome known as isolated or focal retrograde amnesia. Studies with M.L. demonstrated a dissociation between episodic and semantic memory. His retrograde amnesia was specific to episodic autobiographical memory. Convergent behavioral and functional imaging data suggested that his anterograde memory, while appearing normal, was accomplished with reduced autonoetic awareness (awareness of the self as a continuous entity across time that is a crucial element of episodic memory). While previous research on M.L. focused on anterograde memory of laboratory stimuli, in this study, M.L.'s autobiographical memory for post-injury events or anterograde autobiographical memory was examined using prospective collection of autobiographical events via audio diary with detailed behavioral and functional neuroanatomical analysis. Consistent with his reports of subjective disconnection from post-injury autobiographical events, M.L. assigned fewer "remember" ratings to his autobiographical events than comparison subjects. His generation of event-specific details using the Autobiographical Interview [Levine, B., Svoboda, E., Hay, J., Winocur, G., & Moscovitch, M. (2002). Aging and autobiographical memory: dissociating episodic from semantic retrieval. Psychology and Aging, 17, 677-689] was low, but not significantly so, suggesting that it is possible to generate episodic-like details even when re-experiencing of those details is compromised. While listening to the autobiographical audio diary segments, M.L. showed reduced activation relative to comparison subjects in midline frontal and posterior nodes previously identified as part of the

Studying depression using imaging and machine learning methods.

Science.gov (United States)

Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

2016-01-01

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

A MuDDy Experience-ML Bindings to a BDD Library

DEFF Research Database (Denmark)

Larsen, Ken Friis

2009-01-01

. This combination of an ML interface to a high-performance C library is surprisingly fruitful. ML allows you to quickly experiment with high-level symbolic algorithms before handing over the grunt work to the C library. I show how, with a relatively little effort, you can make a domain specific language...... for concurrent finite state-machines embedded in Standard ML and then write various custom model-checking algorithms for this domain specific embedded language (DSEL)....

Characterizing Engineering Learners' Preferences for Active and Passive Learning Methods

Science.gov (United States)

Magana, Alejandra J.; Vieira, Camilo; Boutin, Mireille

2018-01-01

This paper studies electrical engineering learners' preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called "slectures," and some others. The results suggest that…

Microsoft Azure machine learning

CERN Document Server

Mund, Sumit

2015-01-01

The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.

Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

Science.gov (United States)

Gong, Xiajing; Hu, Meng; Zhao, Liang

2018-05-01

Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Approximation methods for efficient learning of Bayesian networks

CERN Document Server

Riggelsen, C

2008-01-01

This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

Unsupervised process monitoring and fault diagnosis with machine learning methods

CERN Document Server

Aldrich, Chris

2013-01-01

This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

Path to Stochastic Stability: Comparative Analysis of Stochastic Learning Dynamics in Games

KAUST Repository

Jaleel, Hassan; Shamma, Jeff S.

2018-01-01

dynamics: Log-Linear Learning (LLL) and Metropolis Learning (ML). Although both of these dynamics have the same stochastically stable states, LLL and ML correspond to different behavioral models for decision making. Moreover, we demonstrate through

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Science.gov (United States)

Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

2017-12-26

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

Science.gov (United States)

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

Machine Learning Techniques for Modelling Short Term Land-Use Change

Directory of Open Access Journals (Sweden)

Mileva Samardžić-Petrović

2017-11-01

Full Text Available The representation of land use change (LUC is often achieved by using data-driven methods that include machine learning (ML techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT, Neural Networks (NN, and Support Vector Machines (SVM for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.

A Preliminary Survey of the Preferred Learning Methods for Interpretation Students

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Heinz, Michael

2013-01-01

There are many different methods that individuals use to learn languages like reading books or writing essays. Not all methods are equally successful for second language learners but nor do all successful learners of a second language show identical preferences for learning methods. Additionally, at the highest level of language learning various…

Promises of Machine Learning Approaches in Prediction of Absorption of Compounds.

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Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2018-01-01

The Machine Learning (ML) is one of the fastest developing techniques in the prediction and evaluation of important pharmacokinetic properties such as absorption, distribution, metabolism and excretion. The availability of a large number of robust validation techniques for prediction models devoted to pharmacokinetics has significantly enhanced the trust and authenticity in ML approaches. There is a series of prediction models generated and used for rapid screening of compounds on the basis of absorption in last one decade. Prediction of absorption of compounds using ML models has great potential across the pharmaceutical industry as a non-animal alternative to predict absorption. However, these prediction models still have to go far ahead to develop the confidence similar to conventional experimental methods for estimation of drug absorption. Some of the general concerns are selection of appropriate ML methods and validation techniques in addition to selecting relevant descriptors and authentic data sets for the generation of prediction models. The current review explores published models of ML for the prediction of absorption using physicochemical properties as descriptors and their important conclusions. In addition, some critical challenges in acceptance of ML models for absorption are also discussed. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Studying depression using imaging and machine learning methods

Directory of Open Access Journals (Sweden)

Meenal J. Patel

2016-01-01

Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

"Mastery Learning" Como Metodo Psicoeducativo para Ninos con Problemas Especificos de Aprendizaje. ("Mastery Learning" as a Psychoeducational Method for Children with Specific Learning Problems.)

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Coya, Liliam de Barbosa; Perez-Coffie, Jorge

1982-01-01

"Mastery Learning" was compared with the "conventional" method of teaching reading skills to Puerto Rican children with specific learning disabilities. The "Mastery Learning" group showed significant gains in the cognitive and affective domains. Results suggested Mastery Learning is a more effective method of teaching…

Early identification of posttraumatic stress following military deployment: Application of machine learning methods to a prospective study of Danish soldiers.

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Karstoft, Karen-Inge; Statnikov, Alexander; Andersen, Søren B; Madsen, Trine; Galatzer-Levy, Isaac R

2015-09-15

Pre-deployment identification of soldiers at risk for long-term posttraumatic stress psychopathology after home coming is important to guide decisions about deployment. Early post-deployment identification can direct early interventions to those in need and thereby prevents the development of chronic psychopathology. Both hold significant public health benefits given large numbers of deployed soldiers, but has so far not been achieved. Here, we aim to assess the potential for pre- and early post-deployment prediction of resilience or posttraumatic stress development in soldiers by application of machine learning (ML) methods. ML feature selection and prediction algorithms were applied to a prospective cohort of 561 Danish soldiers deployed to Afghanistan in 2009 to identify unique risk indicators and forecast long-term posttraumatic stress responses. Robust pre- and early postdeployment risk indicators were identified, and included individual PTSD symptoms as well as total level of PTSD symptoms, previous trauma and treatment, negative emotions, and thought suppression. The predictive performance of these risk indicators combined was assessed by cross-validation. Together, these indicators forecasted long term posttraumatic stress responses with high accuracy (pre-deployment: AUC = 0.84 (95% CI = 0.81-0.87), post-deployment: AUC = 0.88 (95% CI = 0.85-0.91)). This study utilized a previously collected data set and was therefore not designed to exhaust the potential of ML methods. Further, the study relied solely on self-reported measures. Pre-deployment and early post-deployment identification of risk for long-term posttraumatic psychopathology are feasible and could greatly reduce the public health costs of war. Copyright © 2015 Elsevier B.V. All rights reserved.

SMALL GROUP LEARNING METHODS AND THEIR EFFECT ON LEARNERS’ RELATIONSHIPS

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Radka Borůvková

2016-04-01

Full Text Available Building relationships in the classroom is an essential part of any teacher's career. Having healthy teacher-to-learner and learner-to-learner relationships is an effective way to help prevent pedagogical failure, social conflict and quarrelsome behavior. Many strategies are available that can be used to achieve good long-lasting relationships in the classroom setting. Successful teachers’ pedagogical work in the classroom requires detailed knowledge of learners’ relationships. Good understanding of the relationships is necessary, especially in the case of teenagers’ class. This sensitive period of adolescence demands attention of all teachers who should deal with the problems of their learners. Special care should be focused on children that are out of their classmates’ interest (so called isolated learners or isolates in such class and on possibilities to integrate them into the class. Natural idea how to do it is that of using some modern non-traditional teaching/learning methods, especially the methods based on work in small groups involving learners’ cooperation. Small group education (especially problem-based learning, project-based learning, cooperative learning, collaborative learning or inquire-based learning as one of these methods involves a high degree of interaction. The effectiveness of learning groups is determined by the extent to which the interaction enables members to clarify their own understanding, build upon each other's contributions, sift out meanings, ask and answer questions. An influence of this kind of methods (especially cooperative learning (CL on learners’ relationships was a subject of the further described research. Within the small group education, students work with their classmates to solve complex and authentic problems that help develop content knowledge as well as problem-solving, reasoning, communication, and self-assessment skills. The aim of the research was to answer the question: Can the

CytometryML: a markup language for analytical cytology

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Leif, Robert C.; Leif, Stephanie H.; Leif, Suzanne B.

2003-06-01

Cytometry Markup Language, CytometryML, is a proposed new analytical cytology data standard. CytometryML is a set of XML schemas for encoding both flow cytometry and digital microscopy text based data types. CytometryML schemas reference both DICOM (Digital Imaging and Communications in Medicine) codes and FCS keywords. These schemas provide representations for the keywords in FCS 3.0 and will soon include DICOM microscopic image data. Flow Cytometry Standard (FCS) list-mode has been mapped to the DICOM Waveform Information Object. A preliminary version of a list mode binary data type, which does not presently exist in DICOM, has been designed. This binary type is required to enhance the storage and transmission of flow cytometry and digital microscopy data. Index files based on Waveform indices will be used to rapidly locate the cells present in individual subsets. DICOM has the advantage of employing standard file types, TIF and JPEG, for Digital Microscopy. Using an XML schema based representation means that standard commercial software packages such as Excel and MathCad can be used to analyze, display, and store analytical cytometry data. Furthermore, by providing one standard for both DICOM data and analytical cytology data, it eliminates the need to create and maintain special purpose interfaces for analytical cytology data thereby integrating the data into the larger DICOM and other clinical communities. A draft version of CytometryML is available at www.newportinstruments.com.

VarioML framework for comprehensive variation data representation and exchange.

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Byrne, Myles; Fokkema, Ivo Fac; Lancaster, Owen; Adamusiak, Tomasz; Ahonen-Bishopp, Anni; Atlan, David; Béroud, Christophe; Cornell, Michael; Dalgleish, Raymond; Devereau, Andrew; Patrinos, George P; Swertz, Morris A; Taschner, Peter Em; Thorisson, Gudmundur A; Vihinen, Mauno; Brookes, Anthony J; Muilu, Juha

2012-10-03

Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.

Reactive Programming in Standard ML

OpenAIRE

Pucella, Riccardo

2004-01-01

Reactive systems are systems that maintain an ongoing interaction with their environment, activated by receiving input events from the environment and producing output events in response. Modern programming languages designed to program such systems use a paradigm based on the notions of instants and activations. We describe a library for Standard ML that provides basic primitives for programming reactive systems. The library is a low-level system upon which more sophisticated reactive behavi...

Experts in Teams – An experiential learning method

DEFF Research Database (Denmark)

Johansen, Steffen Kjær

2017-01-01

T becomes a learning method rather than a teaching method. Besides discussing the pedagogical characteristics of EiT, the study also gives a general introduction to EiT as it was taught at SDU fall 2016 as well as a brief review of the basic theory behind experiential learning. As such this study serves...... courses. Most of the practical courses are group work along the lines of project based learning. EiT is in a way both. It is a practical course in as much as our students get hands-on experience with interdisciplinary team work and innovation processes. EiT is a theoretical course in as much as our...... both as an introduction to e.g. new teachers of EiT but also as a starting point for a clarification of the features that makes EiT an experiential learning endeavor....

Developing a Blended Learning-Based Method for Problem-Solving in Capability Learning

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Dwiyogo, Wasis D.

2018-01-01

The main objectives of the study were to develop and investigate the implementation of blended learning based method for problem-solving. Three experts were involved in the study and all three had stated that the model was ready to be applied in the classroom. The implementation of the blended learning-based design for problem-solving was…

Active Learning Methods

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Zayapragassarazan, Z.; Kumar, Santosh

2012-01-01

Present generation students are primarily active learners with varied learning experiences and lecture courses may not suit all their learning needs. Effective learning involves providing students with a sense of progress and control over their own learning. This requires creating a situation where learners have a chance to try out or test their…

Learning Method and Its Influence on Nutrition Study Results Throwing the Ball

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Samsudin; Nugraha, Bayu

2015-01-01

This study aimed to know the difference between playing and learning methods of exploratory learning methods to learning outcomes throwing the ball. In addition, this study also aimed to determine the effect of nutritional status of these two learning methods mentioned above. This research was conducted at SDN Cipinang Besar Selatan 16 Pagi East…

QuakeML: XML for Seismological Data Exchange and Resource Metadata Description

Science.gov (United States)

Euchner, F.; Schorlemmer, D.; Becker, J.; Heinloo, A.; Kästli, P.; Saul, J.; Weber, B.; QuakeML Working Group

2007-12-01

QuakeML is an XML-based data exchange format for seismology that is under development. Current collaborators are from ETH, GFZ, USC, USGS, IRIS DMC, EMSC, ORFEUS, and ISTI. QuakeML development was motivated by the lack of a widely accepted and well-documented data format that is applicable to a broad range of fields in seismology. The development team brings together expertise from communities dealing with analysis and creation of earthquake catalogs, distribution of seismic bulletins, and real-time processing of seismic data. Efforts to merge QuakeML with existing XML dialects are under way. The first release of QuakeML will cover a basic description of seismic events including picks, arrivals, amplitudes, magnitudes, origins, focal mechanisms, and moment tensors. Further extensions are in progress or planned, e.g., for macroseismic information, location probability density functions, slip distributions, and ground motion information. The QuakeML language definition is supplemented by a concept to provide resource metadata and facilitate metadata exchange between distributed data providers. For that purpose, we introduce unique, location-independent identifiers of seismological resources. As an application of QuakeML, ETH Zurich currently develops a Python-based seismicity analysis toolkit as a contribution to CSEP (Collaboratory for the Study of Earthquake Predictability). We follow a collaborative and transparent development approach along the lines of the procedures of the World Wide Web Consortium (W3C). QuakeML currently is in working draft status. The standard description will be subjected to a public Request for Comments (RFC) process and eventually reach the status of a recommendation. QuakeML can be found at http://www.quakeml.org.

Assembling Components using SysML with Non-Functional Requirements

OpenAIRE

Chouali , Samir; Hammad , Ahmed; Mountassir , Hassan

2013-01-01

International audience; Non-functional requirements of component based systems are important as their functional requirements, therefore they must be considered in components assembly. These properties are beforehand specified with SysML requirement diagram. We specify component based system architecture with SysML block definition diagram, and component behaviors with sequence diagrams. We propose to specify formally component interfaces with interface automata, obtained from requirement and...

Design of dialogic eLearning-to-learn: metalearning as pedagogical methodology

DEFF Research Database (Denmark)

Sorensen, Elsebeth Korsgaard

2008-01-01

This paper presents a perspective emphasising Meta learning (ML) as the most significant and pertinent feature for promoting a democratic, collaborative eLearning-to-Learn (eL2L) phenomenon in a global context. Through attempting to understand and clarify the powers of pedagogical design of global...... networked e Learning based on Learning-to-Learn (L2L), it makes a plea for L2L in a dialogic global learning context, offering a vision of global democratic citizens able to engage in critical dialogue with fellow learners. http://www.inderscience.com/search/index.php?action=record&rec_id=17675&prev...

The Keyimage Method of Learning Sound-Symbol Correspondences: A Case Study of Learning Written Khmer

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Elizabeth Lavolette

2009-01-01

Full Text Available I documented my strategies for learning sound-symbol correspondences during a Khmer course. I used a mnemonic strategy that I call the keyimage method. In this method, a character evokes an image (the keyimage, which evokes the corresponding sound. For example, the keyimage for the character 2 could be a swan with its head tucked in. This evokes the sound "kaw" that a swan makes, which sounds similar to the Khmer sound corresponding to 2. The method has some similarities to the keyword method. Considering the results of keyword studies, I hypothesize that the keyimage method is more effective than rote learning and that peer-generated keyimages are more effective than researcher- or teacher-generated keyimages, which are more effective than learner-generated ones. In Dr. Andrew Cohen's plenary presentation at the Hawaii TESOL 2007 conference, he mentioned that more case studies are needed on learning strategies (LSs. One reason to study LSs is that what learners do with input to produce output is unclear, and knowing what strategies learners use may help us understand that process (Dornyei, 2005, p. 170. Hopefully, we can use that knowledge to improve language learning, perhaps by teaching learners to use the strategies that we find. With that in mind, I have examined the LSs that I used in studying Khmer as a foreign language, focusing on learning the syllabic alphabet.

VarioML framework for comprehensive variation data representation and exchange

Directory of Open Access Journals (Sweden)

Byrne Myles

2012-10-01

Full Text Available Abstract Background Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. Results The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDBs e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. Conclusions VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.

Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database.

Science.gov (United States)

Chen-Ying Hung; Wei-Chen Chen; Po-Tsun Lai; Ching-Heng Lin; Chi-Chun Lee

2017-07-01

Electronic medical claims (EMCs) can be used to accurately predict the occurrence of a variety of diseases, which can contribute to precise medical interventions. While there is a growing interest in the application of machine learning (ML) techniques to address clinical problems, the use of deep-learning in healthcare have just gained attention recently. Deep learning, such as deep neural network (DNN), has achieved impressive results in the areas of speech recognition, computer vision, and natural language processing in recent years. However, deep learning is often difficult to comprehend due to the complexities in its framework. Furthermore, this method has not yet been demonstrated to achieve a better performance comparing to other conventional ML algorithms in disease prediction tasks using EMCs. In this study, we utilize a large population-based EMC database of around 800,000 patients to compare DNN with three other ML approaches for predicting 5-year stroke occurrence. The result shows that DNN and gradient boosting decision tree (GBDT) can result in similarly high prediction accuracies that are better compared to logistic regression (LR) and support vector machine (SVM) approaches. Meanwhile, DNN achieves optimal results by using lesser amounts of patient data when comparing to GBDT method.

WeedML: a Tool for Collaborative Weed Demographic Modeling

OpenAIRE

Holst, Niels

2010-01-01

WeedML is a proposed standard to formulate models of weed demography, or maybe even complex models in general, that are both transparent and straightforward to re-use as building blocks for new models. The paper describes the design and thoughts behind WeedML which relies on XML and object-oriented systems development. Proof-of-concept software is provided as open-source C++ code and executables that can be downloaded freely.

Towards a Quality Assessment Method for Learning Preference Profiles in Negotiation

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Hindriks, Koen V.; Tykhonov, Dmytro

In automated negotiation, information gained about an opponent's preference profile by means of learning techniques may significantly improve an agent's negotiation performance. It therefore is useful to gain a better understanding of how various negotiation factors influence the quality of learning. The quality of learning techniques in negotiation are typically assessed indirectly by means of comparing the utility levels of agreed outcomes and other more global negotiation parameters. An evaluation of learning based on such general criteria, however, does not provide any insight into the influence of various aspects of negotiation on the quality of the learned model itself. The quality may depend on such aspects as the domain of negotiation, the structure of the preference profiles, the negotiation strategies used by the parties, and others. To gain a better understanding of the performance of proposed learning techniques in the context of negotiation and to be able to assess the potential to improve the performance of such techniques a more systematic assessment method is needed. In this paper we propose such a systematic method to analyse the quality of the information gained about opponent preferences by learning in single-instance negotiations. The method includes measures to assess the quality of a learned preference profile and proposes an experimental setup to analyse the influence of various negotiation aspects on the quality of learning. We apply the method to a Bayesian learning approach for learning an opponent's preference profile and discuss our findings.

Preparing Students for Flipped or Team-Based Learning Methods

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Balan, Peter; Clark, Michele; Restall, Gregory

2015-01-01

Purpose: Teaching methods such as Flipped Learning and Team-Based Learning require students to pre-learn course materials before a teaching session, because classroom exercises rely on students using self-gained knowledge. This is the reverse to "traditional" teaching when course materials are presented during a lecture, and students are…

Different protein-protein interface patterns predicted by different machine learning methods.

Science.gov (United States)

Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

2017-11-22

Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

A Review on Different Virtual Learning Methods in Pharmacy Education

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Amin Noori

2015-10-01

Full Text Available Virtual learning is a type of electronic learning system based on the web. It models traditional in- person learning by providing virtual access to classes, tests, homework, feedbacks and etc. Students and teachers can interact through chat rooms or other virtual environments. Web 2.0 services are usually used for this method. Internet audio-visual tools, multimedia systems, a disco CD-ROMs, videotapes, animation, video conferencing, and interactive phones can all be used to deliver data to the students. E-learning can occur in or out of the classroom. It is time saving with lower costs compared to traditional methods. It can be self-paced, it is suitable for distance learning and it is flexible. It is a great learning style for continuing education and students can independently solve their problems but it has its disadvantages too. Thereby, blended learning (combination of conventional and virtual education is being used worldwide and has improved knowledge, skills and confidence of pharmacy students.The aim of this study is to review, discuss and introduce different methods of virtual learning for pharmacy students.Google scholar, Pubmed and Scupus databases were searched for topics related to virtual, electronic and blended learning and different styles like computer simulators, virtual practice environment technology, virtual mentor, virtual patient, 3D simulators, etc. are discussed in this article.Our review on different studies on these areas shows that the students are highly satisfied withvirtual and blended types of learning.

A deep learning-based multi-model ensemble method for cancer prediction.

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Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong

2018-01-01

Cancer is a complex worldwide health problem associated with high mortality. With the rapid development of the high-throughput sequencing technology and the application of various machine learning methods that have emerged in recent years, progress in cancer prediction has been increasingly made based on gene expression, providing insight into effective and accurate treatment decision making. Thus, developing machine learning methods, which can successfully distinguish cancer patients from healthy persons, is of great current interest. However, among the classification methods applied to cancer prediction so far, no one method outperforms all the others. In this paper, we demonstrate a new strategy, which applies deep learning to an ensemble approach that incorporates multiple different machine learning models. We supply informative gene data selected by differential gene expression analysis to five different classification models. Then, a deep learning method is employed to ensemble the outputs of the five classifiers. The proposed deep learning-based multi-model ensemble method was tested on three public RNA-seq data sets of three kinds of cancers, Lung Adenocarcinoma, Stomach Adenocarcinoma and Breast Invasive Carcinoma. The test results indicate that it increases the prediction accuracy of cancer for all the tested RNA-seq data sets as compared to using a single classifier or the majority voting algorithm. By taking full advantage of different classifiers, the proposed deep learning-based multi-model ensemble method is shown to be accurate and effective for cancer prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Computer game-based and traditional learning method: a comparison regarding students' knowledge retention.

Science.gov (United States)

Rondon, Silmara; Sassi, Fernanda Chiarion; Furquim de Andrade, Claudia Regina

2013-02-25

Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students' prior knowledge (i.e. before undergoing the learning method), short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method) were assessed with a multiple choice questionnaire. Students' performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students' short and long-term knowledge retention.

Computer game-based and traditional learning method: a comparison regarding students’ knowledge retention

Directory of Open Access Journals (Sweden)

Rondon Silmara

2013-02-01

Full Text Available Abstract Background Educational computer games are examples of computer-assisted learning objects, representing an educational strategy of growing interest. Given the changes in the digital world over the last decades, students of the current generation expect technology to be used in advancing their learning requiring a need to change traditional passive learning methodologies to an active multisensory experimental learning methodology. The objective of this study was to compare a computer game-based learning method with a traditional learning method, regarding learning gains and knowledge retention, as means of teaching head and neck Anatomy and Physiology to Speech-Language and Hearing pathology undergraduate students. Methods Students were randomized to participate to one of the learning methods and the data analyst was blinded to which method of learning the students had received. Students’ prior knowledge (i.e. before undergoing the learning method, short-term knowledge retention and long-term knowledge retention (i.e. six months after undergoing the learning method were assessed with a multiple choice questionnaire. Students’ performance was compared considering the three moments of assessment for both for the mean total score and for separated mean scores for Anatomy questions and for Physiology questions. Results Students that received the game-based method performed better in the pos-test assessment only when considering the Anatomy questions section. Students that received the traditional lecture performed better in both post-test and long-term post-test when considering the Anatomy and Physiology questions. Conclusions The game-based learning method is comparable to the traditional learning method in general and in short-term gains, while the traditional lecture still seems to be more effective to improve students’ short and long-term knowledge retention.

Two Undergraduate Process Modeling Courses Taught Using Inductive Learning Methods

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Soroush, Masoud; Weinberger, Charles B.

2010-01-01

This manuscript presents a successful application of inductive learning in process modeling. It describes two process modeling courses that use inductive learning methods such as inquiry learning and problem-based learning, among others. The courses include a novel collection of multi-disciplinary complementary process modeling examples. They were…

An overview of the CellML API and its implementation

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Halstead Matt

2010-04-01

Full Text Available Abstract Background CellML is an XML based language for representing mathematical models, in a machine-independent form which is suitable for their exchange between different authors, and for archival in a model repository. Allowing for the exchange and archival of models in a computer readable form is a key strategic goal in bioinformatics, because of the associated improvements in scientific record accuracy, the faster iterative process of scientific development, and the ability to combine models into large integrative models. However, for CellML models to be useful, tools which can process them correctly are needed. Due to some of the more complex features present in CellML models, such as imports, developing code ab initio to correctly process models can be an onerous task. For this reason, there is a clear and pressing need for an application programming interface (API, and a good implementation of that API, upon which tools can base their support for CellML. Results We developed an API which allows the information in CellML models to be retrieved and/or modified. We also developed a series of optional extension APIs, for tasks such as simplifying the handling of connections between variables, dealing with physical units, validating models, and translating models into different procedural languages. We have also provided a Free/Open Source implementation of this application programming interface, optimised to achieve good performance. Conclusions Tools have been developed using the API which are mature enough for widespread use. The API has the potential to accelerate the development of additional tools capable of processing CellML, and ultimately lead to an increased level of sharing of mathematical model descriptions.

Quality control and stability study of 100 mg/ml paracetamol oral drops

International Nuclear Information System (INIS)

Garcia Penna, Caridad M.; Montes de Oca Porto, Yanet; Salomon Izquierdo, Suslebys

2013-01-01

Paracetamol is an effective analgesic and antipyretic drug of the non-steroidal anti-inflammatory drug group. Paracetamol oral drops are indicated for use in infant population aged up to 5 years to relieve fever, headache, toothache and symptomatic relief of common cold. To validate two analytical methods for the quality control and the stability study and to study the stability of 100 mg/ml Paracetamol oral drops made in Cuba

On Combining Elements of Different Ways of Learning, Methods and Knowledge

Directory of Open Access Journals (Sweden)

Dušana Findeisen

2013-12-01

Full Text Available The paper deals with different thinkers' attitude towards methods in adult education. It examines the value of some elements of »trial and error learning« and »non-directive learning«. Like a multifaceted approach based on elements drawn from different methods, the way we learn can also be eclectic.  To illustrate this assertion, the author analyses the »anti method« used by Maurice Pialat, a French film director, contrasting it with methods in which the aim is set in advance and the process leading towards it is organised in sequences. This is most often the case in script-based shooting of films, directing a theatre performance or running adult education. Moreover, the author argues that learning about how to do something is combined with learning about how to be. She further emphasises that methods should not be used to impose one’s knowledge and one’s reality on the learner, thus destroying circumstances necessary for gaining or creating knowledge.

Machine Learning of Fault Friction

Science.gov (United States)

Johnson, P. A.; Rouet-Leduc, B.; Hulbert, C.; Marone, C.; Guyer, R. A.

2017-12-01

We are applying machine learning (ML) techniques to continuous acoustic emission (AE) data from laboratory earthquake experiments. Our goal is to apply explicit ML methods to this acoustic datathe AE in order to infer frictional properties of a laboratory fault. The experiment is a double direct shear apparatus comprised of fault blocks surrounding fault gouge comprised of glass beads or quartz powder. Fault characteristics are recorded, including shear stress, applied load (bulk friction = shear stress/normal load) and shear velocity. The raw acoustic signal is continuously recorded. We rely on explicit decision tree approaches (Random Forest and Gradient Boosted Trees) that allow us to identify important features linked to the fault friction. A training procedure that employs both the AE and the recorded shear stress from the experiment is first conducted. Then, testing takes place on data the algorithm has never seen before, using only the continuous AE signal. We find that these methods provide rich information regarding frictional processes during slip (Rouet-Leduc et al., 2017a; Hulbert et al., 2017). In addition, similar machine learning approaches predict failure times, as well as slip magnitudes in some cases. We find that these methods work for both stick slip and slow slip experiments, for periodic slip and for aperiodic slip. We also derive a fundamental relationship between the AE and the friction describing the frictional behavior of any earthquake slip cycle in a given experiment (Rouet-Leduc et al., 2017b). Our goal is to ultimately scale these approaches to Earth geophysical data to probe fault friction. References Rouet-Leduc, B., C. Hulbert, N. Lubbers, K. Barros, C. Humphreys and P. A. Johnson, Machine learning predicts laboratory earthquakes, in review (2017). https://arxiv.org/abs/1702.05774Rouet-LeDuc, B. et al., Friction Laws Derived From the Acoustic Emissions of a Laboratory Fault by Machine Learning (2017), AGU Fall Meeting Session S025

Machine learning & artificial intelligence in the quantum domain: a review of recent progress.

Science.gov (United States)

Dunjko, Vedran; Briegel, Hans J

2018-03-05

Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and

Theoretical analysis of hydrogen chemisorption on Pd(111), Re(0001) and PdML/Re(0001), ReML/Pd(111) pseudomorphic overlayers

DEFF Research Database (Denmark)

Pallassana, Venkataraman; Neurock, Matthew; Hansen, Lars Bruno

1999-01-01

not appear to provide an independent parameter for assessing surface reactivity. The weak chemisorption of hydrogen on the Pd-ML/Re(0001) surface relates to substantial lowering of the d-band center of Pd, when it is pseudomorphically deposited as a monolayer on a Re substrate. [S0163-1829(99)00331-2].......Gradient-corrected density-functional theory (DFT-GGA) periodic slab calculations have been used to analyze the binding of atomic hydrogen on monometallic Pd(111), Re(0001), and bimetallic Pd-mL/Re(0001) [pseudomorphic monolayer of Pd(111) on Re(0001)] and Re-ML/Pd(111) surfaces. The computed...

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides

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Stanislawski Jerzy

2013-01-01

Full Text Available Abstract Background Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. Results We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%. The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile to 0.5 CPU-hours (simplified 3D profile to seconds (machine learning. Conclusions We showed that the simplified profile generation method does not introduce an error with regard to the original method, while

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

Science.gov (United States)

Stanislawski, Jerzy; Kotulska, Malgorzata; Unold, Olgierd

2013-01-17

Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset

Polymorphic New World monkeys with more than three M/L cone types

Science.gov (United States)

Jacobs, Gerald H.; Deegan, Jess F.

2005-10-01

Most New World (platyrrhine) monkeys have M/L cone photopigment polymorphisms that map directly into individual variations in visual sensitivity and color vision. We used electroretinogram flicker photometry to examine M/L cone photopigments in the New World monkey Callicebus moloch (the dusky Titi). Like other New World monkeys, this species has an M/L cone photopigment polymorphism that reflects the presence of X-chromosome opsin gene alleles. However, unlike other platyrrhines in which three M/L photopigments are typical, Callicebus has a total of five M/L cone photopigments. The peak sensitivity values for these pigments extend across the range from 530 to 562 nm. The result is an enhanced array of potential color vision phenotypes in this species.

Actively Teaching Research Methods with a Process Oriented Guided Inquiry Learning Approach

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Mullins, Mary H.

2017-01-01

Active learning approaches have shown to improve student learning outcomes and improve the experience of students in the classroom. This article compares a Process Oriented Guided Inquiry Learning style approach to a more traditional teaching method in an undergraduate research methods course. Moving from a more traditional learning environment to…

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

Directory of Open Access Journals (Sweden)

Jing Zhang

2017-12-01

Full Text Available Cerebral palsy (CP, a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

The Effect of Using Cooperative Learning Method on Tenth Grade Students' Learning Achievement and Attitude towards Biology

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Rabgay, Tshewang

2018-01-01

The study investigated the effect of using cooperative learning method on tenth grade students' learning achievement in biology and their attitude towards the subject in a Higher Secondary School in Bhutan. The study used a mixed method approach. The quantitative component included an experimental design where cooperative learning was the…

ISAC's Gating-ML 2.0 data exchange standard for gating description.

Science.gov (United States)

Spidlen, Josef; Moore, Wayne; Brinkman, Ryan R

2015-07-01

The lack of software interoperability with respect to gating has traditionally been a bottleneck preventing the use of multiple analytical tools and reproducibility of flow cytometry data analysis by independent parties. To address this issue, ISAC developed Gating-ML, a computer file format to encode and interchange gates. Gating-ML 1.5 was adopted and published as an ISAC Candidate Recommendation in 2008. Feedback during the probationary period from implementors, including major commercial software companies, instrument vendors, and the wider community, has led to a streamlined Gating-ML 2.0. Gating-ML has been significantly simplified and therefore easier to support by software tools. To aid developers, free, open source reference implementations, compliance tests, and detailed examples are provided to stimulate further commercial adoption. ISAC has approved Gating-ML as a standard ready for deployment in the public domain and encourages its support within the community as it is at a mature stage of development having undergone extensive review and testing, under both theoretical and practical conditions. © 2015 International Society for Advancement of Cytometry.

Learning Specific Content in Technology Education: Learning Study as a Collaborative Method in Swedish Preschool Class Using Hands-On Material

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Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

2014-01-01

This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study…

Learning Physics-based Models in Hydrology under the Framework of Generative Adversarial Networks

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Karpatne, A.; Kumar, V.

2017-12-01

Generative adversarial networks (GANs), that have been highly successful in a number of applications involving large volumes of labeled and unlabeled data such as computer vision, offer huge potential for modeling the dynamics of physical processes that have been traditionally studied using simulations of physics-based models. While conventional physics-based models use labeled samples of input/output variables for model calibration (estimating the right parametric forms of relationships between variables) or data assimilation (identifying the most likely sequence of system states in dynamical systems), there is a greater opportunity to explore the full power of machine learning (ML) methods (e.g, GANs) for studying physical processes currently suffering from large knowledge gaps, e.g. ground-water flow. However, success in this endeavor requires a principled way of combining the strengths of ML methods with physics-based numerical models that are founded on a wealth of scientific knowledge. This is especially important in scientific domains like hydrology where the number of data samples is small (relative to Internet-scale applications such as image recognition where machine learning methods has found great success), and the physical relationships are complex (high-dimensional) and non-stationary. We will present a series of methods for guiding the learning of GANs using physics-based models, e.g., by using the outputs of physics-based models as input data to the generator-learner framework, and by using physics-based models as generators trained using validation data in the adversarial learning framework. These methods are being developed under the broad paradigm of theory-guided data science that we are developing to integrate scientific knowledge with data science methods for accelerating scientific discovery.

The performance review of EEWS(Earthquake Early Warning System) about Gyeongju earthquakes with Ml 5.1 and Ml 5.8 in Korea

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Park, Jung-Ho; Chi, Heon-Cheol; Lim, In-Seub; Seong, Yun-Jeong; Park, Jihwan

2017-04-01

EEW(Earthquake Early Warning) service to the public has been officially operated by KMA (Korea Meteorological Administration) from 2015 in Korea. For the KMA's official EEW service, KIGAM has adopted ElarmS from UC Berkeley BSL and modified local magnitude relation, 1-D travel time curves and association procedures with real time waveform from about 201 seismic stations of KMA, KIGAM, KINS and KEPRI. There were two moderate size earthquakes with magnitude Ml 5.1 and Ml 5.8 close to Gyeongju city located at the southeastern part of Korea on Sep. 12. 2016. We have checked the performance of EEWS(Earthquake Early Warning System) named as TrigDB by KIGAM reviewing of these two Gyeongju earthquakes. The nearest station to epicenters of two earthquakes Ml 5.1(35.7697 N, 129.1904 E) and Ml 5.8(35.7632 N, 129.1898 E) was MKL which detected P phases in about 2.1 and 3.6 seconds after the origin times respectively. The first events were issued in 6.3 and 7.0 seconds from each origin time. Because of the unstable results on the early steps due to very few stations and unexpected automated analysis, KMA has the policy to wait for more 20 seconds for confirming the reliability. For these events KMA published EEW alarms in about 26 seconds after origin times with M 5.3 and M 5.9 respectively.

HEP meets ML award talk : XGBoost

CERN Multimedia

CERN. Geneva; CHEN, Tianqi

2015-01-01

Tianqi Chen and Tong He (team crowwork) have provided very early in the challenge to all participants XGBoost (for eXtreme Gradient Boosted). It is a parallelised software to train boost decision trees, which has been effectively used by many participants to the challenge. For this, they have won the "HEP meets ML" award which is the invitation to CERN happening today.

imzML: Imaging Mass Spectrometry Markup Language: A common data format for mass spectrometry imaging.

Science.gov (United States)

Römpp, Andreas; Schramm, Thorsten; Hester, Alfons; Klinkert, Ivo; Both, Jean-Pierre; Heeren, Ron M A; Stöckli, Markus; Spengler, Bernhard

2011-01-01

Imaging mass spectrometry is the method of scanning a sample of interest and generating an "image" of the intensity distribution of a specific analyte. The data sets consist of a large number of mass spectra which are usually acquired with identical settings. Existing data formats are not sufficient to describe an MS imaging experiment completely. The data format imzML was developed to allow the flexible and efficient exchange of MS imaging data between different instruments and data analysis software.For this purpose, the MS imaging data is divided in two separate files. The mass spectral data is stored in a binary file to ensure efficient storage. All metadata (e.g., instrumental parameters, sample details) are stored in an XML file which is based on the standard data format mzML developed by HUPO-PSI. The original mzML controlled vocabulary was extended to include specific parameters of imaging mass spectrometry (such as x/y position and spatial resolution). The two files (XML and binary) are connected by offset values in the XML file and are unambiguously linked by a universally unique identifier. The resulting datasets are comparable in size to the raw data and the separate metadata file allows flexible handling of large datasets.Several imaging MS software tools already support imzML. This allows choosing from a (growing) number of processing tools. One is no longer limited to proprietary software, but is able to use the processing software which is best suited for a specific question or application. On the other hand, measurements from different instruments can be compared within one software application using identical settings for data processing. All necessary information for evaluating and implementing imzML can be found at http://www.imzML.org .

Learning in Non-Stationary Environments Methods and Applications

CERN Document Server

Lughofer, Edwin

2012-01-01

Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences.   Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dyna...

Deep Learning for ECG Classification

Science.gov (United States)

Pyakillya, B.; Kazachenko, N.; Mikhailovsky, N.

2017-10-01

The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

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Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further

QualityML: a dictionary for quality metadata encoding

Science.gov (United States)

Ninyerola, Miquel; Sevillano, Eva; Serral, Ivette; Pons, Xavier; Zabala, Alaitz; Bastin, Lucy; Masó, Joan

2014-05-01

The scenario of rapidly growing geodata catalogues requires tools focused on facilitate users the choice of products. Having quality fields populated in metadata allow the users to rank and then select the best fit-for-purpose products. In this direction, we have developed the QualityML (http://qualityml.geoviqua.org), a dictionary that contains hierarchically structured concepts to precisely define and relate quality levels: from quality classes to quality measurements. Generically, a quality element is the path that goes from the higher level (quality class) to the lowest levels (statistics or quality metrics). This path is used to encode quality of datasets in the corresponding metadata schemas. The benefits of having encoded quality, in the case of data producers, are related with improvements in their product discovery and better transmission of their characteristics. In the case of data users, particularly decision-makers, they would find quality and uncertainty measures to take the best decisions as well as perform dataset intercomparison. Also it allows other components (such as visualization, discovery, or comparison tools) to be quality-aware and interoperable. On one hand, the QualityML is a profile of the ISO geospatial metadata standards providing a set of rules for precisely documenting quality indicator parameters that is structured in 6 levels. On the other hand, QualityML includes semantics and vocabularies for the quality concepts. Whenever possible, if uses statistic expressions from the UncertML dictionary (http://www.uncertml.org) encoding. However it also extends UncertML to provide list of alternative metrics that are commonly used to quantify quality. A specific example, based on a temperature dataset, is shown below. The annual mean temperature map has been validated with independent in-situ measurements to obtain a global error of 0.5 ° C. Level 0: Quality class (e.g., Thematic accuracy) Level 1: Quality indicator (e.g., Quantitative

A SysML-based Integration Framework for the Engineering of Mechatronic Systems

OpenAIRE

Chami, Muhammad; Seemüller, Holger; Voos, Holger

2010-01-01

The engineering discipline mechatronics is one of the main innovation leader in industry nowadays. With the need for an optimal synergetic integration of the involved disciplines, the engineering process of mechatronic systems is faced with an increasing complexity and the interdisciplinary nature of these systems. New methods and techniques have to be developed to deal with these challenges. This document presents an approach of a SysML-based integration framework that s...

Separation of pulsar signals from noise using supervised machine learning algorithms

Science.gov (United States)

Bethapudi, S.; Desai, S.

2018-04-01

We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP), Adaboost, Gradient Boosting Classifier (GBC), and XGBoost, for the separation of pulsars from radio frequency interference (RFI) and other sources of noise, using a dataset obtained from the post-processing of a pulsar search pipeline. This dataset was previously used for the cross-validation of the SPINN-based machine learning engine, obtained from the reprocessing of the HTRU-S survey data (Morello et al., 2014). We have used the Synthetic Minority Over-sampling Technique (SMOTE) to deal with high-class imbalance in the dataset. We report a variety of quality scores from all four of these algorithms on both the non-SMOTE and SMOTE datasets. For all the above ML methods, we report high accuracy and G-mean for both the non-SMOTE and SMOTE cases. We study the feature importances using Adaboost, GBC, and XGBoost and also from the minimum Redundancy Maximum Relevance approach to report algorithm-agnostic feature ranking. From these methods, we find that the signal to noise of the folded profile to be the best feature. We find that all the ML algorithms report FPRs about an order of magnitude lower than the corresponding FPRs obtained in Morello et al. (2014), for the same recall value.

Advanced Steel Microstructural Classification by Deep Learning Methods.

Science.gov (United States)

Azimi, Seyed Majid; Britz, Dominik; Engstler, Michael; Fritz, Mario; Mücklich, Frank

2018-02-01

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Evaluation of ML-MC as a Depth Discriminant in Yellowstone, USA and Italy

Science.gov (United States)

Li, Z.; Koper, K. D.; Burlacu, R.; Sun, D.; D'Amico, S.

2017-12-01

Recent work has shown that the difference between two magnitude scales, ML (local Richter magnitude) and MC (coda/duration magnitude), acts as a depth discriminant in Utah. Shallow seismic sources, such as mining induced earthquakes and explosions, have strongly negative ML-MC values, while deeper tectonic earthquakes have ML-MC values near zero. These observations imply that ML-MC might be effective at discriminating small explosions from deeper natural earthquakes at local distances. In this work, we examine seismicity catalogs for the Yellowstone region and Italy to determine if ML-MCacts as a depth discriminant in these regions as well. We identified 4,780 earthquakes that occurred in the Yellowstone region between Sept. 24, 1994 and March 31, 2017 for which both ML and MC were calculated. The ML-MC distribution is well described by a Gaussian function with a mean of 0.102 and a standard deviation of 0.326. We selected a subset of these events with accurate depths and determined mean ML-MC values in various depth bins. An event depth was considered accurate if the formal depth error was less than 2 km and either (1) the nearest station was within one focal depth or (2) the distance to the nearest station was smaller than the bin size. We find that ML-MC decreases as event depths become shallower than about 10 km. Similar to the results for Utah, the decrease is statistically significant and is robust with respect to small changes in bin size and the criteria used to define accurate depths. We used a similar process to evaluate whether ML-MC was a function of source depth for 63,555 earthquakes that occurred between April 16, 2005 and April 30, 2012 in Italy. The ML-MC values in Italy are also well described by a normal distribution, with a mean of -0.477 and standard deviation of 0.315. We again find a statistically significant decrease in ML-MC for shallow earthquakes. In contrast to the Yellowstone results, for Italy ML-MC decreases at a nearly constant rate

Postoperative Urinary Catheterization Thresholds of 500 versus 800 ml after Fast-track Total Hip and Knee Arthroplasty

DEFF Research Database (Denmark)

Bjerregaard, Lars Stryhn; Hornum, Ulla Marianne; Troldborg, C.

2016-01-01

arthroplasty (THA) and total knee arthroplasty (TKA). METHODS: This was a randomized, controlled, open-label trial that included patients greater than or equal to 18 yr who underwent THA or TKA in three Danish, fast-track, orthopedic departments. Consenting patients were eligible if they were cooperative......BACKGROUND: No evidence-based threshold exists for postoperative urinary bladder catheterization. The authors hypothesized that a catheterization threshold of 800 ml was superior to 500 ml in reducing postoperative urinary catheterization and urological complications after fast-track total hip...

Implementation of Active Learning Method in Unit Operations II Subject

OpenAIRE

Ma'mun, Sholeh

2018-01-01

ABSTRACT: Active Learning Method which requires students to take an active role in the process of learning in the classroom has been applied in Department of Chemical Engineering, Faculty of Industrial Technology, Islamic University of Indonesia for Unit Operations II subject in the Even Semester of Academic Year 2015/2016. The purpose of implementation of the learning method is to assist students in achieving competencies associated with the Unit Operations II subject and to help in creating...

The Effect of Salmon Fish’s Oil on Avoidance Learning in Mature Male Rats

Directory of Open Access Journals (Sweden)

Seyed Ebrahim Hosseini

2013-09-01

Full Text Available  Background & Objective: There is plenty of fatty acids of omega-3 in Salmon Fish that affect brain development and function, therefore, this study aimed to investigate the effect of Salmon fish’s oil on learning avoidance in mature male rats.   Materials & Methods: In this empirical study, we used 40 mature male rats that were enrolled into control, sham and experimental groups. The control group was not treated. The sham group received only 1 ml saline daily, 3 experimental groups of different types received 0.25, 0.5 and 0.75 ml/kg body weight respectively, for 28 days. For the evaluation of avoidance learning behavior, the shuttle box was used. The data was evaluated using ANOVA.    Results: The results suggest that consumption of salmon oil significantly increases in minimum and maximum doses (P<0.01 and average doses (P<0.001 the process of avoidance learning.   Conclusion: Salmon oil can lead to increased learning. Therefore, further investigation can be used to treat learning disorders and memory . 

TEACHING METHODS IN MBA AND LIFELONG LEARNING PROGRAMMES FOR MANAGERS

Directory of Open Access Journals (Sweden)

Jarošová, Eva

2017-09-01

Full Text Available Teaching methods in MBA and Lifelong Learning Programmes (LLP for managers should be topically relevant in terms of content as well as the teaching methods used. In terms of the content, the integral part of MBA and Lifelong Learning Programmes for managers should be the development of participants’ leadership competencies and their understanding of current leadership concepts. The teaching methods in educational programmes for managers as adult learners should correspond to the strategy of learner-centred teaching that focuses on the participants’ learning process and their active involvement in class. The focus on the participants’ learning process also raises questions about whether the programme’s participants perceive the teaching methods used as useful and relevant for their development as leaders. The paper presents the results of the analysis of the responses to these questions in a sample of 54 Czech participants in the MBA programme and of lifelong learning programmes at the University of Economics, Prague. The data was acquired based on written or electronically submitted questionnaires. The data was analysed in relation to the usefulness of the teaching methods for understanding the concepts of leadership, leadership skills development as well as respondents’ personal growth. The results show that the respondents most valued the methods that enabled them to get feedback, activated them throughout the programme and got them involved in discussions with others in class. Implications for managerial education practices are discussed.

Investigating Learning with an Interactive Tutorial: A Mixed-Methods Strategy

Science.gov (United States)

de Villiers, M. R.; Becker, Daphne

2017-01-01

From the perspective of parallel mixed-methods research, this paper describes interactivity research that employed usability-testing technology to analyse cognitive learning processes; personal learning styles and times; and errors-and-recovery of learners using an interactive e-learning tutorial called "Relations." "Relations"…

Rare A2ML1 variants confer susceptibility to otitis media

Science.gov (United States)

Santos-Cortez, Regie Lyn P.; Chiong, Charlotte M.; Reyes-Quintos, Ma. Rina T.; Tantoco, Ma. Leah C.; Wang, Xin; Acharya, Anushree; Abbe, Izoduwa; Giese, Arnaud P.; Smith, Joshua D.; Allen, E. Kaitlynn; Li, Biao; Cutiongco-de la Paz, Eva Maria; Garcia, Marieflor Cristy; Llanes, Erasmo Gonzalo D.V.; Labra, Patrick John; Gloria-Cruz, Teresa Luisa I.; Chan, Abner L.; Wang, Gao T.; Daly, Kathleen A.; Shendure, Jay; Bamshad, Michael J.; Nickerson, Deborah A.; Patel, Janak A.; Riazuddin, Saima; Sale, Michele M.; Chonmaitree, Tasnee; Ahmed, Zubair M.; Abes, Generoso T.; Leal, Suzanne M.

2015-01-01

A duplication variant within middle-ear-specific gene A2ML1 co-segregates with otitis media in an indigenous Filipino pedigree (LOD score=7.5 at reduced penetrance) and lies within a founder haplotype that is also shared by three otitis-prone European- and Hispanic-American children, but is absent in non-otitis-prone children and >62,000 next-generation sequences. Seven additional A2ML1 variants were identified in six otitis-prone children. Collectively our studies support a role for A2ML1 in the pathophysiology of otitis media. PMID:26121085

Considerations for Task Analysis Methods and Rapid E-Learning Development Techniques

Directory of Open Access Journals (Sweden)

Dr. Ismail Ipek

2014-02-01

Full Text Available The purpose of this paper is to provide basic dimensions for rapid training development in e-learning courses in education and business. Principally, it starts with defining task analysis and how to select tasks for analysis and task analysis methods for instructional design. To do this, first, learning and instructional technologies as visions of the future were discussed. Second, the importance of task analysis methods in rapid e-learning was considered, with learning technologies as asynchronous and synchronous e-learning development. Finally, rapid instructional design concepts and e-learning design strategies were defined and clarified with examples, that is, all steps for effective task analysis and rapid training development techniques based on learning and instructional design approaches were discussed, such as m-learning and other delivery systems. As a result, the concept of task analysis, rapid e-learning development strategies and the essentials of online course design were discussed, alongside learner interface design features for learners and designers.

LEARNING CURVE IN SINGLE-LEVEL MINIMALLY INVASIVE TLIF: EXPERIENCE OF A NEUROSURGEON

Directory of Open Access Journals (Sweden)

Samuel Romano-Feinholz

Full Text Available ABSTRACT Objective: To describe the learning curve that shows the progress of a single neurosurgeon when performing single-level MI-TLIF. Methods: We included 99 consecutive patients who underwent single-level MI-TLIF by the same neurosurgeon (JASS. Patient’s demographic characteristics were analyzed. In addition, surgical time, intraoperative blood loss and hospital stay were evaluated. The learning curves were calculated with a piecewise regression model. Results: The mean age was 54.6 years. The learning curves showed an inverse relationship between the surgical experience and the variable analyzed, reaching an inflection point for surgical time in case 43 and for blood loss in case 48. The mean surgical time was 203.3 minutes (interquartile range [IQR] 150-240 minutes, intraoperative bleeding was 97.4ml (IQR 40-100ml and hospital stay of four days (IQR 3-5 days. Conclusions: MI-TLIF is a very frequent surgical procedure due to its effectiveness and safety, which has shown similar results to open procedure. According to this study, the required learning curve is slightly higher than for open procedures, and is reached after about 45 cases.

qcML: an exchange format for quality control metrics from mass spectrometry experiments.

Science.gov (United States)

Walzer, Mathias; Pernas, Lucia Espona; Nasso, Sara; Bittremieux, Wout; Nahnsen, Sven; Kelchtermans, Pieter; Pichler, Peter; van den Toorn, Henk W P; Staes, An; Vandenbussche, Jonathan; Mazanek, Michael; Taus, Thomas; Scheltema, Richard A; Kelstrup, Christian D; Gatto, Laurent; van Breukelen, Bas; Aiche, Stephan; Valkenborg, Dirk; Laukens, Kris; Lilley, Kathryn S; Olsen, Jesper V; Heck, Albert J R; Mechtler, Karl; Aebersold, Ruedi; Gevaert, Kris; Vizcaíno, Juan Antonio; Hermjakob, Henning; Kohlbacher, Oliver; Martens, Lennart

2014-08-01

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

qcML: An Exchange Format for Quality Control Metrics from Mass Spectrometry Experiments*

Science.gov (United States)

Walzer, Mathias; Pernas, Lucia Espona; Nasso, Sara; Bittremieux, Wout; Nahnsen, Sven; Kelchtermans, Pieter; Pichler, Peter; van den Toorn, Henk W. P.; Staes, An; Vandenbussche, Jonathan; Mazanek, Michael; Taus, Thomas; Scheltema, Richard A.; Kelstrup, Christian D.; Gatto, Laurent; van Breukelen, Bas; Aiche, Stephan; Valkenborg, Dirk; Laukens, Kris; Lilley, Kathryn S.; Olsen, Jesper V.; Heck, Albert J. R.; Mechtler, Karl; Aebersold, Ruedi; Gevaert, Kris; Vizcaíno, Juan Antonio; Hermjakob, Henning; Kohlbacher, Oliver; Martens, Lennart

2014-01-01

Quality control is increasingly recognized as a crucial aspect of mass spectrometry based proteomics. Several recent papers discuss relevant parameters for quality control and present applications to extract these from the instrumental raw data. What has been missing, however, is a standard data exchange format for reporting these performance metrics. We therefore developed the qcML format, an XML-based standard that follows the design principles of the related mzML, mzIdentML, mzQuantML, and TraML standards from the HUPO-PSI (Proteomics Standards Initiative). In addition to the XML format, we also provide tools for the calculation of a wide range of quality metrics as well as a database format and interconversion tools, so that existing LIMS systems can easily add relational storage of the quality control data to their existing schema. We here describe the qcML specification, along with possible use cases and an illustrative example of the subsequent analysis possibilities. All information about qcML is available at http://code.google.com/p/qcml. PMID:24760958

Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

Science.gov (United States)

Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

2017-03-01

Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Machine Learning Methods for Attack Detection in the Smart Grid.

Science.gov (United States)

Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

2016-08-01

Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

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

Science.gov (United States)

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

2014-01-01

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

Research on demand-oriented Business English learning method

Directory of Open Access Journals (Sweden)

Zhou Yuan

2016-01-01

Full Text Available Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher vocational students to learn Business English Visual-audio-oral Course, master Business English knowledge, and improve communicative competence of Business English.

Learning Machine Learning: A Case Study

Science.gov (United States)

Lavesson, N.

2010-01-01

This correspondence reports on a case study conducted in the Master's-level Machine Learning (ML) course at Blekinge Institute of Technology, Sweden. The students participated in a self-assessment test and a diagnostic test of prerequisite subjects, and their results on these tests are correlated with their achievement of the course's learning…

QuakeML: status of the XML-based seismological data exchange format

Directory of Open Access Journals (Sweden)

Joachim Saul

2011-04-01

Full Text Available QuakeML is an XML-based data exchange standard for seismology that is in its fourth year of active community-driven development. Its development was motivated by the need to consolidate existing data formats for applications in statistical seismology, as well as setting a cutting-edge, community-agreed standard to foster interoperability of distributed infrastructures. The current release (version 1.2 is based on a public Request for Comments process and accounts for suggestions and comments provided by a broad international user community. QuakeML is designed as an umbrella schema under which several sub-packages are collected. The present scope of QuakeML 1.2 covers a basic description of seismic events including picks, arrivals, amplitudes, magnitudes, origins, focal mechanisms, and moment tensors. Work on additional packages (macroseismic information, ground motion, seismic inventory, and resource metadata has been started, but is at an early stage. Several applications based on the QuakeML data model have been created so far. Among these are earthquake catalog web services at the European Mediterranean Seismological Centre (EMSC, GNS Science, and the Southern California Earthquake Data Center (SCEDC, and QuakePy, an open-source Python-based seismicity analysis toolkit. Furthermore, QuakeML is being used in the SeisComP3 system from GFZ Potsdam, and in the Collaboratory for the Study of Earthquake Predictability (CSEP testing center installations, developed by Southern California Earthquake Center (SCEC. QuakeML is still under active and dynamic development. Further contributions from the community are crucial to its success and are highly welcome.

Activating teaching methods, studying responses and learning

OpenAIRE

Christensen, Hans Peter; Vigild, Martin E.; Thomsen, Erik; Szabo, Peter; Horsewell, Andy

2009-01-01

Students’ study strategies when exposed to activating teaching methods are measured, analysed and compared to study strategies in more traditional lecture-based teaching. The resulting learning outcome is discussed. Peer Reviewed

GeoSciML version 3: A GML application for geologic information

Science.gov (United States)

International Union of Geological Sciences., I. C.; Richard, S. M.

2011-12-01

After 2 years of testing and development, XML schema for GeoSciML version 3 are now ready for application deployment. GeoSciML draws from many geoscience data modelling efforts to establish a common suite of feature types to represent information associated with geologic maps (materials, structures, and geologic units) and observations including structure data, samples, and chemical analyses. After extensive testing and use case analysis, in December 2008 the CGI Interoperability Working Group (IWG) released GeoSciML 2.0 as an application schema for basic geological information. GeoSciML 2.0 is in use to deliver geologic data by the OneGeology Europe portal, the Geological Survey of Canada Groundwater Information Network (wet GIN), and the Auscope Mineral Resources portal. GeoSciML to version 3.0 is updated to OGC Geography Markup Language v3.2, re-engineered patterns for association of element values with controlled vocabulary concepts, incorporation of ISO19156 Observation and Measurement constructs for representing numeric and categorical values and for representing analytical data, incorporation of EarthResourceML to represent mineral occurrences and mines, incorporation of the GeoTime model to represent GSSP and stratigraphic time scale, and refactoring of the GeoSciML namespace to follow emerging ISO practices for decoupling of dependencies between standardized namespaces. These changes will make it easier for data providers to link to standard vocabulary and registry services. The depth and breadth of GeoSciML remains largely unchanged, covering the representation of geologic units, earth materials and geologic structures. ISO19156 elements and patterns are used to represent sampling features such as boreholes and rock samples, as well as geochemical and geochronologic measurements. Geologic structures include shear displacement structures (brittle faults and ductile shears), contacts, folds, foliations, lineations and structures with no preferred

Unintended consequences of machine learning in medicine?

Science.gov (United States)

McDonald, Laura; Ramagopalan, Sreeram V; Cox, Andrew P; Oguz, Mustafa

2017-01-01

Machine learning (ML) has the potential to significantly aid medical practice. However, a recent article highlighted some negative consequences that may arise from using ML decision support in medicine. We argue here that whilst the concerns raised by the authors may be appropriate, they are not specific to ML, and thus the article may lead to an adverse perception about this technique in particular. Whilst ML is not without its limitations like any methodology, a balanced view is needed in order to not hamper its use in potentially enabling better patient care.

Incorporating Meaningful Gamification in a Blended Learning Research Methods Class: Examining Student Learning, Engagement, and Affective Outcomes

Science.gov (United States)

Tan, Meng; Hew, Khe Foon

2016-01-01

In this study, we investigated how the use of meaningful gamification affects student learning, engagement, and affective outcomes in a short, 3-day blended learning research methods class using a combination of experimental and qualitative research methods. Twenty-two postgraduates were randomly split into two groups taught by the same…

Simultaneous anatomical sketching as learning by doing method of teaching human anatomy.

Science.gov (United States)

Noorafshan, Ali; Hoseini, Leila; Amini, Mitra; Dehghani, Mohammad-Reza; Kojuri, Javad; Bazrafkan, Leila

2014-01-01

Learning by lecture is a passive experience. Many innovative techniques have been presented to stimulate students to assume a more active attitude toward learning. In this study, simultaneous sketch drawing, as an interactive learning technique was applied to teach anatomy to the medical students. We reconstructed a fun interactive model of teaching anatomy as simultaneous anatomic sketching. To test the model's instruction effectiveness, we conducted a quasi- experimental study and then the students were asked to write their learning experiences in their portfolio, also their view was evaluated by a questionnaire. The results of portfolio evaluation revealed that students believed that this method leads to deep learning and understanding anatomical subjects better. Evaluation of the students' views on this teaching approach was showed that, more than 80% of the students were agreed or completely agreed with this statement that leaning anatomy concepts are easier and the class is less boring with this method. More than 60% of the students were agreed or completely agreed to sketch anatomical figures with professor simultaneously. They also found the sketching make anatomy more attractive and it reduced the time for learning anatomy. These number of students were agree or completely agree that the method help them learning anatomical concept in anatomy laboratory. More than 80% of the students found the simultaneous sketching is a good method for learning anatomy overall. Sketch drawing, as an interactive learning technique, is an attractive for students to learn anatomy.

Quantification of gastrointestinal liquid volumes and distribution following a 240 mL dose of water in the fasted state.

Science.gov (United States)

Mudie, Deanna M; Murray, Kathryn; Hoad, Caroline L; Pritchard, Susan E; Garnett, Martin C; Amidon, Gordon L; Gowland, Penny A; Spiller, Robin C; Amidon, Gregory E; Marciani, Luca

2014-09-02

The rate and extent of drug dissolution and absorption from solid oral dosage forms is highly dependent upon the volumes and distribution of gastric and small intestinal water. However, little is known about the time courses and distribution of water volumes in vivo in an undisturbed gut. Previous imaging studies offered a snapshot of water distribution in fasted humans and showed that water in the small intestine is distributed in small pockets. This study aimed to quantify the volume and number of water pockets in the upper gut of fasted healthy humans following ingestion of a glass of water (240 mL, as recommended for bioavailability/bioequivalence (BA/BE) studies), using recently validated noninvasive magnetic resonance imaging (MRI) methods. Twelve healthy volunteers underwent upper and lower abdominal MRI scans before drinking 240 mL (8 fluid ounces) of water. After ingesting the water, they were scanned at intervals for 2 h. The drink volume, inclusion criteria, and fasting conditions matched the international standards for BA/BE testing in healthy volunteers. The images were processed for gastric and intestinal total water volumes and for the number and volume of separate intestinal water pockets larger than 0.5 mL. The fasted stomach contained 35 ± 7 mL (mean ± SEM) of resting water. Upon drinking, the gastric fluid rose to 242 ± 9 mL. The gastric water volume declined rapidly after that with a half emptying time (T50%) of 13 ± 1 min. The mean gastric volume returned back to baseline 45 min after the drink. The fasted small bowel contained a total volume of 43 ± 14 mL of resting water. Twelve minutes after ingestion of water, small bowel water content rose to a maximum value of 94 ± 24 mL contained within 15 ± 2 pockets of 6 ± 2 mL each. At 45 min, when the glass of water had emptied completely from the stomach, total intestinal water volume was 77 ± 15 mL distributed into 16 ± 3 pockets of 5 ± 1 mL each. MRI provided unprecedented insights into

Decentralized indirect methods for learning automata games.

Science.gov (United States)

Tilak, Omkar; Martin, Ryan; Mukhopadhyay, Snehasis

2011-10-01

We discuss the application of indirect learning methods in zero-sum and identical payoff learning automata games. We propose a novel decentralized version of the well-known pursuit learning algorithm. Such a decentralized algorithm has significant computational advantages over its centralized counterpart. The theoretical study of such a decentralized algorithm requires the analysis to be carried out in a nonstationary environment. We use a novel bootstrapping argument to prove the convergence of the algorithm. To our knowledge, this is the first time that such analysis has been carried out for zero-sum and identical payoff games. Extensive simulation studies are reported, which demonstrate the proposed algorithm's fast and accurate convergence in a variety of game scenarios. We also introduce the framework of partial communication in the context of identical payoff games of learning automata. In such games, the automata may not communicate with each other or may communicate selectively. This comprehensive framework has the capability to model both centralized and decentralized games discussed in this paper.

Learning a specific content in technology education : Learning Study as collaborative method in Swedish preschool class using hands-on material 

OpenAIRE

Kilbrink, Nina; Bjurulf, Veronica; Blomberg, Ingela; Heidkamp, Anja; Hollsten, Ann-Christin

2014-01-01

This article describes the process of a learning study conducted in technology education in a Swedish preschool class. The learning study method used in this study is a collaborative method, where researchers and teachers work together as a team concerning teaching and learning about a specific learning object. The object of learning in this study concerns strong constructions and framed structures. This article describes how this learning study was conducted and discusses reflections made du...

Active semi-supervised learning method with hybrid deep belief networks.

Science.gov (United States)

Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

2014-01-01

In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

Method to predict process signals to learn for SVM

International Nuclear Information System (INIS)

Minowa, Hirotsugu; Gofuku, Akio

2013-01-01

Study of diagnostic system using machine learning to reduce the incidents of the plant is in advance because an accident causes large damage about human, economic and social loss. There is a problem that 2 performances between a classification performance and generalization performance on the machine diagnostic machine is exclusive. However, multi agent diagnostic system makes it possible to use a diagnostic machine specialized either performance by multi diagnostic machines can be used. We propose method to select optimized variables to improve classification performance. The method can also be used for other supervised learning machine but Support Vector Machine. This paper reports that our method and result of evaluation experiment applied our method to output 40% of Monju. (author)

Using Machine Learning to Predict MCNP Bias

Energy Technology Data Exchange (ETDEWEB)

Grechanuk, Pavel Aleksandrovi [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2018-01-09

For many real-world applications in radiation transport where simulations are compared to experimental measurements, like in nuclear criticality safety, the bias (simulated - experimental k_eff) in the calculation is an extremely important quantity used for code validation. The objective of this project is to accurately predict the bias of MCNP6 [1] criticality calculations using machine learning (ML) algorithms, with the intention of creating a tool that can complement the current nuclear criticality safety methods. In the latest release of MCNP6, the Whisper tool is available for criticality safety analysts and includes a large catalogue of experimental benchmarks, sensitivity profiles, and nuclear data covariance matrices. This data, coming from 1100+ benchmark cases, is used in this study of ML algorithms for criticality safety bias predictions.

Parallelization of the ROOT Machine Learning Methods

CERN Document Server

Vakilipourtakalou, Pourya

2016-01-01

Today computation is an inseparable part of scientific research. Specially in Particle Physics when there is a classification problem like discrimination of Signals from Backgrounds originating from the collisions of particles. On the other hand, Monte Carlo simulations can be used in order to generate a known data set of Signals and Backgrounds based on theoretical physics. The aim of Machine Learning is to train some algorithms on known data set and then apply these trained algorithms to the unknown data sets. However, the most common framework for data analysis in Particle Physics is ROOT. In order to use Machine Learning methods, a Toolkit for Multivariate Data Analysis (TMVA) has been added to ROOT. The major consideration in this report is the parallelization of some TMVA methods, specially Cross-Validation and BDT.

Students' Ideas on Cooperative Learning Method

Science.gov (United States)

Yoruk, Abdulkadir

2016-01-01

Aim of this study is to investigate students' ideas on cooperative learning method. For that purpose students who are studying at elementary science education program are distributed into two groups through an experimental design. Factors threaten the internal validity are either eliminated or reduced to minimum value. Data analysis is done…

Clinical application of l-123 MlBG cardiac imaging

International Nuclear Information System (INIS)

Kang, Do Young

2004-01-01

Cardiac neurotransmission imaging allows in vivo assessment of presynaptic reuptake, neurotransmitter storage and postsynaptic receptors. Among the various neurotransmitter, I-123 MlBG is most available and relatively well-established. Metaiodobenzylguanidine (MIBG) is an analogue of the false neurotransmitter guanethidine. It is taken up to adrenergic neurons by uptake-1 mechanism as same as norepinephrine. As tagged with I-123, it can be used to image sympathetic function in various organs including heart with planar or SPECT techniques. I-123 MIBG imaging has a unique advantage to evaluate myocardial neuronal activity in which the heart has no significant structural abnormality or even no functional derangement measured with other conventional examination. In patients with cardiomyopathy and heart failure, this imaging has most sensitive technique to predict prognosis and treatment response of betablocker or ACE inhibitor. In diabetic patients, it allow very early detection of autonomic neuropathy. In patients with dangerous arrhythmia such as ventricular tachycardia or fibrillation, MIBG imaging may be only an abnormal result among various exams. In patients with ischemic heart disease, sympathetic derangement may be used as the method of risk stratification. In heart transplanted patients, sympathetic reinnervation is well evaluated. Adriamycin-induced cardiotoxicity is detected earlier than ventricular dysfunction with sympathetic dysfunction. Neurodegenerative disorder such as Parkinson's disease or dementia with Lewy bodies has also cardiac sympathetic dysfunction. Noninvasive assessment of cardiac sympathetic nerve activity with l-123 MlBG imaging may be improve understanding of the pathophysiology of cardiac disease and make a contribution to predict survival and therapy efficacy

Clinical application of l-123 MlBG cardiac imaging

Energy Technology Data Exchange (ETDEWEB)

Kang, Do Young [College of Medicine, Donga Univ., Busan (Korea, Republic of)

2004-10-01

Cardiac neurotransmission imaging allows in vivo assessment of presynaptic reuptake, neurotransmitter storage and postsynaptic receptors. Among the various neurotransmitter, I-123 MlBG is most available and relatively well-established. Metaiodobenzylguanidine (MIBG) is an analogue of the false neurotransmitter guanethidine. It is taken up to adrenergic neurons by uptake-1 mechanism as same as norepinephrine. As tagged with I-123, it can be used to image sympathetic function in various organs including heart with planar or SPECT techniques. I-123 MIBG imaging has a unique advantage to evaluate myocardial neuronal activity in which the heart has no significant structural abnormality or even no functional derangement measured with other conventional examination. In patients with cardiomyopathy and heart failure, this imaging has most sensitive technique to predict prognosis and treatment response of betablocker or ACE inhibitor. In diabetic patients, it allow very early detection of autonomic neuropathy. In patients with dangerous arrhythmia such as ventricular tachycardia or fibrillation, MIBG imaging may be only an abnormal result among various exams. In patients with ischemic heart disease, sympathetic derangement may be used as the method of risk stratification. In heart transplanted patients, sympathetic reinnervation is well evaluated. Adriamycin-induced cardiotoxicity is detected earlier than ventricular dysfunction with sympathetic dysfunction. Neurodegenerative disorder such as Parkinson's disease or dementia with Lewy bodies has also cardiac sympathetic dysfunction. Noninvasive assessment of cardiac sympathetic nerve activity with l-123 MlBG imaging may be improve understanding of the pathophysiology of cardiac disease and make a contribution to predict survival and therapy efficacy.

E-learning as new method of medical education.

Science.gov (United States)

Masic, Izet

2008-01-01

NONE DECLARED Distance learning refers to use of technologies based on health care delivered on distance and covers areas such as electronic health, tele-health (e-health), telematics, telemedicine, tele-education, etc. For the need of e-health, telemedicine, tele-education and distance learning there are various technologies and communication systems from standard telephone lines to the system of transmission digitalized signals with modem, optical fiber, satellite links, wireless technologies, etc. Tele-education represents health education on distance, using Information Communication Technologies (ICT), as well as continuous education of a health system beneficiaries and use of electronic libraries, data bases or electronic data with data bases of knowledge. Distance learning (E-learning) as a part of tele-education has gained popularity in the past decade; however, its use is highly variable among medical schools and appears to be more common in basic medical science courses than in clinical education. Distance learning does not preclude traditional learning processes; frequently it is used in conjunction with in-person classroom or professional training procedures and practices. Tele-education has mostly been used in biomedical education as a blended learning method, which combines tele-education technology with traditional instructor-led training, where, for example, a lecture or demonstration is supplemented by an online tutorial. Distance learning is used for self-education, tests, services and for examinations in medicine i.e. in terms of self-education and individual examination services. The possibility of working in the exercise mode with image files and questions is an attractive way of self education. Automated tracking and reporting of learners' activities lessen faculty administrative burden. Moreover, e-learning can be designed to include outcomes assessment to determine whether learning has occurred. This review article evaluates the current

Lessons learned applying CASE methods/tools to Ada software development projects

Science.gov (United States)

Blumberg, Maurice H.; Randall, Richard L.

1993-01-01

This paper describes the lessons learned from introducing CASE methods/tools into organizations and applying them to actual Ada software development projects. This paper will be useful to any organization planning to introduce a software engineering environment (SEE) or evolving an existing one. It contains management level lessons learned, as well as lessons learned in using specific SEE tools/methods. The experiences presented are from Alpha Test projects established under the STARS (Software Technology for Adaptable and Reliable Systems) project. They reflect the front end efforts by those projects to understand the tools/methods, initial experiences in their introduction and use, and later experiences in the use of specific tools/methods and the introduction of new ones.

A Doctoral Seminar in Qualitative Research Methods: Lessons Learned

Directory of Open Access Journals (Sweden)

Suzanne Franco

2016-09-01

Full Text Available New qualitative research methods continue to emerge in response to factors such as renewed interest in mixed methods, better understanding of the importance of a researcher’s philosophical stance, as well as the increased use of technology in data collection and analysis, to name a few. As a result, those facilitating research methods courses must revisit content and instructional strategies in order to prepare well-informed researchers. Approaches range from paradigm to pragmatic emphasis. This descriptive case study of a doctoral seminar for novice qualitative researchers describes the intricacies of the syllabus of a pragmatic approach in a constructivist/social constructionist learning environment. The purpose was to document the delivery and faculty/student interactions and reactions. Noteworthy were the contradictions and frustrations in the delivery as well as in student experiences. In the end, student input led to seminal learning experiences. The confirmation of the effectiveness of a constructivist/social constructivist learning environment is applicable to higher education pedagogy in general.

A diagram retrieval method with multi-label learning

Science.gov (United States)

Fu, Songping; Lu, Xiaoqing; Liu, Lu; Qu, Jingwei; Tang, Zhi

2015-01-01

In recent years, the retrieval of plane geometry figures (PGFs) has attracted increasing attention in the fields of mathematics education and computer science. However, the high cost of matching complex PGF features leads to the low efficiency of most retrieval systems. This paper proposes an indirect classification method based on multi-label learning, which improves retrieval efficiency by reducing the scope of compare operation from the whole database to small candidate groups. Label correlations among PGFs are taken into account for the multi-label classification task. The primitive feature selection for multi-label learning and the feature description of visual geometric elements are conducted individually to match similar PGFs. The experiment results show the competitive performance of the proposed method compared with existing PGF retrieval methods in terms of both time consumption and retrieval quality.

VolcanoGasML: a format to exchange geochemical volcanic gases data

Directory of Open Access Journals (Sweden)

Eric Reiter

2007-01-01

Full Text Available Chemical analyses of volcanic gases consist of: location of sampling, date of sampling, identification of the sampling, etc. Nowadays, these data are generally represented in different formats. All of these formats are inflexible and machine dependent. XML has become the most important method of transferring data between computers. VolcanoGasML is a new format, based on XML, for the chemical analyses of volcanic gases. Its definition is divided into several layers: the first one describes the general information concerning the sample, the second, which is organized in several sublayers, contains the chemical data.

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

Science.gov (United States)

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Nurse practitioner preferences for distance education methods related to learning style, course content, and achievement.

Science.gov (United States)

Andrusyszyn, M A; Cragg, C E; Humbert, J

2001-04-01

The relationships among multiple distance delivery methods, preferred learning style, content, and achievement was sought for primary care nurse practitioner students. A researcher-designed questionnaire was completed by 86 (71%) participants, while 6 engaged in follow-up interviews. The results of the study included: participants preferred learning by "considering the big picture"; "setting own learning plans"; and "focusing on concrete examples." Several positive associations were found: learning on own with learning by reading, and setting own learning plans; small group with learning through discussion; large group with learning new things through hearing and with having learning plans set by others. The most preferred method was print-based material and the least preferred method was audio tape. The most suited method for content included video teleconferencing for counseling, political action, and transcultural issues; and video tape for physical assessment. Convenience, self-direction, and timing of learning were more important than delivery method or learning style. Preferred order of learning was reading, discussing, observing, doing, and reflecting. Recommended considerations when designing distance courses include a mix of delivery methods, specific content, outcomes, learner characteristics, and state of technology.

Exploring Non-Traditional Learning Methods in Virtual and Real-World Environments

Science.gov (United States)

Lukman, Rebeka; Krajnc, Majda

2012-01-01

This paper identifies the commonalities and differences within non-traditional learning methods regarding virtual and real-world environments. The non-traditional learning methods in real-world have been introduced within the following courses: Process Balances, Process Calculation, and Process Synthesis, and within the virtual environment through…

Path to Stochastic Stability: Comparative Analysis of Stochastic Learning Dynamics in Games

KAUST Repository

Jaleel, Hassan

2018-04-08

Stochastic stability is a popular solution concept for stochastic learning dynamics in games. However, a critical limitation of this solution concept is its inability to distinguish between different learning rules that lead to the same steady-state behavior. We address this limitation for the first time and develop a framework for the comparative analysis of stochastic learning dynamics with different update rules but same steady-state behavior. We present the framework in the context of two learning dynamics: Log-Linear Learning (LLL) and Metropolis Learning (ML). Although both of these dynamics have the same stochastically stable states, LLL and ML correspond to different behavioral models for decision making. Moreover, we demonstrate through an example setup of sensor coverage game that for each of these dynamics, the paths to stochastically stable states exhibit distinctive behaviors. Therefore, we propose multiple criteria to analyze and quantify the differences in the short and medium run behavior of stochastic learning dynamics. We derive and compare upper bounds on the expected hitting time to the set of Nash equilibria for both LLL and ML. For the medium to long-run behavior, we identify a set of tools from the theory of perturbed Markov chains that result in a hierarchical decomposition of the state space into collections of states called cycles. We compare LLL and ML based on the proposed criteria and develop invaluable insights into the comparative behavior of the two dynamics.

An Activity-based Approach to the Learning and Teaching of Research Methods: Measuring Student Engagement and Learning

Directory of Open Access Journals (Sweden)

Eimear Fallon

2013-05-01

Full Text Available This paper discusses a research project carried out with 82 final and third year undergraduate students, learning Research Methods prior to undertaking an undergraduate thesis during the academic years 2010 and 2011. The research had two separate, linked objectives, (a to develop a Research Methods module that embraces an activity-based approach to learning in a group environment, (b to improve engagement by all students. The Research Methods module was previously taught through a traditional lecture-based format. Anecdotally, it was felt that student engagement was poor and learning was limited. It was believed that successful completion of the development of this Module would equip students with a deeply-learned battery of research skills to take into their further academic and professional careers. Student learning was achieved through completion of a series of activities based on different research methods. In order to encourage student engagement, a wide variety of activities were used. These activities included workshops, brainstorming, mind-mapping, presentations, written submissions, peer critiquing, lecture/seminar, and ‘speed dating’ with more senior students and self reflection. Student engagement was measured through a survey based on a U.S. National Survey of Student Engagement (2000. A questionnaire was devised to establish whether, and to what degree, students were engaged in the material that they were learning, while they were learning it. The results of the questionnaire were very encouraging with between 63% and 96% of students answering positively to a range of questions concerning engagement. In terms of the two objectives set, these were satisfactorily met. The module was successfully developed and continues to be delivered, based upon this new and significant level of student engagement.

Efficient automated synthesis of 2-(5-["1"8F]fluoropentyl)-2-methylmalonic acid (["1"8F]ML-10) on a commercial available ["1"8F]FDG synthesis module

International Nuclear Information System (INIS)

Liu, Shaoyu; Nie, Dahong; Jiang, Shende; Tang, Ganghua

2017-01-01

["1"8F]ML-10 (2-(5-["1"8F]fluoro-pentyl)-2-methylmalonic acid) is a small molecule positron emission tomography (PET) probe for apoptosis imaging. Automated synthesis of ["1"8F]ML-10 was developed by using two different purification methods through a direct saponification procedure on a modified commercial ["1"8F]Fluoro-2-Deoxyglucose (["1"8F]FDG) synthesizer. C18 purification method 1: The final ["1"8F]ML-10 solution containing ethanol was obtained with radiochemical yields of 60±5% (n=5) at the end of bombardment (EOB) and radiochemical purity of 98% in 35 min. Al_2O_3 and SCX purification method 2: To avoid possible side effects of a conventional ethanol-containing formulation, an new ethanol-free solution of ["1"8F]ML-10 was also developed, the radiochemical yields was 50±5% (n=5, EOB) within 45 min and the radiochemical purity was 98%. - Highlights: • The production of ["1"8F]ML-10 was optimized by using a straightforward saponification procedure. • Automated synthesis was performed on a commonly FDG synthesis module. • An ethanol-containing ["1"8F]ML-10 formulation was obtained with high radiochemical yield in a shorter time. • An ethanol-free formulation method of ["1"8F]ML-10 was also developed.

Research on demand-oriented Business English learning method

OpenAIRE

Zhou Yuan

2016-01-01

Business English is integrated with visual-audio-oral English, which focuses on the application for English listening and speaking skills in common business occasions, and acquire business knowledge and improve skills through English. This paper analyzes the Business English Visual-audio-oral Course, and learning situation of higher vocational students’ learning objectives, interests, vocabulary, listening and speaking, and focuses on the research of effective methods to guide the higher voca...

IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

Directory of Open Access Journals (Sweden)

A. S. Potapov

2014-09-01

Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

Science.gov (United States)

Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

2018-06-12

Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data.

Science.gov (United States)

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-07

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data

Science.gov (United States)

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-01

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Studying learning in the healthcare setting: the potential of quantitative diary methods.

Science.gov (United States)

Ciere, Yvette; Jaarsma, Debbie; Visser, Annemieke; Sanderman, Robbert; Snippe, Evelien; Fleer, Joke

2015-08-01

Quantitative diary methods are longitudinal approaches that involve the repeated measurement of aspects of peoples' experience of daily life. In this article, we outline the main characteristics and applications of quantitative diary methods and discuss how their use may further research in the field of medical education. Quantitative diary methods offer several methodological advantages, such as measuring aspects of learning with great detail, accuracy and authenticity. Moreover, they enable researchers to study how and under which conditions learning in the health care setting occurs and in which way learning can be promoted. Hence, quantitative diary methods may contribute to theory development and the optimization of teaching methods in medical education.

Comparing three experiential learning methods and their effect on medical students' attitudes to learning communication skills.

Science.gov (United States)

Koponen, Jonna; Pyörälä, Eeva; Isotalus, Pekka

2012-01-01

Despite numerous studies exploring medical students' attitudes to communication skills learning (CSL), there are apparently no studies comparing different experiential learning methods and their influence on students' attitudes. We compared medical students' attitudes to learning communication skills before and after a communication course in the data as a whole, by gender and when divided into three groups using different methods. Second-year medical students (n = 129) were randomly assigned to three groups. In group A (n = 42) the theatre in education method, in group B (n = 44) simulated patients and in group C (n = 43) role-play were used. The data were gathered before and after the course using Communication Skills Attitude Scale. Students' positive attitudes to learning communication skills (PAS; positive attitude scale) increased significantly and their negative attitudes (NAS; negative attitude scale) decreased significantly between the beginning and end of the course. Female students had more positive attitudes than the male students. There were no significant differences in the three groups in the mean scores for PAS or NAS measured before or after the course. The use of experiential methods and integrating communication skills training with visits to health centres may help medical students to appreciate the importance of CSL.

Application and performance of an ML-EM algorithm in NEXT

Science.gov (United States)

Simón, A.; Lerche, C.; Monrabal, F.; Gómez-Cadenas, J. J.; Álvarez, V.; Azevedo, C. D. R.; Benlloch-Rodríguez, J. M.; Borges, F. I. G. M.; Botas, A.; Cárcel, S.; Carrión, J. V.; Cebrián, S.; Conde, C. A. N.; Díaz, J.; Diesburg, M.; Escada, J.; Esteve, R.; Felkai, R.; Fernandes, L. M. P.; Ferrario, P.; Ferreira, A. L.; Freitas, E. D. C.; Goldschmidt, A.; González-Díaz, D.; Gutiérrez, R. M.; Hauptman, J.; Henriques, C. A. O.; Hernandez, A. I.; Hernando Morata, J. A.; Herrero, V.; Jones, B. J. P.; Labarga, L.; Laing, A.; Lebrun, P.; Liubarsky, I.; López-March, N.; Losada, M.; Martín-Albo, J.; Martínez-Lema, G.; Martínez, A.; McDonald, A. D.; Monteiro, C. M. B.; Mora, F. J.; Moutinho, L. M.; Muñoz Vidal, J.; Musti, M.; Nebot-Guinot, M.; Novella, P.; Nygren, D. R.; Palmeiro, B.; Para, A.; Pérez, J.; Querol, M.; Renner, J.; Ripoll, L.; Rodríguez, J.; Rogers, L.; Santos, F. P.; dos Santos, J. M. F.; Sofka, C.; Sorel, M.; Stiegler, T.; Toledo, J. F.; Torrent, J.; Tsamalaidze, Z.; Veloso, J. F. C. A.; Webb, R.; White, J. T.; Yahlali, N.

2017-08-01

The goal of the NEXT experiment is the observation of neutrinoless double beta decay in 136Xe using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of 136Xe) for events distributed over the full active volume of the TPC.

"Debate" Learning Method and Its Implications for the Formal Education System

Science.gov (United States)

Najafi, Mohammad; Motaghi, Zohre; Nasrabadi, Hassanali Bakhtiyar; Heshi, Kamal Nosrati

2016-01-01

Regarding the importance of enhancement in learner's social skills, especially in learning process, this study tries to introduce one of the group learning programs entitled "debate" as a teaching method in Iran religious universities. It also considers the concept and the history of this method by qualitative and descriptive-analytical…

Adult Learners' Preferred Methods of Learning Preventative Heart Disease Care

Science.gov (United States)

Alavi, Nasim

2016-01-01

The purpose of this study was to investigate the preferred method of learning about heart disease by adult learners. This research study also investigated if there was a statistically significant difference between race/ethnicity, age, and gender of adult learners and their preferred method of learning preventative heart disease care. This…

Strategies and Principles of Distributed Machine Learning on Big Data

Directory of Open Access Journals (Sweden)

Eric P. Xing

2016-06-01

Full Text Available The rise of big data has led to new demands for machine learning (ML systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area

Multi-Role Project (MRP): A New Project-Based Learning Method for STEM

Science.gov (United States)

Warin, Bruno; Talbi, Omar; Kolski, Christophe; Hoogstoel, Frédéric

2016-01-01

This paper presents the "Multi-Role Project" method (MRP), a broadly applicable project-based learning method, and describes its implementation and evaluation in the context of a Science, Technology, Engineering, and Mathematics (STEM) course. The MRP method is designed around a meta-principle that considers the project learning activity…

Deep learning for single-molecule science

Science.gov (United States)

Albrecht, Tim; Slabaugh, Gregory; Alonso, Eduardo; Al-Arif, SM Masudur R.

2017-10-01

Exploring and making predictions based on single-molecule data can be challenging, not only due to the sheer size of the datasets, but also because a priori knowledge about the signal characteristics is typically limited and poor signal-to-noise ratio. For example, hypothesis-driven data exploration, informed by an expectation of the signal characteristics, can lead to interpretation bias or loss of information. Equally, even when the different data categories are known, e.g., the four bases in DNA sequencing, it is often difficult to know how to make best use of the available information content. The latest developments in machine learning (ML), so-called deep learning (DL) offer interesting, new avenues to address such challenges. In some applications, such as speech and image recognition, DL has been able to outperform conventional ML strategies and even human performance. However, to date DL has not been applied much in single-molecule science, presumably in part because relatively little is known about the ‘internal workings’ of such DL tools within single-molecule science as a field. In this Tutorial, we make an attempt to illustrate in a step-by-step guide how one of those, a convolutional neural network (CNN), may be used for base calling in DNA sequencing applications. We compare it with a SVM as a more conventional ML method, and discuss some of the strengths and weaknesses of the approach. In particular, a ‘deep’ neural network has many features of a ‘black box’, which has important implications on how we look at and interpret data.

Computer-enhanced visual learning method: a paradigm to teach and document surgical skills.

Science.gov (United States)

Maizels, Max; Mickelson, Jennie; Yerkes, Elizabeth; Maizels, Evelyn; Stork, Rachel; Young, Christine; Corcoran, Julia; Holl, Jane; Kaplan, William E

2009-09-01

Changes in health care are stimulating residency training programs to develop new methods for teaching surgical skills. We developed Computer-Enhanced Visual Learning (CEVL) as an innovative Internet-based learning and assessment tool. The CEVL method uses the educational procedures of deliberate practice and performance to teach and learn surgery in a stylized manner. CEVL is a learning and assessment tool that can provide students and educators with quantitative feedback on learning a specific surgical procedure. Methods involved examine quantitative data of improvement in surgical skills. Herein, we qualitatively describe the method and show how program directors (PDs) may implement this technique in their residencies. CEVL allows an operation to be broken down into teachable components. The process relies on feedback and remediation to improve performance, with a focus on learning that is applicable to the next case being performed. CEVL has been shown to be effective for teaching pediatric orchiopexy and is being adapted to additional adult and pediatric procedures and to office examination skills. The CEVL method is available to other residency training programs.

pymzML--Python module for high-throughput bioinformatics on mass spectrometry data.

Science.gov (United States)

Bald, Till; Barth, Johannes; Niehues, Anna; Specht, Michael; Hippler, Michael; Fufezan, Christian

2012-04-01

pymzML is an extension to Python that offers (i) an easy access to mass spectrometry (MS) data that allows the rapid development of tools, (ii) a very fast parser for mzML data, the standard data format in MS and (iii) a set of functions to compare or handle spectra. pymzML requires Python2.6.5+ and is fully compatible with Python3. The module is freely available on http://pymzml.github.com or pypi, is published under LGPL license and requires no additional modules to be installed. christian@fufezan.net.

Spectra, chromatograms, Metadata: mzML-the standard data format for mass spectrometer output.

Science.gov (United States)

Turewicz, Michael; Deutsch, Eric W

2011-01-01

This chapter describes Mass Spectrometry Markup Language (mzML), an XML-based and vendor-neutral standard data format for storage and exchange of mass spectrometer output like raw spectra and peak lists. It is intended to replace its two precursor data formats (mzData and mzXML), which had been developed independently a few years earlier. Hence, with the release of mzML, the problem of having two different formats for the same purposes is solved, and with it the duplicated effort of maintaining and supporting two data formats. The new format has been developed by a broad-based consortium of major instrument vendors, software vendors, and academic researchers under the aegis of the Human Proteome Organisation (HUPO), Proteomics Standards Initiative (PSI), with full participation of the main developers of the precursor formats. This comprehensive approach helped mzML to become a generally accepted standard. Furthermore, the collaborative development insured that mzML has adopted the best features of its precursor formats. In this chapter, we discuss mzML's development history, its design principles and use cases, as well as its main building components. We also present the available documentation, an example file, and validation software for mzML.

The Effect of WhatsApp Messenger As Mobile Learning Integrated with Group Investigation Method of Learning Achievement

Directory of Open Access Journals (Sweden)

Hendrik Pratama

2017-12-01

Full Text Available The purpose of this research was determined the effect of application WhatsApp Messenger in the Group Investigation (GI method on learning achievement. The methods used experimental research with control group pretest-postest design. The sampling procedure used the purposive sampling technique that consists of 17 students as a control group and 17 students as an experimental group. The sample in this research is students in Electrical Engineering Education Study Program. The experimental group used the GI method that integrated with WhatsApp Messenger. The control group used lecture method without social media integration. The collecting data used observation, documentation, interview, questionnaire, and test. The researcher used a t-test for compared the control group and the experimental group’s learning outcomes at an alpha level of 0,05. The results showed differences between the experiment group and the control group. The study result of the experimental higher than the control groups. This learning was designed with start, grouping, planning, presenting, organizing, investigating, evaluating, ending’s stage. Integration of WhatsApp with group investigation method could cause the positive communication between student and lecturer. Discussion in this learning was well done, the student’s knowledge could appear in a group and the information could spread evenly and quickly.

Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

Science.gov (United States)

da Costa Tavares, Ofelia Cizela; Suyoto; Pranowo

2018-02-01

In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process) and SAW (simple additive Weighting) method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

Hybrid Method for Mobile learning Cooperative: Study of Timor Leste

Directory of Open Access Journals (Sweden)

da Costa Tavares Ofelia Cizela

2018-01-01

Full Text Available In the modern world today the decision support system is very useful to help in solving a problem, so this study discusses the learning process of savings and loan cooperatives in Timor Leste. The purpose of the observation is that the people of Timor Leste are still in the process of learning the use DSS for good saving and loan cooperative process. Based on existing research on the Timor Leste community on credit cooperatives, a mobile application will be built that will help the cooperative learning process in East Timorese society. The methods used for decision making are AHP (Analytical Hierarchy Process and SAW (simple additive Weighting method to see the result of each criterion and the weight of the value. The result of this research is mobile leaning cooperative in decision support system by using SAW and AHP method. Originality Value: Changed the two methods of mobile application development using AHP and SAW methods to help the decision support system process of a savings and credit cooperative in Timor Leste.

Learning Practice-Based Research Methods: Capturing the Experiences of MSW Students

Science.gov (United States)

Natland, Sidsel; Weissinger, Erika; Graaf, Genevieve; Carnochan, Sarah

2016-01-01

The literature on teaching research methods to social work students identifies many challenges, such as dealing with the tensions related to producing research relevant to practice, access to data to teach practice-based research, and limited student interest in learning research methods. This is an exploratory study of the learning experiences of…

Machine Learning Method Applied in Readout System of Superheated Droplet Detector

Science.gov (United States)

Liu, Yi; Sullivan, Clair Julia; d'Errico, Francesco

2017-07-01

Direct readability is one advantage of superheated droplet detectors in neutron dosimetry. Utilizing such a distinct characteristic, an imaging readout system analyzes image of the detector for neutron dose readout. To improve the accuracy and precision of algorithms in the imaging readout system, machine learning algorithms were developed. Deep learning neural network and support vector machine algorithms are applied and compared with generally used Hough transform and curvature analysis methods. The machine learning methods showed a much higher accuracy and better precision in recognizing circular gas bubbles.

Machine Learning Methods for Identifying Composition of Uranium Deposits in Kazakhstan

Directory of Open Access Journals (Sweden)

Kuchin Yan

2017-12-01

Full Text Available The paper explores geophysical methods of wells survey, as well as their role in the development of Kazakhstan’s uranium deposit mining efforts. An analysis of the existing methods for solving the problem of interpreting geophysical data using machine learning in petroleum geophysics is made. The requirements and possible applications of machine learning methods in regard to uranium deposits of Kazakhstan are formulated in the paper.

Alkaline protease production from industrial wastes by bacillus subtilis ML-4

International Nuclear Information System (INIS)

Sher, M.G.; Nadeem, M.; Syed, Q.; Irfan, M.; Baig, S.

2010-01-01

The influence of various culture conditions on protease production by Bacillus subtilis ML-4 was studied in the presence of growth medium containing poultry feed waste (5%), K/sub 2/HPO/sub 4/ (0.3%), CaCl/sub 2/ (0.03%) and MgSO/sub 4/ (0.015%). Maximum protease production (264.25 +- 1.86 U/ml) was observed at initial pH 9 with 3% (v/v) of inoculum size after 48 h of incubation at 37 degree C. The alkaline protease was stable over a broad range of temperature (30 to 60 degree C) and pH (8 to 11). However, maximum activity (155.45 U/ml) was observed at temperature 50 degree C and pH 10. (author)

Teaching numerical methods with IPython notebooks and inquiry-based learning

KAUST Repository

Ketcheson, David I.

2014-01-01

A course in numerical methods should teach both the mathematical theory of numerical analysis and the craft of implementing numerical algorithms. The IPython notebook provides a single medium in which mathematics, explanations, executable code, and visualizations can be combined, and with which the student can interact in order to learn both the theory and the craft of numerical methods. The use of notebooks also lends itself naturally to inquiry-based learning methods. I discuss the motivation and practice of teaching a course based on the use of IPython notebooks and inquiry-based learning, including some specific practical aspects. The discussion is based on my experience teaching a Masters-level course in numerical analysis at King Abdullah University of Science and Technology (KAUST), but is intended to be useful for those who teach at other levels or in industry.

Project Oriented Immersion Learning: Method and Results

DEFF Research Database (Denmark)

Icaza, José I.; Heredia, Yolanda; Borch, Ole M.

2005-01-01

A pedagogical approach called “project oriented immersion learning” is presented and tested on a graduate online course. The approach combines the Project Oriented Learning method with immersion learning in a virtual enterprise. Students assumed the role of authors hired by a fictitious publishing...... house that develops digital products including e-books, tutorials, web sites and so on. The students defined the problem that their product was to solve; choose the type of product and the content; and built the product following a strict project methodology. A wiki server was used as a platform to hold...

Model-driven development of smart grid services using SoaML

DEFF Research Database (Denmark)

Kosek, Anna Magdalena; Gehrke, Oliver

2014-01-01

This paper presents a model-driven software devel- opment process which can be applied to the design of smart grid services. The Service Oriented Architecture Modelling Language (SoaML) is used to describe the architecture as well as the roles and interactions between service participants....... The individual modelling steps and an example design of a SoaML model for a voltage control service are presented and explained. Finally, the paper discusses a proof-of-concept implementation of the modelled service in a smart grid testing laboratory....

Empowering and Engaging Students in Learning Research Methods

Science.gov (United States)

Liu, Shuang; Breit, Rhonda

2013-01-01

The capacity to conduct research is essential for university graduates to survive and thrive in their future career. However, research methods courses have often been considered by students as "abstract", "uninteresting", and "hard". Thus, motivating students to engage in the process of learning research methods has become a crucial challenge for…

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

Science.gov (United States)

Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

2016-01-01

Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

Integration of Traditional and E-Learning Methods to Improve Learning Outcomes for Dental Students in Histopathology.

Science.gov (United States)

Ariana, Armin; Amin, Moein; Pakneshan, Sahar; Dolan-Evans, Elliot; Lam, Alfred K

2016-09-01

Dental students require a basic ability to explain and apply general principles of pathology to systemic, dental, and oral pathology. Although there have been recent advances in electronic and online resources, the academic effectiveness of using self-directed e-learning tools in pathology courses for dental students is unclear. The aim of this study was to determine if blended learning combining e-learning with traditional learning methods of lectures and tutorials would improve students' scores and satisfaction over those who experienced traditional learning alone. Two consecutive cohorts of Bachelor of Dentistry and Oral Health students taking the general pathology course at Griffith University in Australia were compared. The control cohort experienced traditional methods only, while members of the study cohort were also offered self-directed learning materials including online resources and online microscopy classes. Final assessments for the course were used to compare the differences in effectiveness of the intervention, and students' satisfaction with the teaching format was evaluated using questionnaires. On the final course assessments, students in the study cohort had significantly higher scores than students in the control cohort (plearning tools such as virtual microscopy and interactive online resources for delivering pathology instruction can be an effective supplement for developing dental students' competence, confidence, and satisfaction.

Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 2.

Science.gov (United States)

Bergmann, Frank T; Cooper, Jonathan; Le Novère, Nicolas; Nickerson, David; Waltemath, Dagmar

2015-09-04

The number, size and complexity of computational models of biological systems are growing at an ever increasing pace. It is imperative to build on existing studies by reusing and adapting existing models and parts thereof. The description of the structure of models is not sufficient to enable the reproduction of simulation results. One also needs to describe the procedures the models are subjected to, as recommended by the Minimum Information About a Simulation Experiment (MIASE) guidelines. This document presents Level 1 Version 2 of the Simulation Experiment Description Markup Language (SED-ML), a computer-readable format for encoding simulation and analysis experiments to apply to computational models. SED-ML files are encoded in the Extensible Markup Language (XML) and can be used in conjunction with any XML-based model encoding format, such as CellML or SBML. A SED-ML file includes details of which models to use, how to modify them prior to executing a simulation, which simulation and analysis procedures to apply, which results to extract and how to present them. Level 1 Version 2 extends the format by allowing the encoding of repeated and chained procedures.

The Learners’ Attitudes towards Using Different Learning Methods in E-Learning Portal Environment

Directory of Open Access Journals (Sweden)

Issham Ismail

2011-09-01

Full Text Available This study investigates the learners’ preference of academic, collaborative and social interaction towards interaction methods in e-learning portal. Academic interaction consists of interaction between learners and online learning resources such as online reading, online explanation, online examination and also online question answering. Collaborative interaction occurs when learners interact among themselves using online group discussion. Social interaction happens when learners and instructors participate in the session either via online text chatting or voice chatting. The study employed qualitative methodology where data were collected through questionnaire that was administered to 933 distance education students from Bachelor of Management, Bachelor of Science, Bachelor of Social Science and Bachelor of Art. The survey responses were tabulated in a 5-point Likert scale and analyzed using the Statistical Package for Social Science (SPSS Version 12.0 based on frequency and percentage distribution. The result of the study suggest that among three types of interaction, most of the student prefer academic interaction for their learning supports in e-learning portal compared to collaborative and social interaction. They wish to interact with learning content rather than interact with people. They prefer to read and learn from the resources rather than sharing knowledge among themselves and instructors via collaborative and social interaction.

Diguanylate cyclase activity of the Mycobacterium leprae T cell antigen ML1419c.

Science.gov (United States)

Rotcheewaphan, Suwatchareeporn; Belisle, John T; Webb, Kristofor J; Kim, Hee-Jin; Spencer, John S; Borlee, Bradley R

2016-09-01

The second messenger, bis-(3',5')-cyclic dimeric guanosine monophosphate (cyclic di-GMP), is involved in the control of multiple bacterial phenotypes, including those that impact host-pathogen interactions. Bioinformatics analyses predicted that Mycobacterium leprae, an obligate intracellular bacterium and the causative agent of leprosy, encodes three active diguanylate cyclases. In contrast, the related pathogen Mycobacterium tuberculosis encodes only a single diguanylate cyclase. One of the M. leprae unique diguanylate cyclases (ML1419c) was previously shown to be produced early during the course of leprosy. Thus, functional analysis of ML1419c was performed. The gene encoding ML1419c was cloned and expressed in Pseudomonas aeruginosa PAO1 to allow for assessment of cyclic di-GMP production and cyclic di-GMP-mediated phenotypes. Phenotypic studies revealed that ml1419c expression altered colony morphology, motility and biofilm formation of P. aeruginosa PAO1 in a manner consistent with increased cyclic di-GMP production. Direct measurement of cyclic di-GMP levels by liquid chromatography-mass spectrometry confirmed that ml1419c expression increased cyclic di-GMP production in P. aeruginosa PAO1 cultures in comparison to the vector control. The observed phenotypes and increased levels of cyclic di-GMP detected in P. aeruginosa expressing ml1419c could be abrogated by mutation of the active site in ML1419c. These studies demonstrated that ML1419c of M. leprae functions as diguanylate cyclase to synthesize cyclic di-GMP. Thus, this protein was renamed DgcA (Diguanylate cyclase A). These results also demonstrated the ability to use P. aeruginosa as a heterologous host for characterizing the function of proteins involved in the cyclic di-GMP pathway of a pathogen refractory to in vitro growth, M. leprae.

Reproducible computational biology experiments with SED-ML--the Simulation Experiment Description Markup Language.

Science.gov (United States)

Waltemath, Dagmar; Adams, Richard; Bergmann, Frank T; Hucka, Michael; Kolpakov, Fedor; Miller, Andrew K; Moraru, Ion I; Nickerson, David; Sahle, Sven; Snoep, Jacky L; Le Novère, Nicolas

2011-12-15

The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research

Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

Science.gov (United States)

2011-01-01

Background The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. Results In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. Conclusions With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from

Control de la calidad y estudio de estabilidad del paracetamol gotas orales 100 mg/ml Quality control and stability study of 100 mg/ml paracetamol oral drops

Directory of Open Access Journals (Sweden)

Caridad M García Peña

2013-03-01

the non-steroidal anti-inflammatory drug group. Paracetamol oral drops are indicated for use in infant population aged up to 5 years to relieve fever, headache, toothache and symptomatic relief of common cold. Objective: to validate two analytical methods for the quality control and the stability study and to study the stability of 100 mg/ml Paracetamol oral drops made in Cuba. Methods: for quantification of the active principle in the final product in order to study stability, a chromatographic column equipment called Lichrosorb RP-18 was used for separation (5µm (250 x 4 mm, with ultraviolet ray detection at 243 nm and a mobile phase made up of distilled water: methanol (3:1 and the quantification of this principle against a reference sample by using the external standard method. For the quality control, the spectrophotometry used the spectrophotometer SPECTRONIC GENESYS 2, the ideal wavelength was 245 nm since it matches the maximum absorption rate and there is no interference with the excipients. As to the stability study of the drops, the on- shelf life method (temperature below 30 C was used, and for the accelerated stability analysis (40 ± 2ºC through high performance liquid chromatography. Results: the results of the evaluated parameters in the validation of the methods for the quality control and the stability study were within the set limits. The results of the stability study, both accelerated and on- shelf life and reservoir use, showed that the final product met the quality specifications during the study. Conclusions: the analytical methods based on UV spectrophotometry and high performance liquid chromatography are valid for the quality control and the stability study of 100 mg/ml Paracetamol oral drops, since they are linear, precise, accurate and specific. The physical, chemical and microbiological stability of the product was proved for 12 months at a temperature below 30ºC, packed in 15 ml amber glass reservoirs, 18mm opening and hydrolytic

An Analytical framework of social learning facilitated by participatory methods

NARCIS (Netherlands)

Scholz, G.; Dewulf, A.; Pahl-Wostl, C.

2014-01-01

Social learning among different stakeholders is often a goal in problem solving contexts such as environmental management. Participatory methods (e.g., group model-building and role playing games) are frequently assumed to stimulate social learning. Yet understanding if and why this assumption is

Potentials and limitations of low-concentration contrast medium (150 mg iodine/ml) in CT pulmonary angiography

International Nuclear Information System (INIS)

Radon, M.R.; Kaduthodil, M.J.; Jagdish, J.; Matthews, S.; Hill, C.; Bull, M.J.; Morcos, S.K.

2011-01-01

Aim: To assess the feasibility of producing diagnostic multidetector computed tomography (MDCT) pulmonary angiography with low iodine concentration contrast media (150 mg iodine/ml) in patients with suspected acute pulmonary embolism. Materials and methods: Ninety-five randomized patients underwent MDCT (64 row) pulmonary angiography with 100 ml iopromide either at low concentration (LC) of 150 mg iodine/ml (n = 45) or high concentration (HC) of 300 mg iodine/ml (n = 50), delivered at the rate of 5 ml/s via a power injector. Two experienced radiologists, blinded to the concentration used, subjectively assessed the diagnostic quality and confidence using a four-point scale [1 = poor (not diagnostic), 2 = satisfactory, 3 = good, 4 = excellent]. Attenuation values (in HU) were measured in the main proximal branches of the pulmonary arteries. Results: The median diagnostic quality score for both observers was 3.5 (interquartile range 3-4) in the HC group and 2.5 (interquartile range 1.5-3) in the LC group (p < 0.01). The median diagnostic confidence score for both observers was 4 (interquartile range 3-4) in the HC group and 3 (interquartile range 1.5-4) in the LC group (p < 0.01). Both observers rated examinations as diagnostic in 69% of cases in the LC group, compared with 96% of cases in the HC group. Good interobserver agreement was found in both groups (K value 0.72 in the LC group and 0.73 in the HC). Obesity, poor scan timing, and dilution by venous return of non-opacified blood were the main reasons for a reduction in diagnostic quality of examinations in the LC group. Conclusion: Despite a 50% reduction of contrast medium dose in comparison to the standard technique, 150 mg iodine/ml can produce diagnostic MDCT pulmonary angiogram studies in the absence of obesity or high cardiac output and hyper-dynamic pulmonary circulation. Reducing the dose of contrast media would minimize the risk of contrast nephropathy in patients at risk of this complication

Desarrollo de la formulación de la suspensión oral de ibuprofeno 100 mg/5 mL para uso pediátrico Development of formulation for oral suspension Ibuprofen 100 mg/5mL for pediatric use

Directory of Open Access Journals (Sweden)

Nilia de la Paz Martín-Viaña

2009-08-01

Full Text Available Se empleó el método de prueba y error para el desarrollo de la formulación de la suspensión oral de ibuprofeno 100 mg/5 mL para uso pediátrico; se estudió su estabilidad químico-física por el método acelerado y de vida de estante; se envasó en frasco de vidrio ámbar por 120 mL y se almacenó a temperatura ambiente. Se realizó el estudio reológico y la determinación de la viscosidad aparente, además, se efectuó el estudio microbiológico a través de la prueba de efectividad de preservativo antimicrobiano y el conteo microbiano; se comprobó la seguridad del producto mediante el estudio toxicológico. Todos los resultados cumplieron con los límites de calidad establecidos en la literatura científica, USP 30, para este tipo de forma farmacéutica. Se concluye que el medicamento desarrollado está correctamente formulado, desde el punto de vista galénico con un tiempo de vida útil de 24 meses bajo las condiciones estudiadas.Authors used the test and error method to develop the formulation of Ibuprofen oral suspension (100 mg/5 mL for pediatric use. Its chemical-physic stability was studied through accelerated method and shelf life. It was bottled in amber glass small bottles by 120 mL, and it was stored at room temperature. A rheology study and assessment of apparent viscosity was made as well as a microbiologic one by test of effectiveness of antimicrobial preservative and the microbial count. Product safe was verified by toxicology study. All results fulfilled quality limits established in scientific literature, USP 30, for this type of pharmaceutical method. We conclude that drug developed is correctly formulated, from the doctoral point of view with a time of useful life of 24 months under study conditions.

The ATLAS Higgs machine learning challenge

CERN Document Server

Davey, W; The ATLAS collaboration; Rousseau, D; Cowan, G; Kegl, B; Germain-Renaud, C; Guyon, I

2014-01-01

High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 90's with Artificial Neural Net for example, more recently with Boosted Decision Trees, Random Forest etc... Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, Data Scientists are developing new Machine Learning algorithms to extract sense from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, data scientists have advanced algorithms: the goal of the HiggsML project is to bring the two together by a “challenge”: participants from all over the world and any scientific background can compete online ( https://www.kaggle.com/c/higgs-boson ) to obtain the best Higgs to tau tau signal significance on a set of ATLAS full simulated Monte Carlo signal and background. Winners with the best scores will receive money prizes ; authors of the best method (most usable) will be invited t...

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures.

Science.gov (United States)

Rock, Adam J; Coventry, William L; Morgan, Methuen I; Loi, Natasha M

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

Teaching Research Methods and Statistics in eLearning Environments: Pedagogy, Practical Examples, and Possible Futures

Science.gov (United States)

Rock, Adam J.; Coventry, William L.; Morgan, Methuen I.; Loi, Natasha M.

2016-01-01

Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal et al., 1997). Given the ubiquitous and distributed nature of eLearning systems (Nof et al., 2015), teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology. PMID:27014147

Teaching Research Methods and Statistics in eLearning Environments:Pedagogy, Practical Examples and Possible Futures

Directory of Open Access Journals (Sweden)

Adam John Rock

2016-03-01

Full Text Available Generally, academic psychologists are mindful of the fact that, for many students, the study of research methods and statistics is anxiety provoking (Gal, Ginsburg, & Schau, 1997. Given the ubiquitous and distributed nature of eLearning systems (Nof, Ceroni, Jeong, & Moghaddam, 2015, teachers of research methods and statistics need to cultivate an understanding of how to effectively use eLearning tools to inspire psychology students to learn. Consequently, the aim of the present paper is to discuss critically how using eLearning systems might engage psychology students in research methods and statistics. First, we critically appraise definitions of eLearning. Second, we examine numerous important pedagogical principles associated with effectively teaching research methods and statistics using eLearning systems. Subsequently, we provide practical examples of our own eLearning-based class activities designed to engage psychology students to learn statistical concepts such as Factor Analysis and Discriminant Function Analysis. Finally, we discuss general trends in eLearning and possible futures that are pertinent to teachers of research methods and statistics in psychology.

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Directory of Open Access Journals (Sweden)

Zekić-Sušac Marijana

2014-09-01

Full Text Available Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology.

Science.gov (United States)

Wiens, Jenna; Shenoy, Erica S

2018-01-06

The increasing availability of electronic health data presents a major opportunity in healthcare for both discovery and practical applications to improve healthcare. However, for healthcare epidemiologists to best use these data, computational techniques that can handle large complex datasets are required. Machine learning (ML), the study of tools and methods for identifying patterns in data, can help. The appropriate application of ML to these data promises to transform patient risk stratification broadly in the field of medicine and especially in infectious diseases. This, in turn, could lead to targeted interventions that reduce the spread of healthcare-associated pathogens. In this review, we begin with an introduction to the basics of ML. We then move on to discuss how ML can transform healthcare epidemiology, providing examples of successful applications. Finally, we present special considerations for those healthcare epidemiologists who want to use and apply ML. © The Author(s) 2017. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.

Strategic Management: An Evaluation of the Use of Three Learning Methods.

Science.gov (United States)

Jennings, David

2002-01-01

A study of 46 management students compared three methods for learning strategic management: cases, simulation, and action learning through consulting projects. Simulation was superior to action learning on all outcomes and equal or superior to cases on two. Simulation gave students a central role in management and greater control of the learning…

Machine learning versus knowledge based classification of legal texts

NARCIS (Netherlands)

de Maat, E.; Krabben, K.; Winkels, R.; Winkels, R.G.F.

2010-01-01

This paper presents results of an experiment in which we used machine learning (ML) techniques to classify sentences in Dutch legislation. These results are compared to the results of a pattern-based classifier. Overall, the ML classifier performs as accurate (>90%) as the pattern based one, but

Frank Gilbreth and health care delivery method study driven learning.

Science.gov (United States)

Towill, Denis R

2009-01-01

The purpose of this article is to look at method study, as devised by the Gilbreths at the beginning of the twentieth century, which found early application in hospital quality assurance and surgical "best practice". It has since become a core activity in all modern methods, as applied to healthcare delivery improvement programmes. The article traces the origin of what is now currently and variously called "business process re-engineering", "business process improvement" and "lean healthcare" etc., by different management gurus back to the century-old pioneering work of Frank Gilbreth. The outcome is a consistent framework involving "width", "length" and "depth" dimensions within which healthcare delivery systems can be analysed, designed and successfully implemented to achieve better and more consistent performance. Healthcare method (saving time plus saving motion) study is best practised as co-joint action learning activity "owned" by all "players" involved in the re-engineering process. However, although process mapping is a key step forward, in itself it is no guarantee of effective re-engineering. It is not even the beginning of the end of the change challenge, although it should be the end of the beginning. What is needed is innovative exploitation of method study within a healthcare organisational learning culture accelerated via the Gilbreth Knowledge Flywheel. It is shown that effective healthcare delivery pipeline improvement is anchored into a team approach involving all "players" in the system especially physicians. A comprehensive process study, constructive dialogue, proper and highly professional re-engineering plus managed implementation are essential components. Experience suggests "learning" is thereby achieved via "natural groups" actively involved in healthcare processes. The article provides a proven method for exploiting Gilbreths' outputs and their many successors in enabling more productive evidence-based healthcare delivery as summarised

Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era.

Science.gov (United States)

Jing, Yankang; Bian, Yuemin; Hu, Ziheng; Wang, Lirong; Xie, Xiang-Qun Sean

2018-03-30

Over the last decade, deep learning (DL) methods have been extremely successful and widely used to develop artificial intelligence (AI) in almost every domain, especially after it achieved its proud record on computational Go. Compared to traditional machine learning (ML) algorithms, DL methods still have a long way to go to achieve recognition in small molecular drug discovery and development. And there is still lots of work to do for the popularization and application of DL for research purpose, e.g., for small molecule drug research and development. In this review, we mainly discussed several most powerful and mainstream architectures, including the convolutional neural network (CNN), recurrent neural network (RNN), and deep auto-encoder networks (DAENs), for supervised learning and nonsupervised learning; summarized most of the representative applications in small molecule drug design; and briefly introduced how DL methods were used in those applications. The discussion for the pros and cons of DL methods as well as the main challenges we need to tackle were also emphasized.

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

Directory of Open Access Journals (Sweden)

Hongyang Lu

2016-01-01

Full Text Available Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV approach and adaptive dictionary learning (DL. In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

Future Competencies and Learning Methods in Engineering Education

DEFF Research Database (Denmark)

Kolmos, Anette

2002-01-01

What are the competencies for tommorow´s enginnering education and the implications of these regarding the choice of teaching content and learning methods? The paper analyses two trends: the traditional and the techo-science approach. These two trends are based on technological innovation...... and change processes and impact on educational content and methods....

NASA Engineering and Safety Center (NESC) Enhanced Melamine (ML) Foam Acoustic Test (NEMFAT)

Science.gov (United States)

McNelis, Anne M.; Hughes, William O.; McNelis, Mark E.

2014-01-01

The NASA Engineering and Safety Center (NESC) funded a proposal to achieve initial basic acoustic characterization of ML (melamine) foam, which could serve as a starting point for a future, more comprehensive acoustic test program for ML foam. A project plan was developed and implemented to obtain acoustic test data for both normal and enhanced ML foam. This project became known as the NESC Enhanced Melamine Foam Acoustic Test (NEMFAT). This document contains the outcome of the NEMFAT project.

Preferred Methods of Learning for Nursing Students in an On-Line Degree Program.

Science.gov (United States)

Hampton, Debra; Pearce, Patricia F; Moser, Debra K

Investigators have demonstrated that on-line courses result in effective learning outcomes, but limited information has been published related to preferred teaching strategies. Delivery of on-line courses requires various teaching methods to facilitate interaction between students, content, and technology. The purposes of this study were to understand student teaching/learning preferences in on-line courses to include (a) differences in preferred teaching/learning methods for on-line nursing students across generations and (b) which teaching strategies students found to be most engaging and effective. Participants were recruited from 2 accredited, private school nursing programs (N=944) that admit students from across the United States and deliver courses on-line. Participants provided implied consent, and 217 (23%) students completed the on-line survey. Thirty-two percent of the students were from the Baby Boomer generation (1946-1964), 48% from Generation X (1965-1980), and 20% from the Millennial Generation (born after 1980). The preferred teaching/learning methods for students were videos or narrated PowerPoint presentations, followed by synchronous Adobe Connect educations sessions, assigned journal article reading, and e-mail dialog with the instructor. The top 2 methods identified by participants as the most energizing/engaging and most effective for learning were videos or narrated PowerPoint presentations and case studies. The teaching/learning method least preferred by participants and that was the least energizing/engaging was group collaborative projects with other students; the method that was the least effective for learning was wikis. Baby Boomers and Generation X participants had a significantly greater preference for discussion board (PBaby Boomer and Generation X students and rated on-line games as significantly more energizing/engaging and more effective for learning (PBaby Boomer and Generation X students. In conclusion, the results of this

Measuring the learning effectiveness of Web-based teacher professional development in the hypothesis based learning method of teaching science

Science.gov (United States)

Wilson, Penne L.

2007-12-01

This study was conducted as part of the five year evaluation of the Star Schools grant awarded to Oklahoma State University for the development on online teacher professional development in the Hypothesis Based Learning (HbL) method of science instruction. Participants in this research were five teachers who had completed the online workshop, submitted a lesson plan, and who allowed this researcher and other members of the University of New Mexico Evaluation Team into their classrooms to observe and to determine if the learning of the method from the online HbL workshop had translated into practice. These teachers worked in inner city, suburban, metropolitan, and rural communities in the U.S. Southwest. This study was conducted to determine if teachers learned the HbL method from the online HbL workshop, to examine the relationship of satisfaction to learning, and to determine the elements of the online workshop that led to teacher learning. To measure learning of HbL, three different assessment instruments were used: embedded assessments within the online HbL workshop that gave teachers a scenario and asked them to generate questions to facilitate the HbL process; the analysis of a lesson plan provided by teachers using a science concept that they wished to incorporate in their curriculum using an HbL lesson template provided at the HbL website; and, observations of teachers facilitating the HbL process conducted at three different times during the year that they began the HbL online workshop. To determine if teachers were satisfied with the learning environment, the online HbL workshop, and the product, HbL Method for Teaching Science, and to determine if teachers could identify the elements of the online workshop that led to learning, interviews with the participants were conducted. The research findings were presented in two parts: Part I is an analysis of data provided by the assessment instruments and a content analysis of the transcripts of the teacher

An introduction and overview of machine learning in neurosurgical care.

Science.gov (United States)

Senders, Joeky T; Zaki, Mark M; Karhade, Aditya V; Chang, Bliss; Gormley, William B; Broekman, Marike L; Smith, Timothy R; Arnaout, Omar

2018-01-01

Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from large complex datasets without being explicitly programmed. Although ML is already widely manifest in our daily lives in various forms, the considerable potential of ML has yet to find its way into mainstream medical research and day-to-day clinical care. The complex diagnostic and therapeutic modalities used in neurosurgery provide a vast amount of data that is ideally suited for ML models. This systematic review explores ML's potential to assist and improve neurosurgical care. A systematic literature search was performed in the PubMed and Embase databases to identify all potentially relevant studies up to January 1, 2017. All studies were included that evaluated ML models assisting neurosurgical treatment. Of the 6,402 citations identified, 221 studies were selected after subsequent title/abstract and full-text screening. In these studies, ML was used to assist surgical treatment of patients with epilepsy, brain tumors, spinal lesions, neurovascular pathology, Parkinson's disease, traumatic brain injury, and hydrocephalus. Across multiple paradigms, ML was found to be a valuable tool for presurgical planning, intraoperative guidance, neurophysiological monitoring, and neurosurgical outcome prediction. ML has started to find applications aimed at improving neurosurgical care by increasing the efficiency and precision of perioperative decision-making. A thorough validation of specific ML models is essential before implementation in clinical neurosurgical care. To bridge the gap between research and clinical care, practical and ethical issues should be considered parallel to the development of these techniques.

Propofol can Protect Against the Impairment of Learning-memory Induced by Electroconvulsive Shock via Tau Protein Hyperphosphorylation in Depressed Rats

Institute of Scientific and Technical Information of China (English)

Wan-fu Liu; Chao Liu

2015-01-01

Objective To explore the possible neurophysiologic mechanisms of propofol and N-methyl-D-aspartate (NMDA) receptor antagonist against learning-memory impairment of depressed rats without olfactory bulbs. Methods Models of depressed rats without olfactory bulbs were established. For the factorial design in analysis of variance, two intervention factors were included: electroconvulsive shock groups (with and without a course of electroconvulsive shock) and drug intervention groups [intraperotoneal (ip) injection of saline, NMDA receptor antagonist MK-801 and propofol. A total of 60 adult depressed rats without olfactory bulbs were randomly divided into 6 experimental groups (n=10 per group):ip injection of 5 ml saline;ip injection of 5 ml of 10 mg/kg MK-801;ip injection of 5 ml of 10 mg/kg MK-801 and a course of electroconvulsive shock;ip injection of 5 ml of 200 mg/kg propofol;ip injection of 5 ml of 200 mg/kg propofol and a course of electroconvulsive shock;and ip injection of 5 ml saline and a course of electroconvulsive shock. The learning-memory abilities of the rats was evaluated by the Morris water maze test. The content of glutamic acid in the hippocampus was detected by high-performance liquid chromatography. The expressions of p-AT8Ser202 in the hippocampus were determined by Western blot analysis. Results Propofol, MK-801 or electroconvulsive shock alone induced learning-memory impairment in depressed rats, as proven by extended evasive latency time and shortened space probe time. Glutamic acid content in the hippocampus of depressed rats was significantly up-regulated by electroconvulsive shock and down-regulated by propofol, but MK-801 had no significant effect on glutamic acid content. Levels of phosphorylated Tau protein p-AT8Ser202 in the hippocampus was up-regulated by electroconvulsive shock but was reduced by propofol and MK-801 alone. Propofol prevented learning-memory impairment and reduced glutamic acid content and p-AT8Ser202 levels induced by

Suggestology as an Effective Language Learning Method.

Science.gov (United States)

MaCoy, Katherine W.

The methods used and the results obtained by means of the accelerated language learning techniques developed by Georgi Lozanov, Director of the Institute of Suggestology in Bulgaria, are discussed. The following topics are included: (1) discussion of hypermnesia, "super memory," and the reasons foreign languages were chosen for purposes…

CytometryML: a data standard which has been designed to interface with other standards

Science.gov (United States)

Leif, Robert C.

2007-02-01

Because of the differences in the requirements, needs, and past histories including existing standards of the creating organizations, a single encompassing cytology-pathology standard will not, in the near future, replace the multiple existing or under development standards. Except for DICOM and FCS, these standardization efforts are all based on XML. CytometryML is a collection of XML schemas, which are based on the Digital Imaging and Communications in Medicine (DICOM) and Flow Cytometry Standard (FCS) datatypes. The CytometryML schemas contain attributes that link them to the DICOM standard and FCS. Interoperability with DICOM has been facilitated by, wherever reasonable, limiting the difference between CytometryML and the previous standards to syntax. In order to permit the Resource Description Framework, RDF, to reference the CytometryML datatypes, id attributes have been added to many CytometryML elements. The Laboratory Digital Imaging Project (LDIP) Data Exchange Specification and the Flowcyt standards development effort employ RDF syntax. Documentation from DICOM has been reused in CytometryML. The unity of analytical cytology was demonstrated by deriving a microscope type and a flow cytometer type from a generic cytometry instrument type. The feasibility of incorporating the Flowcyt gating schemas into CytometryML has been demonstrated. CytometryML is being extended to include many of the new DICOM Working Group 26 datatypes, which describe patients, specimens, and analytes. In situations where multiple standards are being created, interoperability can be facilitated by employing datatypes based on a common set of semantics and building in links to standards that employ different syntax.

Modern Languages and Specific Learning Difficulties (SpLD): Implications of Teaching Adult Learners with Dyslexia in Distance Learning

Science.gov (United States)

Gallardo, Matilde; Heiser, Sarah; Arias McLaughlin, Ximena

2015-01-01

In modern language (ML) distance learning programmes, teachers and students use online tools to facilitate, reinforce and support independent learning. This makes it essential for teachers to develop pedagogical expertise in using online communication tools to perform their role. Teachers frequently raise questions of how best to support the needs…

A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.

Science.gov (United States)

Koo, Ching Lee; Liew, Mei Jing; Mohamad, Mohd Saberi; Salleh, Abdul Hakim Mohamed

2013-01-01

Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

Directory of Open Access Journals (Sweden)

Ching Lee Koo

2013-01-01

Full Text Available Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs, support vector machine (SVM, and random forests (RFs in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease.

A fast learning method for large scale and multi-class samples of SVM

Science.gov (United States)

Fan, Yu; Guo, Huiming

2017-06-01

A multi-class classification SVM(Support Vector Machine) fast learning method based on binary tree is presented to solve its low learning efficiency when SVM processing large scale multi-class samples. This paper adopts bottom-up method to set up binary tree hierarchy structure, according to achieved hierarchy structure, sub-classifier learns from corresponding samples of each node. During the learning, several class clusters are generated after the first clustering of the training samples. Firstly, central points are extracted from those class clusters which just have one type of samples. For those which have two types of samples, cluster numbers of their positive and negative samples are set respectively according to their mixture degree, secondary clustering undertaken afterwards, after which, central points are extracted from achieved sub-class clusters. By learning from the reduced samples formed by the integration of extracted central points above, sub-classifiers are obtained. Simulation experiment shows that, this fast learning method, which is based on multi-level clustering, can guarantee higher classification accuracy, greatly reduce sample numbers and effectively improve learning efficiency.

Implementing Adaptive Educational Methods with IMS Learning Design

NARCIS (Netherlands)

Specht, Marcus; Burgos, Daniel

2006-01-01

Please, cite this publication as: Specht, M. & Burgos, D. (2006). Implementing Adaptive Educational Methods with IMS Learning Design. Proceedings of Adaptive Hypermedia. June, Dublin, Ireland. Retrieved June 30th, 2006, from http://dspace.learningnetworks.org

Linked-Class Problem-Based Learning in Engineering: Method and Evaluation

Science.gov (United States)

Hunt, Emily M.; Lockwood-Cooke, Pamela; Kelley, Judy

2010-01-01

Problem-Based Learning (PBL) is a problem-centered teaching method with exciting potential in engineering education for motivating and enhancing student learning. Implementation of PBL in engineering education has the potential to bridge the gap between theory and practice. Two common problems are encountered when attempting to integrate PBL into…

Early Language Learning: Complexity and Mixed Methods

Science.gov (United States)

Enever, Janet, Ed.; Lindgren, Eva, Ed.

2017-01-01

This is the first collection of research studies to explore the potential for mixed methods to shed light on foreign or second language learning by young learners in instructed contexts. It brings together recent studies undertaken in Cameroon, China, Croatia, Ethiopia, France, Germany, Italy, Kenya, Mexico, Slovenia, Spain, Sweden, Tanzania and…

Survey compare team based learning and lecture teaching method, on learning-teaching process nursing student\\'s, in Surgical and Internal Diseases course

Directory of Open Access Journals (Sweden)

AA Vaezi

2015-12-01

Full Text Available Introduction: The effect of teaching methods on learning process of students will help teachers to improve the quality of teaching by selecting an appropriate method. This study aimed to compare the team- based learning and lecture teaching method on learning-teaching process of nursing students in surgical and internal diseases courses. Method: This quasi-experimental study was carried on the nursing students in the School of Nursing and Midwifery in Yazd and Meybod cities. Studied sample was all of the students in the sixth term in the Faculty of Nursing in Yazd (48 persons and the Faculty of Nursing in Meybod (28 persons. The rate of students' learning through lecture was measured using MCQ tests and teaching based on team-based learning (TBL method was run using MCQ tests (IRAT, GRAT, Appeals and Task group. Therefore, in order to examine the students' satisfaction about the TBL method, a 5-point Likert scale (translated questionnaire (1=completely disagree, 2= disagree, 3=not effective, 4=agree, and 5=completely agree consisted of 22 items was utilized. The reliability and validity of this translated questionnaire was measured. The collected data were analyzed through SPSS 17.0 using descriptive and analytical statistic. Result: The results showed that the mean scores in team-based learning were meaningful in individual assessment (17±84 and assessment group (17.2±1.17. The mean of overall scores in TBL method (17.84±0.98% was higher compared with the lecture teaching method (16±2.31. Most of the students believed that TBL method has improved their interpersonal and group interaction skills (100%. Among them, 97.7% of students mentioned that this method (TBL helped them to understand the course content better. The lowest levels of the satisfaction have related to the continuous learning during lifelong (51.2%. Conclusion: The results of the present study showed that the TBL method led to improving the communication skills, understanding

COOPERATIVE LEARNING IN DISTANCE LEARNING: A MIXED METHODS STUDY

Directory of Open Access Journals (Sweden)

Lori Kupczynski

2012-07-01

Full Text Available Distance learning has facilitated innovative means to include Cooperative Learning (CL in virtual settings. This study, conducted at a Hispanic-Serving Institution, compared the effectiveness of online CL strategies in discussion forums with traditional online forums. Quantitative and qualitative data were collected from 56 graduate student participants. Quantitative results revealed no significant difference on student success between CL and Traditional formats. The qualitative data revealed that students in the cooperative learning groups found more learning benefits than the Traditional group. The study will benefit instructors and students in distance learning to improve teaching and learning practices in a virtual classroom.

Interoperability between OPC UA and AutomationML

OpenAIRE

Henßen, Robert; Schleipen, Miriam

2014-01-01

OPC UA (OPC Unified Architecture) is a platform-independent standard series (IEC 62541) [1], [2] for communication of industrial automation devices and systems. The OPC Unified Architecture is an advanced communication technology for process control. Certainly the launching costs for the initial information model are quite high. AutomationML (Automation Markup Language) is an upcoming open standard series (IEC 62714) [3], [4] for describing production plants or plant components. The goal of t...

Validation by theoretical approach to the experimental estimation of efficiency for gamma spectrometry of gas in 100 ml standard flask

International Nuclear Information System (INIS)

Mohan, V.; Chudalayandi, K.; Sundaram, M.; Krishnamony, S.

1996-01-01

Estimation of gaseous activity forms an important component of air monitoring at Madras Atomic Power Station (MAPS). The gases of importance are argon 41 an air activation product and fission product noble gas xenon 133. For estimating the concentration, the experimental method is used in which a grab sample is collected in a 100 ml volumetric standard flask. The activity of gas is then computed by gamma spectrometry using a predetermined efficiency estimated experimentally. An attempt is made using theoretical approach to validate the experimental method of efficiency estimation. Two analytical models named relative flux model and absolute activity model were developed independently of each other. Attention is focussed on the efficiencies for 41 Ar and 133 Xe. Results show that the present method of sampling and analysis using 100 ml volumetric flask is adequate and acceptable. (author). 5 refs., 2 tabs

Using Problem Based Learning Methods from Engineering Education in Company Based Development

DEFF Research Database (Denmark)

Kofoed, Lise B.; Jørgensen, Frances

2007-01-01

This paper discusses how Problem-Based Learning (PBL) methods were used to support a Danish company in its efforts to become more of a 'learning organisation', characterized by sharing of knowledge and experiences. One of the central barriers to organisational learning in this company involved...

Non-linguistic learning in aphasia: Effects of training method and stimulus characteristics

Science.gov (United States)

Vallila-Rohter, Sofia; Kiran, Swathi

2013-01-01

Purpose The purpose of the current study was to explore non-linguistic learning ability in patients with aphasia, examining the impact of stimulus typicality and feedback on success with learning. Method Eighteen patients with aphasia and eight healthy controls participated in this study. All participants completed four computerized, non-linguistic category-learning tasks. We probed learning ability under two methods of instruction: feedback-based (FB) and paired-associate (PA). We also examined the impact of task complexity on learning ability, comparing two stimulus conditions: typical (Typ) and atypical (Atyp). Performance was compared between groups and across conditions. Results Results demonstrated that healthy controls were able to successfully learn categories under all conditions. For our patients with aphasia, two patterns of performance arose. One subgroup of patients was able to maintain learning across task manipulations and conditions. The other subgroup of patients demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions. Conclusions Results support the hypothesis that impairments of general learning are present in aphasia. Some patients demonstrated the ability to extract category information under complex training conditions, while others learned only under conditions that were simplified and emphasized salient category features. Overall, the typical training condition facilitated learning for all participants. Findings have implications for therapy, which are discussed. PMID:23695914

A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

Science.gov (United States)

Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

Joint Parametric Fault Diagnosis and State Estimation Using KF-ML Method

DEFF Research Database (Denmark)

Sun, Zhen; Yang, Zhenyu

2014-01-01

The paper proposes a new method for a kind of parametric fault online diagnosis with state estimation jointly. The considered fault affects not only the deterministic part of the system but also the random circumstance. The proposed method first applies Kalman Filter (KF) and Maximum Likelihood (...

A Fast Optimization Method for General Binary Code Learning.

Science.gov (United States)

Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

2016-09-22

Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

The enhanced callose deposition in barley with ml-o powdery mildew resistance genes

DEFF Research Database (Denmark)

Skou, Jens-Peder

1985-01-01

Carborundum treatment of barley leaves induced a callose deposition which was detected as diffuse blotches in the epidermal cells of susceptible barleys and as deeply stained tracks along the scratches in barleys with the ml-o powdery mildew resistance gene. Subsequent inoculation with powdery...... mildew resulted in appositions that enlarged inversely to their size in the respective varieties when inoculated without carborundum treatment. Aphids sucking the leaves resulted in rows of callose containing spots along the anticlinal cell walls. The spots were larger in the ml-o mutant than...... in the mother variety. Callose was deposited in connection with the pleiotropic necrotic spotting in barleys with the ml-o gene. Modification of the necrotic spotting by crossing the ml-o gene into other gene backgrounds did not result in any change in the size of appositions upon inoculation with powdery...

Influence Cooperative Learning Method and Personality Type to Ability to Write The Scientific Article (Experiment Study on SMAN 2 Students Ciamis Learning Indonesian Subject

Directory of Open Access Journals (Sweden)

Supriatna Supriatna

2017-10-01

Full Text Available The purpose of this research was to know the influence of cooperative learning method (Jigsaw and TPS and personality type (extrovert and introvert toward students’ ability in scientific writing at the SMA Negeri 2 Ciamis class XII. The research used experimental method with 2 x 2 factorial design. The population was the students of class XII which consisted of 150. The sample was 57 students. The results showed that: (1 The ability to write scientific articles of students learning by cooperative learning method jigsaw model (= 65,88 is higher than students who learn by cooperative technique method of TPS (= 59,88, (2 Ability writing scientific articles of students whose extroverted personality (= 65.69 is higher than introverted students (= 60.06; (3 there is interaction between cooperative learning method and personality type to score of writing ability of scientific article (4 ability to write scientific article of extrovert student and studying with technique of Jigsaw (= 77,75 higher than extrovert student learning with cooperative learning method model of TPS (= 53,63 to score of writing ability of scientific article, (5 ability to write introverted student's scientific article and get treatment of cooperative learning method of jigsaw model (= 54,00 lower than introverted student learning TPS technique = 66,13, (6 the ability to write extroverted students' scientific articles studied with jigsaw techniques, and introverted students who studied Jigsaw techniques (= 77.75 were higher than those with introverted personality types studied by the Jigsaw technique (= 54.00 , (7 Ability to write scientific articles of students learning by cooperative techniques of TPS technique and have extrovert personality type ( = 53.63 lower than introverted students learning TPS techniques (= 66.13.

Listening to Our Students: Understanding How They Learn Research Methods in Geography

Science.gov (United States)

Keenan, Kevin; Fontaine, Danielle

2012-01-01

How undergraduate students learn research methods in geography has been understudied. Existing work has focused on course description from the instructor's perspective. This study, however, uses a grounded theory approach to allow students' voices to shape a new theory of how they themselves say that they learn research methods. Data from two…

Keystone Method: A Learning Paradigm in Mathematics

Science.gov (United States)

Siadat, M. Vali; Musial, Paul M.; Sagher, Yoram

2008-01-01

This study reports the effects of an integrated instructional program (the Keystone Method) on the students' performance in mathematics and reading, and tracks students' persistence and retention. The subject of the study was a large group of students in remedial mathematics classes at the college, willing to learn but lacking basic educational…

Building Customer Churn Prediction Models in Fitness Industry with Machine Learning Methods

OpenAIRE

Shan, Min

2017-01-01

With the rapid growth of digital systems, churn management has become a major focus within customer relationship management in many industries. Ample research has been conducted for churn prediction in different industries with various machine learning methods. This thesis aims to combine feature selection and supervised machine learning methods for defining models of churn prediction and apply them on fitness industry. Forward selection is chosen as feature selection methods. Support Vector ...

The additional benefit of the ML Flow test to classify leprosy patients.

Science.gov (United States)

Bührer-Sékula, Samira; Illarramendi, Ximena; Teles, Rose B; Penna, Maria Lucia F; Nery, José Augusto C; Sales, Anna Maria; Oskam, Linda; Sampaio, Elizabeth P; Sarno, Euzenir N

2009-08-01

The use of the skin lesion counting classification leads to both under and over diagnosis of leprosy in many instances. Thus, there is a need to complement this classification with another simple and robust test for use in the field. Data of 202 untreated leprosy patients diagnosed at FIOCRUZ, Rio de Janeiro, Brazil, was analyzed. There were 90 patients classified as PB and 112 classified as MB according to the reference standard. The BI was positive in 111 (55%) patients and the ML Flow test in 116 (57.4%) patients. The ML Flow test was positive in 95 (86%) of the patients with a positive BI. The lesion counting classification was confirmed by both BI and ML Flow tests in 65% of the 92 patients with 5 or fewer lesions, and in 76% of the 110 patients with 6 or more lesions. The combination of skin lesion counting and the ML Flow test results yielded a sensitivity of 85% and a specificity of 87% for MB classification, and correctly classified 86% of the patients when compared to the standard reference. A considerable proportion of the patients (43.5%) with discordant test results in relation to standard classification was in reaction. The use of any classification system has limitations, especially those that oversimplify a complex disease such as leprosy. In the absence of an experienced dermatologist and slit skin smear, the ML Flow test could be used to improve treatment decisions in field conditions.

Machine learning for healthcare technologies

CERN Document Server

Clifton, David A

2016-01-01

This book brings together chapters on the state-of-the-art in machine learning (ML) as it applies to the development of patient-centred technologies, with a special emphasis on 'big data' and mobile data.

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

Science.gov (United States)

Senders, Joeky T; Staples, Patrick C; Karhade, Aditya V; Zaki, Mark M; Gormley, William B; Broekman, Marike L D; Smith, Timothy R; Arnaout, Omar

2018-01-01

Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data. In this systematic review, we evaluate the potential of ML for neurosurgical outcome prediction. A systematic search in the PubMed and Embase databases was performed to identify all potential relevant studies up to January 1, 2017. Thirty studies were identified that evaluated ML algorithms used as prediction models for survival, recurrence, symptom improvement, and adverse events in patients undergoing surgery for epilepsy, brain tumor, spinal lesions, neurovascular disease, movement disorders, traumatic brain injury, and hydrocephalus. Depending on the specific prediction task evaluated and the type of input features included, ML models predicted outcomes after neurosurgery with a median accuracy and area under the receiver operating curve of 94.5% and 0.83, respectively. Compared with logistic regression, ML models performed significantly better and showed a median absolute improvement in accuracy and area under the receiver operating curve of 15% and 0.06, respectively. Some studies also demonstrated a better performance in ML models compared with established prognostic indices and clinical experts. In the research setting, ML has been studied extensively, demonstrating an excellent performance in outcome prediction for a wide range of neurosurgical conditions. However, future studies should investigate how ML can be implemented as a practical tool supporting neurosurgical care. Copyright © 2017 Elsevier Inc. All rights reserved.

The Bonding of Pa to d8-ML3 Complexes

OpenAIRE

Kang, Sung-Kwon; Albright, Thomas A.; Silvestre, Jerome

1985-01-01

Extended Hiickel calculmions were carried out on 171, 'f/ 2, and 'f/3 complexes of P4 to Rh(PH3)2Cl. The 'f/ 1-square planar and an 'f/2 complex with C2v symmetry are the most stable. Geometrical optimizations and a detailed account of the bonding in each have been carried out. d10 'f/1-tetrahedral complexes of P4 are expected to be quite stable. The best candidate for an 'f/3 mode of bonding is the trimer Fe3(C0)9. Alternative complexes at 'f/3 include a d6-ML3 and d4-ML...

Perception of mathematics teachers on cooperative learning method in the 21st century

Science.gov (United States)

Taufik, Nurshahira Alwani Mohd; Maat, Siti Mistima

2017-05-01

Mathematics education is one of the branches to be mastered by students to help them compete with the upcoming challenges that are very challenging. As such, all parties should work together to help increase student achievement in Mathematics education in line with the Malaysian Education Blueprint (MEB) 2010-2025. Teaching methods play a very important role in attracting and fostering student understanding and interested in learning Mathematics. Therefore, this study was conducted to identify the perceptions of teachers in carrying out cooperative methods in the teaching and learning of mathematics. Participants of this study involving 4 teachers who teach Mathematics in primary schools around the state of Negeri Sembilan. Interviews are used as a method for gathering data. The findings indicate that cooperative methods help increasing interest and understanding in the teaching and learning of mathematics. In conclusion, the teaching methods affect the interest and understanding of students in the learning of Mathematics in the classroom.

Simple ML Detector for Multiple Antennas Communication System

Directory of Open Access Journals (Sweden)

Ahmad Taqwa

2010-10-01

Full Text Available In order to support providing broadband wireless communication services against limited and expensive frequency bandwidth, we have to develop a bandwidth efficient system. Therefore, in this paper we propose a closed-loop MIMO (Multiple-Input-Multiple-Output system using ML (Maximum Likelihood detector to optimize capacity and to increase system performance. What is especially exciting about the benefits offered by MIMO is that a high capacity and performance can be attained without additional frequency-spectral resource. The grand scenario of this concept is the attained advantages of transformation matrices having capability to allocate transmitted signals power suit to the channel. Furthermore, product of these matrices forms parallel singular channels. Due to zero inter-channels correlation, thus we can design ML detector to increase the system performance. Finally, computer simulations validates that at 0 dB SNR our system can reach optimal capacity up to 1 bps/Hz and SER up to 0.2 higher than opened-loop MIMO.

Implementation of Simulation Based-Concept Attainment Method to Increase Interest Learning of Engineering Mechanics Topic

Science.gov (United States)

Sultan, A. Z.; Hamzah, N.; Rusdi, M.

2018-01-01

The implementation of concept attainment method based on simulation was used to increase student’s interest in the subjects Engineering of Mechanics in second semester of academic year 2016/2017 in Manufacturing Engineering Program, Department of Mechanical PNUP. The result of the implementation of this learning method shows that there is an increase in the students’ learning interest towards the lecture material which is summarized in the form of interactive simulation CDs and teaching materials in the form of printed books and electronic books. From the implementation of achievement method of this simulation based concept, it is noted that the increase of student participation in the presentation and discussion as well as the deposit of individual assignment of significant student. With the implementation of this method of learning the average student participation reached 89%, which before the application of this learning method only reaches an average of 76%. And also with previous learning method, for exam achievement of A-grade under 5% and D-grade above 8%. After the implementation of the new learning method (simulation based-concept attainment method) the achievement of Agrade has reached more than 30% and D-grade below 1%.

A Pharmacy Preregistration Course Using Online Teaching and Learning Methods

Science.gov (United States)

McDowell, Jenny; Marriott, Jennifer L.; Calandra, Angela; Duncan, Gregory

2009-01-01

Objective To design and evaluate a preregistration course utilizing asynchronous online learning as the primary distance education delivery method. Design Online course components including tutorials, quizzes, and moderated small-group asynchronous case-based discussions were implemented. Online delivery was supplemented with self-directed and face-to-face learning. Assessment Pharmacy graduates who had completed the course in 2004 and 2005 were surveyed. The majority felt they had benefited from all components of the course, and that online delivery provided benefits including increased peer support, shared learning, and immediate feedback on performance. A majority of the first cohort reported that the workload associated with asynchronous online discussions was too great. The course was altered in 2005 to reduce the online component. Participant satisfaction improved, and most felt that the balance of online to face-to-face delivery was appropriate. Conclusion A new pharmacy preregistration course was successfully implemented. Online teaching and learning was well accepted and appeared to deliver benefits over traditional distance education methods once workload issues were addressed. PMID:19777092

A Photometric Machine-Learning Method to Infer Stellar Metallicity

Science.gov (United States)

Miller, Adam A.

2015-01-01

Following its formation, a star's metal content is one of the few factors that can significantly alter its evolution. Measurements of stellar metallicity ([Fe/H]) typically require a spectrum, but spectroscopic surveys are limited to a few x 10(exp 6) targets; photometric surveys, on the other hand, have detected > 10(exp 9) stars. I present a new machine-learning method to predict [Fe/H] from photometric colors measured by the Sloan Digital Sky Survey (SDSS). The training set consists of approx. 120,000 stars with SDSS photometry and reliable [Fe/H] measurements from the SEGUE Stellar Parameters Pipeline (SSPP). For bright stars (g' learning method is similar to the scatter in [Fe/H] measurements from low-resolution spectra..

DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

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Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

2015-12-01

Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

Arabic Supervised Learning Method Using N-Gram

Science.gov (United States)

Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun

2008-01-01

Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…

SED-ED, a workflow editor for computational biology experiments written in SED-ML.

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Adams, Richard R

2012-04-15

The simulation experiment description markup language (SED-ML) is a new community data standard to encode computational biology experiments in a computer-readable XML format. Its widespread adoption will require the development of software support to work with SED-ML files. Here, we describe a software tool, SED-ED, to view, edit, validate and annotate SED-ML documents while shielding end-users from the underlying XML representation. SED-ED supports modellers who wish to create, understand and further develop a simulation description provided in SED-ML format. SED-ED is available as a standalone Java application, as an Eclipse plug-in and as an SBSI (www.sbsi.ed.ac.uk) plug-in, all under an MIT open-source license. Source code is at https://sed-ed-sedmleditor.googlecode.com/svn. The application itself is available from https://sourceforge.net/projects/jlibsedml/files/SED-ED/.

Density Functional Calculation of the 0.5ML-Terminated Allyl Mercaptan/Si(100)-(2 × 1) Surface

International Nuclear Information System (INIS)

Chun-Mei, Tang; Kai-Ming, Deng; Xuan, Chen; Chuan-Yun, Xiao; Yu-Zhen, Liu; Qun-Xiang, Li

2009-01-01

The structural and electronic properties of the 0.5 ML-terminated allyl mercaptan (ALM)/Si(100)-(2 × 1) surface are studied using the density functional method. The calculated absorption energy of the ALM molecule on the 0.5 ML-terminated ALM/Si(100)-(2 × 1) surface is 3.36 eV, implying that adsorption is strongly favorable. The electronic structure calculations show that the ALM/Si(100)-(2 × 1), the clean Si(100)-(2 × 1), and the fully-terminated H/Si(100)-(2 × 1) surfaces have the nature of an indirect band gap semiconductor. The highest occupied molecular orbital is dominated by the ALM, confirming the mechanism proposed by Hossain for its chain reaction. (condensed matter: structure, mechanical and thermal properties)

Evaluation Methods on Usability of M-Learning Environments

Directory of Open Access Journals (Sweden)

Teresa Magal-Royo

2007-10-01

Full Text Available Nowadays there are different evaluation methods focused in the assessment of the usability of telematic methods. The assessment of 3rd generation web environments evaluates the effectiveness and usability of application with regard to the user needs. Wireless usability and, specifically in mobile phones, is concentrated in the validation of the features and tools management using conventional interactive environments. There is not a specific and suitable criterion to evaluate created environments and m-learning platforms, where the restricted and sequential representation is a fundamental aspect to be considered.The present paper exposes the importance of the conventional usability methods to verify both: the employed contents in wireless formats, and the possible interfaces from the conception phases, to the validations of the platform with such characteristics.The development of usability adapted inspection could be complemented with the Remote’s techniques of usability testing, which are being carried out these days in the mobile devices field and which pointed out the need to apply common criteria in the validation of non-located learning scenarios.

WebMail versus WebApp: Comparing Problem-Based Learning Methods in a Business Research Methods Course

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Williams van Rooij, Shahron

2007-01-01

This study examined the impact of two Problem-Based Learning (PBL) approaches on knowledge transfer, problem-solving self-efficacy, and perceived learning gains among four intact classes of adult learners engaged in a group project in an online undergraduate business research methods course. With two of the classes using a text-only PBL workbook…

GeoSciML v3.0 - a significant upgrade of the CGI-IUGS geoscience data model

Science.gov (United States)

Raymond, O.; Duclaux, G.; Boisvert, E.; Cipolloni, C.; Cox, S.; Laxton, J.; Letourneau, F.; Richard, S.; Ritchie, A.; Sen, M.; Serrano, J.-J.; Simons, B.; Vuollo, J.

2012-04-01

GeoSciML version 3.0 (http://www.geosciml.org), released in late 2011, is the latest version of the CGI-IUGS* Interoperability Working Group geoscience data interchange standard. The new version is a significant upgrade and refactoring of GeoSciML v2 which was released in 2008. GeoSciML v3 has already been adopted by several major international interoperability initiatives, including OneGeology, the EU INSPIRE program, and the US Geoscience Information Network, as their standard data exchange format for geoscience data. GeoSciML v3 makes use of recently upgraded versions of several Open Geospatial Consortium (OGC) and ISO data transfer standards, including GML v3.2, SWE Common v2.0, and Observations and Measurements v2 (ISO 19156). The GeoSciML v3 data model has been refactored from a single large application schema with many packages, into a number of smaller, but related, application schema modules with individual namespaces. This refactoring allows the use and future development of modules of GeoSciML (eg; GeologicUnit, GeologicStructure, GeologicAge, Borehole) in smaller, more manageable units. As a result of this refactoring and the integration with new OGC and ISO standards, GeoSciML v3 is not backwardly compatible with previous GeoSciML versions. The scope of GeoSciML has been extended in version 3.0 to include new models for geomorphological data (a Geomorphology application schema), and for geological specimens, geochronological interpretations, and metadata for geochemical and geochronological analyses (a LaboratoryAnalysis-Specimen application schema). In addition, there is better support for borehole data, and the PhysicalProperties model now supports a wider range of petrophysical measurements. The previously used CGI_Value data type has been superseded in favour of externally governed data types provided by OGC's SWE Common v2 and GML v3.2 data standards. The GeoSciML v3 release includes worked examples of best practice in delivering geochemical

Research progress in machine learning methods for gene-gene interaction detection.

Science.gov (United States)

Peng, Zhe-Ye; Tang, Zi-Jun; Xie, Min-Zhu

2018-03-20

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.