Laboratory research irradiators with enhanced security features

International Nuclear Information System (INIS)

Srivastava, Piyush

2014-01-01

Over the years BRIT has developed state of art technology for laboratory research irradiators which are suited most for carrying out research and development works in the fields of radiation processing. These equipment which house radioactive sources up to 14 kCi are having a number of features to meet users requirements. They are manufactured as per the national and International standards of safety codes. The paper deals with design, development and application aspects of laboratory research irradiator called Gamma Chamber and also the new security features planned for incorporation in the equipment. Equipment are being regularly manufactured, supplied and installed by BRIT in India and Abroad. There are a number of such equipment in use at different institutions and are found to be very useful. (author)

Dynamical Downscaling of NASA/GISS ModelE: Continuous, Multi-Year WRF Simulations

Science.gov (United States)

Otte, T.; Bowden, J. H.; Nolte, C. G.; Otte, M. J.; Herwehe, J. A.; Faluvegi, G.; Shindell, D. T.

2010-12-01

The WRF Model is being used at the U.S. EPA for dynamical downscaling of the NASA/GISS ModelE fields to assess regional impacts of climate change in the United States. The WRF model has been successfully linked to the ModelE fields in their raw hybrid vertical coordinate, and continuous, multi-year WRF downscaling simulations have been performed. WRF will be used to downscale decadal time slices of ModelE for recent past, current, and future climate as the simulations being conducted for the IPCC Fifth Assessment Report become available. This presentation will focus on the sensitivity to interior nudging within the RCM. The use of interior nudging for downscaled regional climate simulations has been somewhat controversial over the past several years but has been recently attracting attention. Several recent studies that have used reanalysis (i.e., verifiable) fields as a proxy for GCM input have shown that interior nudging can be beneficial toward achieving the desired downscaled fields. In this study, the value of nudging will be shown using fields from ModelE that are downscaled using WRF. Several different methods of nudging are explored, and it will be shown that the method of nudging and the choices made with respect to how nudging is used in WRF are critical to balance the constraint of ModelE against the freedom of WRF to develop its own fields.

Technical and safe development features of modern research reactor

International Nuclear Information System (INIS)

Wang Jiaying; Dong Duo

1998-01-01

The development trend of research reactor in the world, and development situation in China are introduced. Up to now, some research reactors have serviced for long time and equipment have aged, not to be satisfied for requirement of science and technology development. New research reactors must been developed. The technical features and safe features of new type research reactor in China, for example: multi-pile utilization, compact core of high flux, high automation level of control, reactor two independent shutdown systems, great coefficient of negative temperature, passive safety systems, reliable residual heat removal system are studied

Research potential and cognitive features of students.

Directory of Open Access Journals (Sweden)

Bordovskaia N.V.

2014-12-01

Full Text Available This article examines the theoretical and methodological justifications for studying students’ research potential. It presents proof of the isomorphic nature of human research activity and research potential as well as of the fluid nature of its development: from research-like behavior to science-based research activity. It defines three functional components (motivational, cognitive, and behavioral that form the structure of research potential. It further presents the results of empirically studying the cognitive features of master’s students possessing different levels of research potential. It provides data on the dynamics of research-potential components at different educational levels (bachelor’s and master’s programs. Special attention is given to a comparative analysis of evaluations by research tutors regarding their students’ research potential and of the indicators obtained using psychodiagnostic methods.

Issues and special features of animal health research

Directory of Open Access Journals (Sweden)

Ducrot Christian

2011-08-01

Full Text Available Abstract In the rapidly changing context of research on animal health, INRA launched a collective discussion on the challenges facing the field, its distinguishing features, and synergies with biomedical research. As has been declared forcibly by the heads of WHO, FAO and OIE, the challenges facing animal health, beyond diseases transmissible to humans, are critically important and involve food security, agriculture economics, and the ensemble of economic activities associated with agriculture. There are in addition issues related to public health (zoonoses, xenobiotics, antimicrobial resistance, the environment, and animal welfare. Animal health research is distinguished by particular methodologies and scientific questions that stem from the specific biological features of domestic species and from animal husbandry practices. It generally does not explore the same scientific questions as research on human biology, even when the same pathogens are being studied, and the discipline is rooted in a very specific agricultural and economic context. Generic and methodological synergies nevertheless exist with biomedical research, particularly with regard to tools and biological models. Certain domestic species furthermore present more functional similarities with humans than laboratory rodents. The singularity of animal health research in relation to biomedical research should be taken into account in the organization, evaluation, and funding of the field through a policy that clearly recognizes the specific issues at stake. At the same time, the One Health approach should facilitate closer collaboration between biomedical and animal health research at the level of research teams and programmes.

Issues and special features of animal health research.

Science.gov (United States)

Ducrot, Christian; Bed'hom, Bertrand; Béringue, Vincent; Coulon, Jean-Baptiste; Fourichon, Christine; Guérin, Jean-Luc; Krebs, Stéphane; Rainard, Pascal; Schwartz-Cornil, Isabelle; Torny, Didier; Vayssier-Taussat, Muriel; Zientara, Stephan; Zundel, Etienne; Pineau, Thierry

2011-08-24

In the rapidly changing context of research on animal health, INRA launched a collective discussion on the challenges facing the field, its distinguishing features, and synergies with biomedical research. As has been declared forcibly by the heads of WHO, FAO and OIE, the challenges facing animal health, beyond diseases transmissible to humans, are critically important and involve food security, agriculture economics, and the ensemble of economic activities associated with agriculture. There are in addition issues related to public health (zoonoses, xenobiotics, antimicrobial resistance), the environment, and animal welfare.Animal health research is distinguished by particular methodologies and scientific questions that stem from the specific biological features of domestic species and from animal husbandry practices. It generally does not explore the same scientific questions as research on human biology, even when the same pathogens are being studied, and the discipline is rooted in a very specific agricultural and economic context.Generic and methodological synergies nevertheless exist with biomedical research, particularly with regard to tools and biological models. Certain domestic species furthermore present more functional similarities with humans than laboratory rodents.The singularity of animal health research in relation to biomedical research should be taken into account in the organization, evaluation, and funding of the field through a policy that clearly recognizes the specific issues at stake. At the same time, the One Health approach should facilitate closer collaboration between biomedical and animal health research at the level of research teams and programmes.

Improved Upper Ocean/Sea Ice Modeling in the GISS GCM for Investigating Climate Change

Science.gov (United States)

1998-01-01

This project built on our previous results in which we highlighted the importance of sea ice in overall climate sensitivity by determining that for both warming and cooling climates, when sea ice was not allowed to change, climate sensitivity was reduced by 35-40%. We also modified the GISS 8 deg x lO deg atmospheric GCM to include an upper-ocean/sea-ice model involving the Semtner three-layer ice/snow thermodynamic model, the Price et al. (1986) ocean mixed layer model and a general upper ocean vertical advection/diffusion scheme for maintaining and fluxing properties across the pycnocline. This effort, in addition to improving the sea ice representation in the AGCM, revealed a number of sensitive components of the sea ice/ocean system. For example, the ability to flux heat through the ice/snow properly is critical in order to resolve the surface temperature properly, since small errors in this lead to unrestrained climate drift. The present project, summarized in this report, had as its objectives: (1) introducing a series of sea ice and ocean improvements aimed at overcoming remaining weaknesses in the GCM sea ice/ocean representation, and (2) performing a series of sensitivity experiments designed to evaluate the climate sensitivity of the revised model to both Antarctic and Arctic sea ice, determine the sensitivity of the climate response to initial ice distribution, and investigate the transient response to doubling CO2.

Improved Upper Ocean/Sea Ice Modeling in the GISS GCM for Investigating Climate Change

Science.gov (United States)

1997-01-01

This project built on our previous results in which we highlighted the importance of sea ice in overall climate sensitivity by determining that for both warming and cooling climates, when sea ice was not allowed to change, climate sensitivity was reduced by 35-40%. We also modified the Goddard Institute for Space Studies (GISS) 8 deg x lO deg atmospheric General Circulation Model (GCM) to include an upper-ocean/sea-ice model involving the Semtner three-layer ice/snow thermodynamic model, the Price et al. (1986) ocean mixed layer model and a general upper ocean vertical advection/diffusion scheme for maintaining and fluxing properties across the pycnocline. This effort, in addition to improving the sea ice representation in the AGCM, revealed a number of sensitive components of the sea ice/ocean system. For example, the ability to flux heat through the ice/snow properly is critical in order to resolve the surface temperature properly, since small errors in this lead to unrestrained climate drift. The present project, summarized in this report, had as its objectives: (1) introducing a series of sea ice and ocean improvements aimed at overcoming remaining weaknesses in the GCM sea ice/ocean representation, and (2) performing a series of sensitivity experiments designed to evaluate the climate sensitivity of the revised model to both Antarctic and Arctic sea ice, determine the sensitivity of the climate response to initial ice distribution, and investigate the transient response to doubling CO2.

Evaluation of aerosol distributions in the GISS-TOMAS global aerosol microphysics model with remote sensing observations

Directory of Open Access Journals (Sweden)

Y. H. Lee

2010-03-01

Full Text Available The Aerosol Optical Depth (AOD and Angstrom Coefficient (AC predictions in the GISS-TOMAS model of global aerosol microphysics are evaluated against remote sensing data from MODIS, MISR, and AERONET. The model AOD agrees well (within a factor of two over polluted continental (or high sulfate, dusty, and moderate sea-salt regions but less well over the equatorial, high sea-salt, and biomass burning regions. Underprediction of sea-salt in the equatorial region is likely due to GCM meteorology (low wind speeds and high precipitation. For the Southern Ocean, overprediction of AOD is very likely due to high sea-salt emissions and perhaps aerosol water uptake in the model. However, uncertainties in cloud screening at high latitudes make it difficult to evaluate the model AOD there with the satellite-based AOD. AOD in biomass burning regions is underpredicted, a tendency found in other global models but more severely here. Using measurements from the LBA-SMOCC 2002 campaign, the surface-level OC concentration in the model are found to be underpredicted severely during the dry season while much less severely for EC concentration, suggesting the low AOD in the model is due to underpredictions in OM mass. The potential for errors in emissions and wet deposition to contribute to this bias is discussed.

Qualitative research methods: key features and insights gained from use in infection prevention research.

Science.gov (United States)

Forman, Jane; Creswell, John W; Damschroder, Laura; Kowalski, Christine P; Krein, Sarah L

2008-12-01

Infection control professionals and hospital epidemiologists are accustomed to using quantitative research. Although quantitative studies are extremely important in the field of infection control and prevention, often they cannot help us explain why certain factors affect the use of infection control practices and identify the underlying mechanisms through which they do so. Qualitative research methods, which use open-ended techniques, such as interviews, to collect data and nonstatistical techniques to analyze it, provide detailed, diverse insights of individuals, useful quotes that bring a realism to applied research, and information about how different health care settings operate. Qualitative research can illuminate the processes underlying statistical correlations, inform the development of interventions, and show how interventions work to produce observed outcomes. This article describes the key features of qualitative research and the advantages that such features add to existing quantitative research approaches in the study of infection control. We address the goal of qualitative research, the nature of the research process, sampling, data collection and analysis, validity, generalizability of findings, and presentation of findings. Health services researchers are increasingly using qualitative methods to address practical problems by uncovering interacting influences in complex health care environments. Qualitative research methods, applied with expertise and rigor, can contribute important insights to infection prevention efforts.

Distinguishing Features and Similarities Between Descriptive Phenomenological and Qualitative Description Research.

Science.gov (United States)

Willis, Danny G; Sullivan-Bolyai, Susan; Knafl, Kathleen; Cohen, Marlene Z

2016-09-01

Scholars who research phenomena of concern to the discipline of nursing are challenged with making wise choices about different qualitative research approaches. Ultimately, they want to choose an approach that is best suited to answer their research questions. Such choices are predicated on having made distinctions between qualitative methodology, methods, and analytic frames. In this article, we distinguish two qualitative research approaches widely used for descriptive studies: descriptive phenomenological and qualitative description. Providing a clear basis that highlights the distinguishing features and similarities between descriptive phenomenological and qualitative description research will help students and researchers make more informed choices in deciding upon the most appropriate methodology in qualitative research. We orient the reader to distinguishing features and similarities associated with each approach and the kinds of research questions descriptive phenomenological and qualitative description research address. © The Author(s) 2016.

Multiple GISS AGCM Hindcasts and MSU Versions of 1979-1998

Science.gov (United States)

Shah, Kathryn Pierce; Rind, David; Druyan, Leonard; Lonergan, Patrick; Chandler, Mark

1998-01-01

Multiple realizations of the 1979-1998 time period have been simulated by the Goddard Institute for Space Studies Atmospheric General Circulation Model (GISS AGCM) to explore its responsiveness to accumulated forcings, particularly over sensitive agricultural regions. A microwave radiative transfer postprocessor has produced the AGCM's lower tropospheric, tropospheric and lower stratospheric brightness temperature (Tb) time series for correlations with the various Microwave Sounding Unit (MSU) time series available. MSU maps of monthly means and anomalies were also used to assess the AGCM's mean annual cycle and regional variability. Seven realizations by the AGCM were forced by observed sea surface temperatures (sst) through 1992 to gather rough standard deviations associated with internal model variability. Subsequent runs hindcast January 1979 through April 1998 with an accumulation of forcings: observed ssts, greenhouse gases, stratospheric volcanic aerosols. stratospheric and tropospheric ozone and tropospheric sulfate and black carbon aerosols. The goal of narrowing gaps between AGCM and MSU time series was complicated by MSU time series, by Tb simulation concerns and by unforced climatic variability in the AGCM and in the real world. Lower stratospheric Tb correlations between the AGCM and MSU for 1979-1998 reached as high as 0.91 +/-0.16 globally with sst, greenhouse gases, volcanic aerosol, stratospheric ozone forcings and tropospheric aerosols. Mid-tropospheric Tb correlations reached as high as 0.66 +/-.04 globally and 0.84 +/-.02 in the tropics. Oceanic lower tropospheric Tb correlations similarly reached 0.61 +/-.06 globally and 0.79 +/-.02 in the tropics. Of the sensitive agricultural areas considered, Nordeste in northeastern Brazil was simulated best with mid-tropospheric Tb correlations up to 0.75 +/- .03. The two other agricultural regions, in Africa and in the northern mid-latitudes, suffered from higher levels of non-sst variability. Zimbabwe

Trends and Features of Student Research Integration in Educational Program

Science.gov (United States)

Grinenko, Svetlana; Makarova, Elena; Andreassen, John-Erik

2016-01-01

This study examines trends and features of student research integration in educational program during international cooperation between Østfold University College in Norway and Southern Federal University in Russia. According to research and education approach the international project is aimed to use four education models, which linked student…

Simulations of the Mid-Pliocene Warm Period Using Two Versions of the NASA-GISS ModelE2-R Coupled Model

Science.gov (United States)

Chandler, M. A.; Sohl, L. E.; Jonas, J. A.; Dowsett, H. J.; Kelley, M.

2013-01-01

The mid-Pliocene Warm Period (mPWP) bears many similarities to aspects of future global warming as projected by the Intergovernmental Panel on Climate Change (IPCC, 2007). Both marine and terrestrial data point to high-latitude temperature amplification, including large decreases in sea ice and land ice, as well as expansion of warmer climate biomes into higher latitudes. Here we present our most recent simulations of the mid-Pliocene climate using the CMIP5 version of the NASAGISS Earth System Model (ModelE2-R). We describe the substantial impact associated with a recent correction made in the implementation of the Gent-McWilliams ocean mixing scheme (GM), which has a large effect on the simulation of ocean surface temperatures, particularly in the North Atlantic Ocean. The effect of this correction on the Pliocene climate results would not have been easily determined from examining its impact on the preindustrial runs alone, a useful demonstration of how the consequences of code improvements as seen in modern climate control runs do not necessarily portend the impacts in extreme climates.Both the GM-corrected and GM-uncorrected simulations were contributed to the Pliocene Model Intercomparison Project (PlioMIP) Experiment 2. Many findings presented here corroborate results from other PlioMIP multi-model ensemble papers, but we also emphasize features in the ModelE2-R simulations that are unlike the ensemble means. The corrected version yields results that more closely resemble the ocean core data as well as the PRISM3D reconstructions of the mid-Pliocene, especially the dramatic warming in the North Atlantic and Greenland-Iceland-Norwegian Sea, which in the new simulation appears to be far more realistic than previously found with older versions of the GISS model. Our belief is that continued development of key physical routines in the atmospheric model, along with higher resolution and recent corrections to mixing parameterisations in the ocean model, have led

Dangerous human-made interference with climate: a GISS modelE study

Directory of Open Access Journals (Sweden)

J. Hansen

2007-01-01

Full Text Available We investigate the issue of "dangerous human-made interference with climate" using simulations with GISS modelE driven by measured or estimated forcings for 1880–2003 and extended to 2100 for IPCC greenhouse gas scenarios as well as the "alternative" scenario of Hansen and Sato (2004. Identification of "dangerous" effects is partly subjective, but we find evidence that added global warming of more than 1°C above the level in 2000 has effects that may be highly disruptive. The alternative scenario, with peak added forcing ~1.5 W/m2 in 2100, keeps further global warming under 1°C if climate sensitivity is ~3°C or less for doubled CO2. The alternative scenario keeps mean regional seasonal warming within 2σ (standard deviations of 20th century variability, but other scenarios yield regional changes of 5–10σ, i.e. mean conditions outside the range of local experience. We conclude that a CO2 level exceeding about 450 ppm is "dangerous", but reduction of non-CO2 forcings can provide modest relief on the CO2 constraint. We discuss three specific sub-global topics: Arctic climate change, tropical storm intensification, and ice sheet stability. We suggest that Arctic climate change has been driven as much by pollutants (O3, its precursor CH4, and soot as by CO2, offering hope that dual efforts to reduce pollutants and slow CO2 growth could minimize Arctic change. Simulated recent ocean warming in the region of Atlantic hurricane formation is comparable to observations, suggesting that greenhouse gases (GHGs may have contributed to a trend toward greater hurricane intensities. Increasing GHGs cause significant warming in our model in submarine regions of ice shelves and shallow methane hydrates, raising concern about the potential for accelerating sea level rise and future positive feedback from methane release. Growth of non-CO2 forcings has slowed in recent years, but CO2 emissions are now surging well above the alternative scenario. Prompt

Dangerous human-made interference with climate: a GISS modelE study

International Nuclear Information System (INIS)

Hansen, J.; Lacis, A.; Miller, R.; Schmidt, G.A.; Russell, G.; Canuto, V.; Del Genio, A.; Hall, T.; Kiang, N.Y.; Rind, D.; Romanou, A.; Shindell, D.; Sun, S.; Hansen, J.; Sato, M.; Kharecha, P.; Nazarenko, L.; Aleinov, I.; Bauer, S.; Chandler, M.; Faluvegi, G.; Jonas, J.; Koch, D.; Lerner, J.; Perlwitz, Ju.; Unger, N.; Zhang, S.; Ruedy, R.; Lo, K.; Cheng, Y.; Oinas, V.; Schmunk, R.; Tausnev, N.; Yao, M.; Lacis, A.; Schmidt, G.A.; Del Genio, A.; Rind, D.; Romanou, A.; Shindell, D.; Thresher, D.; Miller, R.; Cairns, B.; Hall, T.; Perlwitz, Ja.; Baum, E.; Cohen, A.; Fleming, E.; Jackman, C.; Labow, G.; Friend, A.; Kelley, M.

2007-01-01

We investigate the issue of 'dangerous human-made interference with climate' using simulations with GISS modelE driven by measured or estimated forcing for 1880-2003 and extended to 2100 for IPCC greenhouse gas scenarios as well as the 'alternative' scenario of Hansen and Sato (2004). Identification of 'dangerous' effects is partly subjective, but we find evidence that added global warming of more than 1 degrees C above the level in 2000 has effects that may be highly disruptive. The alternative scenario, with peak added forcing similar to 1.5 W/m 2 in 2100, keeps further global warming under 1 degrees C if climate sensitivity is similar to 3 degrees C or less for doubled CO 2 . The alternative scenario keeps mean regional seasonal warming within 2 σ (standard deviations) of 20. century variability, but other scenarios yield regional changes of 5-10 σ, i.e. mean conditions outside the range of local experience. We conclude that a CO 2 level exceeding about 450 ppm is 'dangerous', but reduction of non-CO 2 forcing can provide modest relief on the CO 2 constraint. We discuss three specific sub-global topics: Arctic climate change, tropical storm intensification, and ice sheet stability. We suggest that Arctic climate change has been driven as much by pollutants (O 3 , its precursor CH 4 , and soot) as by CO 2 , offering hope that dual efforts to reduce pollutants and slow CO 2 growth could minimize Arctic change. Simulated recent ocean warming in the region of Atlantic hurricane formation is comparable to observations, suggesting that greenhouse gases (GHGs) may have contributed to a trend toward greater hurricane intensities. Increasing GHGs cause significant warming in our model in submarine regions of ice shelves and shallow methane hydrates, raising concern about the potential for accelerating sea level rise and future positive feedback from methane release. Growth of non-CO 2 forcing has slowed in recent years, but CO 2 emissions are now surging well above the

Feature: Post Traumatic Stres Disorder PTSD: NIH Research to Results

Science.gov (United States)

... Navigation Bar Home Current Issue Past Issues Feature PTSD NIH Research to Results Past Issues / Winter 2009 ... be a key to a better understanding of PTSD and early identification of those at risk. Early ...

Eocene climate and Arctic paleobathymetry: A tectonic sensitivity study using GISS ModelE-R

Science.gov (United States)

Roberts, C. D.; Legrande, A. N.; Tripati, A. K.

2009-12-01

The early Paleogene (65-45 million years ago, Ma) was a ‘greenhouse’ interval with global temperatures warmer than any other time in the last 65 Ma. This period was characterized by high levels of CO2, warm high-latitudes, warm surface-and-deep oceans, and an intensified hydrological cycle. Sediments from the Arctic suggest that the Eocene surface Arctic Ocean was warm, brackish, and episodically enabled the freshwater fern Azolla to bloom. The precise mechanisms responsible for the development of these conditions remain uncertain. We present equilibrium climate conditions derived from a fully-coupled, water-isotope enabled, general circulation model (GISS ModelE-R) configured for the early Eocene. We also present model-data comparison plots for key climatic variables (SST and δ18O) and analyses of the leading modes of variability in the tropical Pacific and North Atlantic regions. Our tectonic sensitivity study indicates that Northern Hemisphere climate would have been very sensitive to the degree of oceanic exchange through the seaways connecting the Arctic to the Atlantic and Tethys. By restricting these seaways, we simulate freshening of the surface Arctic Ocean to ~6 psu and warming of sea-surface temperatures by 2°C in the North Atlantic and 5-10°C in the Labrador Sea. Our results may help explain the occurrence of low-salinity tolerant taxa in the Arctic Ocean during the Eocene and provide a mechanism for enhanced warmth in the north western Atlantic. We also suggest that the formation of a volcanic land-bridge between Greenland and Europe could have caused increased ocean convection and warming of intermediate waters in the Atlantic. If true, this result is consistent with the theory that bathymetry changes may have caused thermal destabilisation of methane clathrates in the Atlantic.

Climate implications of carbonaceous aerosols: An aerosol microphysical study using the GISS/MATRIX climate model

International Nuclear Information System (INIS)

Bauer, Susanne E.; Menon, Surabi; Koch, Dorothy; Bond, Tami; Tsigaridis, Kostas

2010-01-01

Recently, attention has been drawn towards black carbon aerosols as a likely short-term climate warming mitigation candidate. However the global and regional impacts of the direct, cloud-indirect and semi-direct forcing effects are highly uncertain, due to the complex nature of aerosol evolution and its climate interactions. Black carbon is directly released as particle into the atmosphere, but then interacts with other gases and particles through condensation and coagulation processes leading to further aerosol growth, aging and internal mixing. A detailed aerosol microphysical scheme, MATRIX, embedded within the global GISS modelE includes the above processes that determine the lifecycle and climate impact of aerosols. This study presents a quantitative assessment of the impact of microphysical processes involving black carbon, such as emission size distributions and optical properties on aerosol cloud activation and radiative forcing. Our best estimate for net direct and indirect aerosol radiative forcing change is -0.56 W/m 2 between 1750 and 2000. However, the direct and indirect aerosol effects are very sensitive to the black and organic carbon size distribution and consequential mixing state. The net radiative forcing change can vary between -0.32 to -0.75 W/m 2 depending on these carbonaceous particle properties. Assuming that sulfates, nitrates and secondary organics form a coating shell around a black carbon core, rather than forming a uniformly mixed particles, changes the overall net radiative forcing from a negative to a positive number. Black carbon mitigation scenarios showed generally a benefit when mainly black carbon sources such as diesel emissions are reduced, reducing organic and black carbon sources such as bio-fuels, does not lead to reduced warming.

The northern annular mode in summer and its relation to solar activity variations in the GISS ModelE

Science.gov (United States)

Lee, Jae N.; Hameed, Sultan; Shindell, Drew T.

2008-03-01

The northern annular mode (NAM) has been successfully used in several studies to understand the variability of the winter atmosphere and its modulation by solar activity. The variability of summer circulation can also be described by the leading empirical orthogonal function (EOF) of geopotential heights. We compare the annular modes of the summer geopotential heights in the northern hemisphere stratosphere and troposphere in the Goddard Institute for Space Studies (GISS) ModelE with those in the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. In the stratosphere, the summer NAM obtained from NCEP/NCAR reanalysis as well as from the ModelE simulations has the same sign throughout the northern hemisphere, but shows greater variability at low latitudes. The patterns in both analyses are consistent with the interpretation that low NAM conditions represent an enhancement of the seasonal difference between the summer and the annual averages of geopotential height, temperature and velocity distributions, while the reverse holds for high NAM conditions. Composite analysis of high and low NAM cases in both model and observation suggests that the summer stratosphere is more "summer-like" when the solar activity is near a maximum. This means that the zonal easterly wind flow is stronger and the temperature is higher than normal. Thus increased irradiance favors a low summer NAM. A quantitative comparison of the anti-correlation between the NAM and the solar forcing is presented in the model and in the observation, both of which show lower/higher NAM index in solar maximum/minimum conditions. The temperature fluctuations in simulated solar minimum conditions are greater than in solar maximum throughout the summer stratosphere. The summer NAM in the troposphere obtained from NCEP/NCAR reanalysis has a dipolar zonal structure with maximum variability over the Asian monsoon region. The corresponding EOF in ModelE has

The research of structural features of astralens - nanodimensional carbon particles of fulleroid type

International Nuclear Information System (INIS)

Ponomarev, A.N.; Nikitin, V.A.; Rybalko, V.V.

2006-01-01

The article is focused on the research of structural features of astralens - nanodimensional carbonic particles of fulleroid type. Astralens are perspective nanomodificators of properties of materials of different types. The potentials os astralens as modificators depend on their characteristic structural features, and in the first place, on the distribution of nanoparticles by sizes. The typical dimensions of astralens are determined to be within the range of 15-75 nm [ru

Research and Analysis on Energy Consumption Features of Civil Airports

Science.gov (United States)

Li, Bo; Zhang, Wen; Wang, Jianping; Xu, Junku; Su, Jixiang

2017-11-01

Civil aviation is an important part of China’s transportation system, and also the fastest-growing field of comprehensive transportation. Airports, as a key infrastructure of the air transportation system, are the junctions of air and ground transportation. Large airports are generally comprehensive transportation hubs that integrate various modes of transportation, serving as important functional zones of cities. Compared with other transportation hubs, airports cover a wide area, with plenty of functional sections, complex systems and strong specialization, while airport buildings represented by terminals have exhibited characteristics of large space, massive energy consumption, high requirement for safety and comfort, as well as concentrated and rapidly changing passenger flows. Through research and analysis on energy consumption features of civil airports, and analysis on energy consumption features of airports with different sizes or in different climate regions, this article has drawn conclusions therefrom.

Safety features and research needs of westinghouse advanced reactors

International Nuclear Information System (INIS)

Carelli, M.D.; Winters, J.W.; Cummins, W.E.; Bruschi, H.J.

2002-01-01

The three Westinghouse advanced reactors - AP600, AP1000 and IRIS - are at different levels of readiness. AP600 has received a Design Certification, its larger size version AP1000 is currently in the design certification process and IRIS has just completed its conceptual design and will initiate soon a licensing pre-application. The safety features of the passive designs AP600/AP1000 are presented, followed by the features of the more revolutionary IRIS, a small size modular integral reactor. A discussion of the IRIS safety by design approach is given. The AP600/AP1000 design certification is backed by completed testing and development which is summarized, together with a research program currently in progress which will extend AP600 severe accident test data to AP1000 conditions. While IRIS will of course rely on applicable AP600/1000 data, a very extensive testing campaign is being planned to address all the unique aspects of its design. Finally, IRIS plans to use a risk-informed approach in its licensing process. (authors)

The Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: A Case Study over the Continental U.S. using the GISS-ModelE2 and WRF Models

Science.gov (United States)

Racherla, P. N.; Shindell, D. T.; Faluvegi, G. S.

2012-01-01

Dynamical downscaling is being increasingly used for climate change studies, wherein the climates simulated by a coupled atmosphere-ocean general circulation model (AOGCM) for a historical and a future (projected) decade are used to drive a regional climate model (RCM) over a specific area. While previous studies have demonstrated that RCMs can add value to AOGCM-simulated climatologies over different world regions, it is unclear as to whether or not this translates to a better reproduction of the observed climate change therein. We address this issue over the continental U.S. using the GISS-ModelE2 and WRF models, a state-of-the-science AOGCM and RCM, respectively. As configured here, the RCM does not effect holistic improvement in the seasonally and regionally averaged surface air temperature or precipitation for the individual historical decades. Insofar as the climate change between the two decades is concerned, the RCM does improve upon the AOGCM when nudged in the domain proper, but only modestly so. Further, the analysis indicates that there is not a strong relationship between skill in capturing climatological means and skill in capturing climate change. Though additional research would be needed to demonstrate the robustness of this finding in AOGCM/RCM models generally, the evidence indicates that, for climate change studies, the most important factor is the skill of the driving global model itself, suggesting that highest priority should be given to improving the long-range climate skill of AOGCMs.

Research on feature extraction techniques of Hainan Li brocade pattern

Science.gov (United States)

Zhou, Yuping; Chen, Fuqiang; Zhou, Yuhua

2016-03-01

Hainan Li brocade skills has been listed as world non-material cultural heritage preservation, therefore, the research on Hainan Li brocade patterns plays an important role in Li brocade culture inheritance. The meaning of Li brocade patterns was analyzed and the shape feature extraction techniques to original Li brocade patterns were advanced in this paper, based on the contour tracking algorithm. First, edge detection was made on the design patterns, and then the morphological closing operation was used to smooth the image, and finally contour tracking was used to extract the outer contours of Li brocade patterns. The extracted contour features were processed by means of morphology, and digital characteristics of contours are obtained by invariant moments. At last, different patterns of Li brocade design are briefly analyzed according to the digital characteristics. The results showed that the pattern extraction method to Li brocade pattern shapes is feasible and effective according to above method.

RESEARCH ON FEATURE POINTS EXTRACTION METHOD FOR BINARY MULTISCALE AND ROTATION INVARIANT LOCAL FEATURE DESCRIPTOR

Directory of Open Access Journals (Sweden)

Hongwei Ying

2014-08-01

Full Text Available An extreme point of scale space extraction method for binary multiscale and rotation invariant local feature descriptor is studied in this paper in order to obtain a robust and fast method for local image feature descriptor. Classic local feature description algorithms often select neighborhood information of feature points which are extremes of image scale space, obtained by constructing the image pyramid using certain signal transform method. But build the image pyramid always consumes a large amount of computing and storage resources, is not conducive to the actual applications development. This paper presents a dual multiscale FAST algorithm, it does not need to build the image pyramid, but can extract feature points of scale extreme quickly. Feature points extracted by proposed method have the characteristic of multiscale and rotation Invariant and are fit to construct the local feature descriptor.

A Novel Approach for Automatic Machining Feature Recognition with Edge Blend Feature

OpenAIRE

Keong Chen Wong; Yusof Yusri

2017-01-01

This paper presents an algorithm for efficiently recognizing and determining the convexity of an edge blend feature. The algorithm first recognizes all of the edge blend features from the Boundary Representation of a part; then a series of convexity test have been run on the recognized edge blend features. The novelty of the presented algorithm lies in, instead of each recognized blend feature is suppressed as most of researchers did, the recognized blend features of this research are gone th...

Dependence of stratocumulus-topped boundary-layer entrainment on cloud-water sedimentation: Impact on global aerosol indirect effect in GISS ModelE3 single column model and global simulations

Science.gov (United States)

Ackerman, A. S.; Kelley, M.; Cheng, Y.; Fridlind, A. M.; Del Genio, A. D.; Bauer, S.

2017-12-01

Reduction in cloud-water sedimentation induced by increasing droplet concentrations has been shown in large-eddy simulations (LES) and direct numerical simulation (DNS) to enhance boundary-layer entrainment, thereby reducing cloud liquid water path and offsetting the Twomey effect when the overlying air is sufficiently dry, which is typical. Among recent upgrades to ModelE3, the latest version of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM), are a two-moment stratiform cloud microphysics treatment with prognostic precipitation and a moist turbulence scheme that includes an option in its entrainment closure of a simple parameterization for the effect of cloud-water sedimentation. Single column model (SCM) simulations are compared to LES results for a stratocumulus case study and show that invoking the sedimentation-entrainment parameterization option indeed reduces the dependence of cloud liquid water path on increasing aerosol concentrations. Impacts of variations of the SCM configuration and the sedimentation-entrainment parameterization will be explored. Its impact on global aerosol indirect forcing in the framework of idealized atmospheric GCM simulations will also be assessed.

Features of the choice of object of management and object of research in socially-educational systems

Directory of Open Access Journals (Sweden)

O G Fedorov

2009-12-01

Full Text Available In work features of modeling and research of socially-educational systems are analyzed, principles of a choice of objects of management and objects of research, and also definition of factors of the importance of subsystems are considered.

Inherent Safety Feature of Hybrid Low Power Research Reactor during Reactivity Induced Accident

Energy Technology Data Exchange (ETDEWEB)

Kim, DongHyun; Yum, Soo Been; Hong, Sung Teak; Lim, In-Cheol [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

Hybrid low power research reactor(H-LPRR) is the new design concept of low power research reactor for critical facility as well as education and training. In the case of typical low power research reactor, the purposes of utilization are the experiments for education of nuclear engineering students, Neutron Activation Analysis(NAA) and radio-isotope production for research purpose. H-LPRR is a light-water cooled and moderated research reactor that uses rod-type LEU UO{sub 2} fuels same as those for commercial power plants. The maximum core thermal power is 70kW and, the core is placed in the bottom of open pool. There are 1 control rod and 2 shutdown rods in the core. It is designed to cool the core by natural convection, retain negative feedback coefficient for entire fuel periods and operate for 20 years without refueling. Inherent safety in H-LPRR is achieved by passive design features such as negative temperature feedback coefficient and core cooling by natural convection during normal and emergency conditions. The purpose of this study is to find out that the inherent safety characteristics of H-LPRR is able to control the power and protect the reactor from the RIA(Reactivity induced accident). RIA analysis was performed to investigate the inherent safety feature of H-LPRR. As a result, it was found that the reactor controls its power without fuel damage in the event and that the reactor remains safe states inherently. Therefore, it is believed that high degree of safety inheres in H-LPRR.

Research on oral test modeling based on multi-feature fusion

Science.gov (United States)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

Research of image retrieval technology based on color feature

Science.gov (United States)

Fu, Yanjun; Jiang, Guangyu; Chen, Fengying

2009-10-01

Recently, with the development of the communication and the computer technology and the improvement of the storage technology and the capability of the digital image equipment, more and more image resources are given to us than ever. And thus the solution of how to locate the proper image quickly and accurately is wanted.The early method is to set up a key word for searching in the database, but now the method has become very difficult when we search much more picture that we need. In order to overcome the limitation of the traditional searching method, content based image retrieval technology was aroused. Now, it is a hot research subject.Color image retrieval is the important part of it. Color is the most important feature for color image retrieval. Three key questions on how to make use of the color characteristic are discussed in the paper: the expression of color, the abstraction of color characteristic and the measurement of likeness based on color. On the basis, the extraction technology of the color histogram characteristic is especially discussed. Considering the advantages and disadvantages of the overall histogram and the partition histogram, a new method based the partition-overall histogram is proposed. The basic thought of it is to divide the image space according to a certain strategy, and then calculate color histogram of each block as the color feature of this block. Users choose the blocks that contain important space information, confirming the right value. The system calculates the distance between the corresponding blocks that users choosed. Other blocks merge into part overall histograms again, and the distance should be calculated. Then accumulate all the distance as the real distance between two pictures. The partition-overall histogram comprehensive utilizes advantages of two methods above, by choosing blocks makes the feature contain more spatial information which can improve performance; the distances between partition-overall histogram

Study of In-Pile test facility for fast reactor safety research: performance requirements and design features

Energy Technology Data Exchange (ETDEWEB)

Nonaka, N.; Kawatta, N.; Niwa, H.; Kondo, S.; Maeda, K

1996-12-31

This paper describes a program and the main design features of a new in-pile safety facility SERAPH planned for future fast reactor safety research. The current status of R and D on technical developments in relation to the research objectives and performance requirements to the facility design is given.

Key features for more successful place-based sustainability research on social-ecological systems: a Programme on Ecosystem Change and Society (PECS perspective

Directory of Open Access Journals (Sweden)

Patricia Balvanera

2017-03-01

Full Text Available The emerging discipline of sustainability science is focused explicitly on the dynamic interactions between nature and society and is committed to research that spans multiple scales and can support transitions toward greater sustainability. Because a growing body of place-based social-ecological sustainability research (PBSESR has emerged in recent decades, there is a growing need to understand better how to maximize the effectiveness of this work. The Programme on Ecosystem Change and Society (PECS provides a unique opportunity for synthesizing insights gained from this research community on key features that may contribute to the relative success of PBSESR. We surveyed the leaders of PECS-affiliated projects using a combination of open, closed, and semistructured questions to identify which features of a research project are perceived to contribute to successful research design and implementation. We assessed six types of research features: problem orientation, research team, and contextual, conceptual, methodological, and evaluative features. We examined the desirable and undesirable aspects of each feature, the enabling factors and obstacles associated with project implementation, and asked respondents to assess the performance of their own projects in relation to these features. Responses were obtained from 25 projects working in 42 social-ecological study cases within 25 countries. Factors that contribute to the overall success of PBSESR included: explicitly addressing integrated social-ecological systems; a focus on solution- and transformation-oriented research; adaptation of studies to their local context; trusted, long-term, and frequent engagement with stakeholders and partners; and an early definition of the purpose and scope of research. Factors that hindered the success of PBSESR included: the complexities inherent to social-ecological systems, the imposition of particular epistemologies and methods on the wider research group

Climate and transboundary water management issues

International Nuclear Information System (INIS)

Bjonback, D.

1991-01-01

The potential effects of climate change on transboundary river systems, major water uses, interjurisdictional arrangements, and water issues affecting water management in the Great Plains of Canada are discussed. Three atmospheric general circulation models (GCM) have been applied for a two times carbon dioxide concentration scenario for the Saskatchewan River system. The models were the Goddard Institute for Space Studies (GISS) model, the Geophysical Fluid Dynamics Laboratory (GFDL) model, and the Oregon State University (OSU) model. For all models, soil moisture on the plains was reduced. The GISS model predicted slightly higher runoff for plains-originating streams, and a substantial increase in runoff (32%) in the Rockies. The GFDL model predicted lower runoffs in the plains and Rockies, with some locations near the Alberta-Saskatchewan border indicating zero runoff. The OSU model results generally bracketed the GISS and GFDL results, with total runoff approximating 1951-1980 mean. The GISS model indicated an increase in net basin supply of 28%, while the GFDL model, due to lower runoff and high soil moisture defecits, showed a decrease of 38%. For policy making, monitoring, and research, the GFDL model results can provide important guidelines. Greater attention to demand management and conservation will have short-term benefits in stretching the limited water resource base to support a larger economy, while providing flexibility to cope with future climate as it evolves. 1 ref

Assessing modeled Greenland surface mass balance in the GISS Model E2 and its sensitivity to surface albedo

Science.gov (United States)

Alexander, Patrick; LeGrande, Allegra N.; Koenig, Lora S.; Tedesco, Marco; Moustafa, Samiah E.; Ivanoff, Alvaro; Fischer, Robert P.; Fettweis, Xavier

2016-04-01

The surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) plays an important role in global sea level change. Regional Climate Models (RCMs) such as the Modèle Atmosphérique Régionale (MAR) have been employed at high spatial resolution with relatively complex physics to simulate ice sheet SMB. Global climate models (GCMs) incorporate less sophisticated physical schemes and provide outputs at a lower spatial resolution, but have the advantage of modeling the interaction between different components of the earth's oceans, climate, and land surface at a global scale. Improving the ability of GCMs to represent ice sheet SMB is important for making predictions of future changes in global sea level. With the ultimate goal of improving SMB simulated by the Goddard Institute for Space Studies (GISS) Model E2 GCM, we compare simulated GrIS SMB against the outputs of the MAR model and radar-derived estimates of snow accumulation. In order to reproduce present-day climate variability in the Model E2 simulation, winds are constrained to match the reanalysis datasets used to force MAR at the lateral boundaries. We conduct a preliminary assessment of the sensitivity of the simulated Model E2 SMB to surface albedo, a parameter that is known to strongly influence SMB. Model E2 albedo is set to a fixed value of 0.8 over the entire ice sheet in the initial configuration of the model (control case). We adjust this fixed value in an ensemble of simulations over a range of 0.4 to 0.8 (roughly the range of observed summer GrIS albedo values) to examine the sensitivity of ice-sheet-wide SMB to albedo. We prescribe albedo from the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 v6 to examine the impact of a more realistic spatial and temporal variations in albedo. An age-dependent snow albedo parameterization is applied, and its impact on SMB relative to observations and the RCM is assessed.

RESEARCH OF CLASSIFICATION FEATURES OF THE FINANCIAL CONTROL

Directory of Open Access Journals (Sweden)

Knarik K. Arabyan

2013-01-01

Full Text Available One of the major problems is an improvement of classification features in the financial control theory. There is not a consensus concerning the form classification and the methods of financial control. This factor hinders the development of methodology and investigation of other issues of the financial control theory. The author summarizes scientists’ approaches to studying the classification features of financial control in the article.

Design Features and Capabilities of the First Materials Science Research Rack

Science.gov (United States)

Pettigrew, P. J.; Lehoczky, S. L.; Cobb, S. D.; Holloway, T.; Kitchens, L.

2003-01-01

The First Materials Science Research Rack (MSRR-1) aboard the International Space Station (ISS) will offer many unique capabilities and design features to facilitate a wide range of materials science investigations. The initial configuration of MSRR-1 will accommodate two independent Experiment Modules (EMS) and provide the capability for simultaneous on-orbit processing. The facility will provide the common subsystems and interfaces required for the operation of experiment hardware and accommodate telescience capabilities. MSRR1 will utilize an International Standard Payload Rack (ISPR) equipped with an Active Rack Isolation System (ARIS) for vibration isolation of the facility.

Animating climate model data

Science.gov (United States)

DaPonte, John S.; Sadowski, Thomas; Thomas, Paul

2006-05-01

This paper describes a collaborative project conducted by the Computer Science Department at Southern Connecticut State University and NASA's Goddard Institute for Space Science (GISS). Animations of output from a climate simulation math model used at GISS to predict rainfall and circulation have been produced for West Africa from June to September 2002. These early results have assisted scientists at GISS in evaluating the accuracy of the RM3 climate model when compared to similar results obtained from satellite imagery. The results presented below will be refined to better meet the needs of GISS scientists and will be expanded to cover other geographic regions for a variety of time frames.

Using Self Organizing Maps to evaluate the NASA GISS AR5 SCM at the ARM SGP Site

Science.gov (United States)

Dong, X.; Kennedy, A. D.; Xi, B.

2010-12-01

Cluster analyses have gained popularity in recent years to establish cloud regimes using satellite and radar cloud data. These regimes can then be used to evaluate climate models or to determine what large-scale or subgrid processes are responsible for cloud formation. An alternative approach is to first classify the meteorological regimes (i.e. synoptic pattern and forcing) and then determine what cloud scenes occur. In this study, a competitive neural network known as the Self Organizing Map (SOM) is used to classify synoptic patterns over the Southern Great Plains (SGP) region to evaluate simulated clouds from the AR5 version of the NASA GISS Model E Single Column Model (SCM). In detail, 54-class SOMs have been developed using North American Regional Reanalysis (NARR) variables averaged to 2x2.5 degree latitude longitude grid boxes for a region of 7x7 grid boxes centered on the ARM SGP site. Variables input into the SOM include mean sea-level pressure and the horizontal wind components, relative humidity, and geopotential height at the 900, 500, and 300 hPa levels. These SOMs are produced for the winter (DJF), spring (MAM), summer (JJA), and fall (SON) seasons during 1999-2001. This synoptic typing will be associated with observed cloud fractions and forcing properties from the ARM SGP site and then used to evaluate simulated clouds from the SCM. SOMs provide a visually intuitive way to understand their classifications because classes are related to each other in a two-dimensional space. In Fig. 1 for example, the reader can easily see for a 54 class SOM during the winter season, classes with higher 300 hPa mean relative humidities are clustered near each other. This allows for the user to identify that there appears to be a relationship between mean 300 hPa RH and high cloud fraction as observed by the ARM SGP site. Figure 1. Mean high cloud fraction (top panel) and 300 hPa Relative Humidity (bottom panel) for a 9x6 (54 class) SOM during the winter (DJF) season

Design of the Control System for Engineered Safety Features of KIJANG Research Reactor

International Nuclear Information System (INIS)

Kim, Hagtae; Kim, Jun-Yeon; Chae, Hee-Taek

2015-01-01

The purpose of this paper is to design an effective control system for the Engineered Safety Features (ESF) of KJRR such as the Safety Residual Heat Removal System (SRHRS) pumps and Siphon Break Valve (SBV) without an Engineered Safety Features-Component Control System (ESF-CCS). This control system is called a 'local motor starter', because this system controls motors in the SRHRS pumps and SBVs by receiving the signal from Reactor Protection System (RPS) and Alternate Protection System (APS) when the differential pressure or pool level reach the set points. In this paper, the design concepts and requirements of the local motor starter based on the design features of KJRR is proposed. An ESF is a safety system that mitigates consequences of the Anticipated Operational Occurrence (AOO) and Design Basis Accident (DBA). The results of this paper are able to be used for the development of control systems for research reactors similar to KJRR. The precondition for such application is to have a few ESFs and conduct simple logic. The proposed control system called a local motor starter is being designed, and a manufacture of the actual systems is expected in the foreseeable future

Climate response to projected changes in short-lived species under an A1B scenario from 2000-2050 in the GISS climate model

Energy Technology Data Exchange (ETDEWEB)

Menon, Surabi; Shindell, Drew T.; Faluvegi, Greg; Bauer, Susanne E.; Koch, Dorothy M.; Unger, Nadine; Menon, Surabi; Miller, Ron L.; Schmidt, Gavin A.; Streets, David G.

2007-03-26

We investigate the climate forcing from and response to projected changes in short-lived species and methane under the A1B scenario from 2000-2050 in the GISS climate model. We present a meta-analysis of new simulations of the full evolution of gas and aerosol species and other existing experiments with variations of the same model. The comparison highlights the importance of several physical processes in determining radiative forcing, especially the effect of climate change on stratosphere-troposphere exchange, heterogeneous sulfate-nitrate-dust chemistry, and changes in methane oxidation and natural emissions. However, the impact of these fairly uncertain physical effects is substantially less than the difference between alternative emission scenarios for all short-lived species. The net global mean annual average direct radiative forcing from the short-lived species is .02 W/m{sup 2} or less in our projections, as substantial positive ozone forcing is largely offset by negative aerosol direct forcing. Since aerosol reductions also lead to a reduced indirect effect, the global mean surface temperature warms by {approx}0.07 C by 2030 and {approx}0.13 C by 2050, adding 19% and 17%, respectively, to the warming induced by long-lived greenhouse gases. Regional direct forcings are large, up to 3.8 W/m{sup 2}. The ensemble-mean climate response shows little regional correlation with the spatial pattern of the forcing, however, suggesting that oceanic and atmospheric mixing generally overwhelms the effect of even large localized forcings. Exceptions are the polar regions, where ozone and aerosols may induce substantial seasonal climate changes.

The Discourse Structure and Linguistic Features of Research Article Abstracts in English by Indonesian Academics

Science.gov (United States)

Arsyad, Safnil

2014-01-01

To effectively teach university lecturers or students to write a good research article (RA) abstract for publication in international journals, instructors need to know the present characteristics of abstracts written published in such journals. This study examines the discourse structure and linguistic features of RA abstracts written in English…

Linear feature extraction from radar imagery: SBIR (Small Business Innovative Research), phase 2, option 2

Science.gov (United States)

Milgram, David L.; Kahn, Philip; Conner, Gary D.; Lawton, Daryl T.

1988-12-01

The goal of this effort is to develop and demonstrate prototype processing capabilities for a knowledge-based system to automatically extract and analyze features from Synthetic Aperture Radar (SAR) imagery. This effort constitutes Phase 2 funding through the Defense Small Business Innovative Research (SBIR) Program. Previous work examined the feasibility of and technology issues involved in the development of an automated linear feature extraction system. This final report documents this examination and the technologies involved in automating this image understanding task. In particular, it reports on a major software delivery containing an image processing algorithmic base, a perceptual structures manipulation package, a preliminary hypothesis management framework and an enhanced user interface.

Using long-term ARM observations to evaluate Arctic mixed-phased cloud representation in the GISS ModelE GCM

Science.gov (United States)

Lamer, K.; Fridlind, A. M.; Luke, E. P.; Tselioudis, G.; Ackerman, A. S.; Kollias, P.; Clothiaux, E. E.

2016-12-01

The presence of supercooled liquid in clouds affects surface radiative and hydrological budgets, especially at high latitudes. Capturing these effects is crucial to properly quantifying climate sensitivity. Currently, a number of CGMs disagree on the distribution of cloud phase. Adding to the challenge is a general lack of observations on the continuum of clouds, from high to low-level and from warm to cold. In the current study, continuous observations from 2011 to 2014 are used to evaluate all clouds produced by the GISS ModelE GCM over the ARM North Slope of Alaska site. The International Satellite Cloud Climatology Project (ISCCP) Global Weather State (GWS) approach reveals that fair-weather (GWS 7, 32% occurrence rate), as well as mid-level storm related (GWS 5, 28%) and polar (GWS 4, 14%) clouds, dominate the large-scale cloud patterns at this high latitude site. At higher spatial and temporal resolutions, ground-based cloud radar observations reveal a majority of single layer cloud vertical structures (CVS). While clear sky and low-level clouds dominate (each with 30% occurrence rate) a fair amount of shallow ( 10%) to deep ( 5%) convection are observed. Cloud radar Doppler spectra are used along with depolarization lidar observations in a neural network approach to detect the presence, layering and inhomogeneity of supercooled liquid layers. Preliminary analyses indicate that most of the low-level clouds sampled contain one or more supercooled liquid layers. Furthermore, the relationship between CVS and the presence of supercooled liquid is established, as is the relationship between the presence of supercool liquid and precipitation susceptibility. Two approaches are explored to bridge the gap between large footprint GCM simulations and high-resolution ground-based observations. The first approach consists of comparing model output and ground-based observations that exhibit the same column CVS type (i.e. same cloud depth, height and layering

Research and development on reduced-moderation light water reactor with passive safety features (Contract research)

International Nuclear Information System (INIS)

Iwamura, Takamichi; Okubo, Tsutomu; Akie, Hiroshi; Kugo, Teruhiko; Yonomoto, Taisuke; Kureta, Masatoshi; Ishikawa, Nobuyuki; Nagaya, Yasunobu; Araya, Fumimasa; Okajima, Shigeaki; Okumura, Keisuke; Suzuki, Motoe; Mineo, Hideaki; Nakatsuka, Toru

2004-06-01

The present report contains the achievement of 'Research and Development on Reduced-moderation Light Water Reactor with Passive Safety Features', which was performed by Japan Atomic Energy Research Institute (JAERI), Hitachi Ltd., Japan Atomic Power Company and Tokyo Institute of Technology in FY2000-2002 as the innovative and viable nuclear energy technology (IVNET) development project operated by the Institute of Applied Energy (IAE). In the present project, the reduced-moderation water reactor (RMWR) has been developed to ensure sustainable energy supply and to solve the recent problems of nuclear power and nuclear fuel cycle, such as economical competitiveness, effective use of plutonium and reduction of spent fuel storage. The RMWR can attain the favorable characteristics such as high burnup, long operation cycle, multiple recycling of plutonium (Pu) and effective utilization of uranium resources based on accumulated LWR technologies. Our development target is 'Reduced-moderation Light Water Reactor with Passive Safety Features' with innovative technologies to achieve above mentioned requirement. Electric power is selected as 300 MWe considering anticipated size required for future deployment. The reactor core consists of MOX fuel assemblies with tight lattice arrangement to increase the conversion ratio. Design targets of the core specification are conversion ratio more than unity, negative void reactivity feedback coefficient to assure safety, discharged burnup more than 60 GWd/t and operation cycle more than 2 years. As for the reactor system, a small size natural circulation BWR with passive safety systems is adopted to increase safety and reduce construction cost. The results obtained are as follows: As regards core design study, core design was performed to meet the goal. Sequence of startup operation was constructed for the RMWR. As the plant design, plant system was designed to achieve enhanced economy using passive safety system effectively. In

Linear feature extraction from radar imagery: SBIR (Small Business Innovative Research) phase 2, option 1

Science.gov (United States)

Conner, Gary D.; Milgram, David L.; Lawton, Daryl T.; McConnell, Christopher C.

1988-04-01

The goal of this effort is to develop and demonstrate prototype processing capabilities for a knowledge-based system to automatically extract and analyze linear features from synthetic aperture radar (SAR) imagery. This effort constitutes Phase 2 funding through the Defense Small Business Innovative Research (SBIR) Program. Previous work examined the feasibility of the technology issues involved in the development of an automatedlinear feature extraction system. This Option 1 Final Report documents this examination and the technologies involved in automating this image understanding task. In particular, it reports on a major software delivery containing an image processing algorithmic base, a perceptual structures manipulation package, a preliminary hypothesis management framework and an enhanced user interface.

Defining features of the practice of global health research: an examination of 14 global health research teams

Directory of Open Access Journals (Sweden)

Craig Stephen

2010-07-01

Full Text Available Objectives: This paper strives to develop a pragmatic view of the scope of practice and core characteristics of global health research (GHR by examining the activities of 14 Canadian-funded global health teams that were in the process of implementing research programs. Methods: Information was collected by a reflective exploration of team proposals and progress reports, a content analysis of the outputs from an all-team meeting and review of the literature. Results: Teams adopted equity-centered, problem-focused, systems-based approaches intended to find upstream determinants that could make people more resilient to social and ecological factors impacting their health. Long-term visions and time frames were needed to develop and solidify fully functional interdisciplinary, multinational, multicultural partnerships. The implementation of research into practice was a motivating factor for all teams, but to do this, they recognized the need for evidence-based advice on how to best do this. Traditional measures of biomedical research excellence were necessary but not sufficient to encompass views of excellence of team-based interdisciplinary research, which includes features like originality, coherence and cumulative contributions to fields of study, acceptance by peers and success in translating research into gains in health status. An innovative and nuanced approached to GHR ethics was needed to deal with some unique ethical issues because the needs for GHR were not adequately addressed by institutional biomedical research ethics boards. Core competencies for GHR researchers were a blend of those needed for health promotion, population health, international development, sustainable development, and systems science. Discussion: Developing acceptable and meaningful ways to evaluate the short-term contributions for GHR and forecast its long-term impacts is a strategic priority needed to defend decisions being made in GHR development. Planning and

Feature Selection by Reordering

Czech Academy of Sciences Publication Activity Database

Jiřina, Marcel; Jiřina jr., M.

2005-01-01

Roč. 2, č. 1 (2005), s. 155-161 ISSN 1738-6438 Institutional research plan: CEZ:AV0Z10300504 Keywords : feature selection * data reduction * ordering of features Subject RIV: BA - General Mathematics

Effective Feature Preprocessing for Time Series Forecasting

DEFF Research Database (Denmark)

Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

2006-01-01

Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...... series forecasting models....

Opinion mining feature-level using Naive Bayes and feature extraction based analysis dependencies

Science.gov (United States)

Sanda, Regi; Baizal, Z. K. Abdurahman; Nhita, Fhira

2015-12-01

Development of internet and technology, has major impact and providing new business called e-commerce. Many e-commerce sites that provide convenience in transaction, and consumers can also provide reviews or opinions on products that purchased. These opinions can be used by consumers and producers. Consumers to know the advantages and disadvantages of particular feature of the product. Procuders can analyse own strengths and weaknesses as well as it's competitors products. Many opinions need a method that the reader can know the point of whole opinion. The idea emerged from review summarization that summarizes the overall opinion based on sentiment and features contain. In this study, the domain that become the main focus is about the digital camera. This research consisted of four steps 1) giving the knowledge to the system to recognize the semantic orientation of an opinion 2) indentify the features of product 3) indentify whether the opinion gives a positive or negative 4) summarizing the result. In this research discussed the methods such as Naï;ve Bayes for sentiment classification, and feature extraction algorithm based on Dependencies Analysis, which is one of the tools in Natural Language Processing (NLP) and knowledge based dictionary which is useful for handling implicit features. The end result of research is a summary that contains a bunch of reviews from consumers on the features and sentiment. With proposed method, accuration for sentiment classification giving 81.2 % for positive test data, 80.2 % for negative test data, and accuration for feature extraction reach 90.3 %.

Research on Degeneration Model of Neural Network for Deep Groove Ball Bearing Based on Feature Fusion

Directory of Open Access Journals (Sweden)

Lijun Zhang

2018-02-01

Full Text Available Aiming at the pitting fault of deep groove ball bearing during service, this paper uses the vibration signal of five different states of deep groove ball bearing and extracts the relevant features, then uses a neural network to model the degradation for identifying and classifying the fault type. By comparing the effects of training samples with different capacities through performance indexes such as the accuracy and convergence speed, it is proven that an increase in the sample size can improve the performance of the model. Based on the polynomial fitting principle and Pearson correlation coefficient, fusion features based on the skewness index are proposed, and the performance improvement of the model after incorporating the fusion features is also validated. A comparison of the performance of the support vector machine (SVM model and the neural network model on this dataset is given. The research shows that neural networks have more potential for complex and high-volume datasets.

Research of features and structure of electoral space of Ukraine in 2014 with the use of synthetic approach

Directory of Open Access Journals (Sweden)

M. M. Shelemba

2015-02-01

Full Text Available The article is aimed at the ground of expediency of the use of synthetic authorial model for research of features and structure of electoral space of Ukraine in 2014 year. Methodological principles of the use of synthetic model are expounded with the use of quality and quantitative methods researches of electoral space, among that methods of factor and cross­correlation analysis. A synthetic model (approach that is built on the basis of the use of the best scientific approaches takes into account features and progress of electoral space of Ukraine trends. The analysis of features and structure of electoral space of Ukraine is conducted in 2014 with the use of an offer model. The application author synthetic model allows the study of the use of association factor and correlation analysis to justify support to political parties during election campaigns, respectively, depending on the factors and the most important correlates. It was found that electoral choice depends on the actions of those factors in the highest degree the expectations of the region. This article has shown that the use of Ukraine at this stage of the investigated during election campaigns as the most significant social correlates of «Human Development Index» is reasonable and one that makes it possible to obtain reliable results. It is proved that a high level of correlation holds at a high level of support the party and, consequently, high sense of social correlates all variants of expert research.

Features of Perception of Modern Russian Political Leaders in University Students (A Psychosemantic Research

Directory of Open Access Journals (Sweden)

Sobkin V.S.

2015-09-01

Full Text Available The paper presents outcomes of a research on features of perception of modern Russian political leaders in young people. A technique of multidimensional semantic differential was employed: the subjects were asked to assess 15 objects (political leaders, 'my ideal', 'ideal leader', 'antipathetic person' and 'Myself' according to 33 personality traits using a seven-point scale. The outcomes suggest that the structure of the students' perception of political leaders is quite simple and is based on three modalities: 'morality', 'power' and 'intelligence'. Comparing these outcomes with the research data obtained in 2004 using the same technique allowed the authors to conclude that the students do not assess modern political leaders according to the moral qualities of the latter, but rather perceive them through the qualities of power associated with social manipulation.

How to successfully publish interdisciplinary research: learning from an Ecology and Society Special Feature

Directory of Open Access Journals (Sweden)

Christian Pohl

2015-06-01

Full Text Available What are the factors that hinder or support publishing interdisciplinary research? What does a successful interdisciplinary publishing process look like? We address these questions by analyzing the publishing process of the interdisciplinary research project titled "Mountland." Project researchers published most of their main results as a Special Feature of Ecology and Society. Using the story wall method and qualitative content analysis, we identified ten factors contributing to the success or failure of publishing interdisciplinary research. They can be assigned to four groups of resources: scientific resources, i.e., previous joint research, simultaneously written manuscripts; human resources, i.e., coordination, flexibility, composition of the team; integrative resources, i.e., vision of integration, chronology of results; and feedback resources, i.e., internal reviews, subject editors, external reviewers. According to this analysis, an ideal-typical publishing process necessitates, among other things, (1 a strong, interdisciplinary coordinator, (2 a clear shared vision of integration and a common framework, (3 flexibility in terms of money and time, (4 a certain sense of timing regarding when and how to exchange results and knowledge, (5 subject editors who are familiar with the specific project and its interdisciplinary merits, and (6 reviewers who are open minded about interdisciplinary efforts.

Image feature detectors and descriptors foundations and applications

CERN Document Server

Hassaballah, Mahmoud

2016-01-01

This book provides readers with a selection of high-quality chapters that cover both theoretical concepts and practical applications of image feature detectors and descriptors. It serves as reference for researchers and practitioners by featuring survey chapters and research contributions on image feature detectors and descriptors. Additionally, it emphasizes several keywords in both theoretical and practical aspects of image feature extraction. The keywords include acceleration of feature detection and extraction, hardware implantations, image segmentation, evolutionary algorithm, ordinal measures, as well as visual speech recognition. .

RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

Directory of Open Access Journals (Sweden)

Z. Wang

2017-09-01

Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

Evaluating the Stability of Feature Selectors that Optimize Feature Subset Cardinality

Czech Academy of Sciences Publication Activity Database

Somol, Petr; Novovičová, Jana

2008-01-01

Roč. 2008, č. 5342 (2008), s. 956-966 ISSN 0302-9743. [Joint IAPR International Workshops SSPR 2008 and SPR 2008. Orlando , 04.12.2008-06.12.2008] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA ČR GA102/07/1594 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Feature selection * stability * relative weighted consistency measure * sequential search * floating search Subject RIV: IN - Informatics, Computer Science http://library.utia.cas.cz/separaty/2008/RO/somol-evaluating the stability of feature selectors that optimize feature subset cardinality.pdf

Celluloid devils: a research study of male nurses in feature films.

Science.gov (United States)

Stanley, David

2012-11-01

To report a study of how male nurses are portrayed in feature films. It was hypothesized that male nurses are frequently portrayed negatively or stereotypically in the film media, potentially having a negative impact on male nurse recruitment and the public's perception of male nurses. An interpretive, qualitative methodology guided by insights into hegemonic masculinity and structured around a set of collective case studies (films) was used to examine the portrayal of male nurses in feature films made in the Western world from 1900 to 2007. Over 36,000 feature film synopses were reviewed (via CINAHL, ProQuest and relevant movie-specific literature) for the keyword 'nurse' and 'nursing' with an additional search for films from 1900 to 2010 for the word 'male nurse'. Identified films were labelled as 'cases' and analysed collectively to determine key attributes related to men in nursing and explore them for the emergence of concepts and themes related to the image of male nurses in films. A total of 13 relevant cases (feature films) were identified with 12 being made in the USA. Most films portrayed male nurses negatively and in ways opposed to hegemonic masculinity, as effeminate, homosexual, homicidal, corrupt or incompetent. Few film images of male nurses show them in traditional masculine roles or as clinically competent or self-confident professionals.   Feature films predominantly portray male nurses negatively. Given the popularity of feature films, there may be negative effects on recruitment and on the public's perception of male nurses. © 2012 Blackwell Publishing Ltd.

No Smoke Without Fire: the hidden costs of early life exposure to landscape fire emissions in Indonesia

Science.gov (United States)

Jina, A.; Marlier, M. E.

2012-12-01

Air pollution from landscape fire emissions can have devastating effects upon public health. The consequent health costs place a burden upon the economies of many nations, particularly in developing countries. Recent research has assessed contemporaneous mortality due to respiratory infections or cardiovascular disease, but little has looked at the potential long-term consequences and hidden costs of exposure to fire pollution at a population scale. The difficulty of quantifying these costs is partly due to incomplete or inaccurate health data in many developing countries, and is further compounded by sparse air pollution monitoring data. While satellite data partially compensates for this, there can still be significant gaps in data availability and difficulty in retrieving surface concentrations. In this study, we demonstrate the dramatic long-term health and human development consequences of fine particulate matter (PM2.5) exposure by using modeled PM2.5 to quantify repeated exposure to landscape fire emissions in Indonesia, which is prone to large, catastrophic fires during El Niño conditions. Surface PM2.5 concentrations at 2x2.5° resolution are obtained from GISS-E2-Puccini (the new version of the NASA GISS ModelE general circulation model), run with monthly fire emissions from the Global Fire Emissions Database version 3 (GFED3). 24-hour ambient PM2.5 concentrations across Indonesia are matched to geographically and socioeconomically representative longitudinal surveys conducted by the Indonesian government. We find important medium- to long-term morbidity associated with early life exposure to ambient air pollution from fire emissions. Our analysis indicates that children exposed to high levels of PM2.5 in utero are more likely to suffer from impaired physical and cognitive development. A one standard deviation increase in ambient air pollution, derived from the GISS-E2-Puccini model, leads to effects that are directly comparable to those from indoor air

Growing Diversity in Space Weather and Climate Change Research

Science.gov (United States)

Johnson, L. P.; Ng, C.; Marchese, P.; Austin, S.; Frost, J.; Cheung, T. D.; Robbins, I.; Carlson, B. E.; Steiner, J. C.; Tremberger, G.; Paglione, T.; Damas, C.; Howard, A.; Scalzo, F.

2013-12-01

Space Weather and Global Climate Impacts are critical items on the present national and international science agendas. Understanding and forecasting solar activity is increasingly important for manned space flight, unmanned missions (including communications satellites, satellites that monitor the space and earth environment), and regional power grids. The ability to predict the effects of forcings and feedback mechanisms on global and local climate is critical to survival of the inhabitants of planet Earth. It is therefore important to motivate students to continue their studies via advanced degrees and pursue careers related to these areas. This CUNY-based initiative, supported by NASA and NSF, provided undergraduate research experience for more than 70 students in topics ranging from urban impacts of global climate change to magnetic rope structure, solar flares and CMEs. Other research topics included investigations of the ionosphere using a CubeSat, stratospheric aerosols in Jupiter's atmosphere, and ocean climate modeling. Mentors for the primarily summer research experiences included CUNY faculty, GISS and GSFC scientists. Students were recruited from CUNY colleges as well as other colleges including Spelman, Cornell, Rutgers and SUNY colleges. Fifty-eight percent of the undergraduate students were under-represented minorities and thirty-four percent were female. Many of the research teams included high school teachers and students as well as graduate students. Supporting workshops for students included data analysis and visualization tools, space weather, planetary energy balance and BalloonSats. The project is supported by NASA awards NNX10AE72G and NNX09AL77G, and NSF REU Site award 0851932.

Direct stratospheric injection of biomass burning emissions: a case study of the 2009 Australian bushfires using the NASA GISS ModelE2 composition-climate model

Science.gov (United States)

Field, Robert; From, Mike; Voulgarakis, Apostolos; Shindell, Drew; Flannigan, Mike; Bernath, Peter

2014-05-01

Direct stratospheric injection (DSI) of forest fire smoke represents a direct biogeochemical link between the land surface and stratosphere. DSI events occur regularly in the northern and southern extratropics, and have been observed across a wide range of measurements, but their fate and effects are not well understood. DSIs result from explosive, short-lived fires, and their plumes stand out from background concentrations immediately. This makes it easier to associate detected DSIs to individual fires and their estimated emissions. Because the emissions pulses are brief, chemical decay can be more clearly assessed, and because the emissions pulses are so large, a wide range of rare chemical species can be detected. Observational evidence suggests that they can persist in the stratosphere for several months, enhance ozone production, and be self-lofted to the middle stratosphere through shortwave absorption and diabatic heating. None of these phenomena have been evaluated, however, with a physical model. To that end, we are simulating the smoke plumes from the February 2009 Australia 'Black Saturday' bushfires using the NASA GISS ModelE2 composition-climate model, nudged toward horizontal winds from reanalysis. To-date, this is the best-observed DSI in the southern hemisphere. Chemical and aerosol signatures of the plume were observed in a wide array of limb and nadir satellite retrievals. Detailed estimates of fuel consumption and injection height have been made because of the severity of the fires. Uncommon among DSIs events was a large segment of the plume that entrained into the upper equatorial easterlies. Preliminary modeling results show that the relative strengths of the equatorial and extratropical plume segments are sensitive to the plume's initial injection height. This highlights the difficulty in reconciling uncertainty in the reanalysis over the Southern Hemisphere with fairly-well constrained estimates of fire location and injection height at the

Methodical Features of the Field Researches of the Anapa Bay-Bar Sediment Composition

Science.gov (United States)

Krylenko, Marina; Krylenko, Viacheslav; Gusakova, Anastasiya; Kosyan, Alisa

2014-05-01

Resort Anapa (Black Sea coast, Russia) holds leading positions in the Russian market of sanatorium-resort and children's recreation. The 50-200 m sandy beaches of Anapa bay-bar are the main value of the resort. Anapa bay-bar is an extensive accumulative sandy body having the length about 47 km. Obvious attributes of the beaches degradation demanding immediate measures on their protection and restoration are observed in last years. The main reason of degradation is beach material deficiency. To organize researches of the sediments of this extensive natural object is a difficult challenge. It is necessary to reduce number of tests to minimum. It is important to record differences of separate bay-bar sites and to receive comparable data for different seasons and years. Our researches showed that the grain-size sediment composition significantly depends of position on local relief. Consequently, researching of the alongshore change of the sediment size is effectual to realize at this morphological elements. Shelly detritus makes to 30% of total amount of beach sediments. It is necessary to consider that quantitative shell distribution along the coast significantly depends on a configuration of the coastline and an underwater relief. Quantity of the shells for cross-shore profile is maximal near coastline. For identification of the sediment sources and researching of their fluxes to use minerals markers (heavy minerals) is optimum. The maximum of heavy minerals concentration is characteristic for fraction 0.1-0.05mm at depth more 5 m. The maintenance of this fraction within other morphological zones isn't enough for the analysis or is excessively changeable. Use of the revealed features allowed to conduct the representative field researches of grain-size and mineral sediment composition for all morphological zones of underwater and coast part of the Anapa bay-bar. This methodic recommendations are workable for researches on others coast accumulative body. The work is

Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality

Czech Academy of Sciences Publication Activity Database

Somol, Petr; Novovičová, Jana

2010-01-01

Roč. 32, č. 11 (2010), s. 1921-1939 ISSN 0162-8828 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593; GA ČR GA102/07/1594 Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : feature selection * feature stability * stability measures * similarity measures * sequential search * individual ranking * feature subset-size optimization * high dimensionality * small sample size Subject RIV: BD - Theory of Information Impact factor: 5.027, year: 2010 http://library.utia.cas.cz/separaty/2010/RO/somol-0348726.pdf

Using beryllium-7 to assess cross-tropopause transport in global models

Energy Technology Data Exchange (ETDEWEB)

Liu, Hongyu [National Institute of Aerospace, Hampton, VA (United States); Considine, David B. [NASA Langley Research Center, Hampton, VA (United States); Horowitz, Larry W. [NOAA Geophysical Fluid and Dynamics Laboratory, Princeton, NJ (United States); and others

2016-07-01

We use the Global Modeling Initiative (GMI) modeling framework to assess the utility of cosmogenic beryllium-7 ({sup 7}Be), a natural aerosol tracer, for evaluating cross-tropopause transport in global models. The GMI chemical transport model (CTM) was used to simulate atmospheric {sup 7}Be distributions using four different meteorological data sets (GEOS1-STRAT DAS, GISS II{sup '} GCM, fvGCM, and GEOS4-DAS), featuring significantly different stratosphere-troposphere exchange (STE) characteristics. The simulations were compared with the upper troposphere and/or lower stratosphere (UT/LS) {sup 7}Be climatology constructed from ∝ 25 years of aircraft and balloon data, as well as climatological records of surface concentrations and deposition fluxes. Comparison of the fraction of surface air of stratospheric origin estimated from the {sup 7}Be simulations with observationally derived estimates indicates excessive cross-tropopause transport at mid-latitudes in simulations using GEOS1-STRAT and at high latitudes using GISS II{sup '} meteorological data. These simulations also overestimate {sup 7}Be deposition fluxes at mid-latitudes (GEOS1-STRAT) and at high latitudes (GISS II{sup '}), respectively. We show that excessive cross-tropopause transport of {sup 7}Be corresponds to overestimated stratospheric contribution to tropospheric ozone. Our perspectives on STE in these meteorological fields based on {sup 7}Be simulations are consistent with previous modeling studies of tropospheric ozone using the same meteorological fields. We conclude that the observational constraints for {sup 7}Be and observed {sup 7}Be total deposition fluxes can be used routinely as a first-order assessment of cross-tropopause transport in global models.

Research on Radar Micro-Doppler Feature Parameter Estimation of Propeller Aircraft

Science.gov (United States)

He, Zhihua; Tao, Feixiang; Duan, Jia; Luo, Jingsheng

2018-01-01

The micro-motion modulation effect of the rotated propellers to radar echo can be a steady feature for aircraft target recognition. Thus, micro-Doppler feature parameter estimation is a key to accurate target recognition. In this paper, the radar echo of rotated propellers is modelled and simulated. Based on which, the distribution characteristics of the micro-motion modulation energy in time, frequency and time-frequency domain are analyzed. The micro-motion modulation energy produced by the scattering points of rotating propellers is accumulated using the Inverse-Radon (I-Radon) transform, which can be used to accomplish the estimation of micro-modulation parameter. Finally, it is proved that the proposed parameter estimation method is effective with measured data. The micro-motion parameters of aircraft can be used as the features of radar target recognition.

Currency features for visually impaired people

Science.gov (United States)

Hyland, Sandra L.; Legge, Gordon E.; Shannon, Robert R.; Baer, Norbert S.

1996-03-01

The estimated 3.7 million Americans with low vision experience a uniquely difficult task in identifying the denominations of U.S. banknotes because the notes are remarkably uniform in size, color, and general design. The National Research Council's Committee on Currency Features Usable by the Visually Impaired assessed features that could be used by people who are visually disabled to distinguish currency from other documents and to denominate and authenticate banknotes using available technology. Variation of length and height, introduction of large numerals on a uniform, high-contrast background, use of different colors for each of the six denominations printed, and the introduction of overt denomination codes that could lead to development of effective, low-cost devices for examining banknotes were all deemed features available now. Issues affecting performance, including the science of visual and tactile perception, were addressed for these features, as well as for those features requiring additional research and development. In this group the committee included durable tactile features such as those printed with transparent ink, and the production of currency with holes to indicate denomination. Among long-range approaches considered were the development of technologically advanced devices and smart money.

Complex Topographic Feature Ontology Patterns

Science.gov (United States)

Varanka, Dalia E.; Jerris, Thomas J.

2015-01-01

Semantic ontologies are examined as effective data models for the representation of complex topographic feature types. Complex feature types are viewed as integrated relations between basic features for a basic purpose. In the context of topographic science, such component assemblages are supported by resource systems and found on the local landscape. Ontologies are organized within six thematic modules of a domain ontology called Topography that includes within its sphere basic feature types, resource systems, and landscape types. Context is constructed not only as a spatial and temporal setting, but a setting also based on environmental processes. Types of spatial relations that exist between components include location, generative processes, and description. An example is offered in a complex feature type ‘mine.’ The identification and extraction of complex feature types are an area for future research.

Using In Situ Observations and Satellite Retrievals to Constrain Large-Eddy Simulations and Single-Column Simulations: Implications for Boundary-Layer Cloud Parameterization in the NASA GISS GCM

Science.gov (United States)

Remillard, J.

2015-12-01

Two low-cloud periods from the CAP-MBL deployment of the ARM Mobile Facility at the Azores are selected through a cluster analysis of ISCCP cloud property matrices, so as to represent two low-cloud weather states that the GISS GCM severely underpredicts not only in that region but also globally. The two cases represent (1) shallow cumulus clouds occurring in a cold-air outbreak behind a cold front, and (2) stratocumulus clouds occurring when the region was dominated by a high-pressure system. Observations and MERRA reanalysis are used to derive specifications used for large-eddy simulations (LES) and single-column model (SCM) simulations. The LES captures the major differences in horizontal structure between the two low-cloud fields, but there are unconstrained uncertainties in cloud microphysics and challenges in reproducing W-band Doppler radar moments. The SCM run on the vertical grid used for CMIP-5 runs of the GCM does a poor job of representing the shallow cumulus case and is unable to maintain an overcast deck in the stratocumulus case, providing some clues regarding problems with low-cloud representation in the GCM. SCM sensitivity tests with a finer vertical grid in the boundary layer show substantial improvement in the representation of cloud amount for both cases. GCM simulations with CMIP-5 versus finer vertical gridding in the boundary layer are compared with observations. The adoption of a two-moment cloud microphysics scheme in the GCM is also tested in this framework. The methodology followed in this study, with the process-based examination of different time and space scales in both models and observations, represents a prototype for GCM cloud parameterization improvements.

Research on Methods for Discovering and Selecting Cloud Infrastructure Services Based on Feature Modeling

Directory of Open Access Journals (Sweden)

Huamin Zhu

2016-01-01

Full Text Available Nowadays more and more cloud infrastructure service providers are providing large numbers of service instances which are a combination of diversified resources, such as computing, storage, and network. However, for cloud infrastructure services, the lack of a description standard and the inadequate research of systematic discovery and selection methods have exposed difficulties in discovering and choosing services for users. First, considering the highly configurable properties of a cloud infrastructure service, the feature model method is used to describe such a service. Second, based on the description of the cloud infrastructure service, a systematic discovery and selection method for cloud infrastructure services are proposed. The automatic analysis techniques of the feature model are introduced to verify the model’s validity and to perform the matching of the service and demand models. Finally, we determine the critical decision metrics and their corresponding measurement methods for cloud infrastructure services, where the subjective and objective weighting results are combined to determine the weights of the decision metrics. The best matching instances from various providers are then ranked by their comprehensive evaluations. Experimental results show that the proposed methods can effectively improve the accuracy and efficiency of cloud infrastructure service discovery and selection.

Overhanging Features and the SLM/DMLS Residual Stresses Problem: Review and Future Research Need

Directory of Open Access Journals (Sweden)

Albert E. Patterson

2017-04-01

Full Text Available A useful and increasingly common additive manufacturing (AM process is the selective laser melting (SLM or direct metal laser sintering (DMLS process. SLM/DMLS can produce full-density metal parts from difficult materials, but it tends to suffer from severe residual stresses introduced during processing. This limits the usefulness and applicability of the process, particularly in the fabrication of parts with delicate overhanging and protruding features. The purpose of this study was to examine the current insight and progress made toward understanding and eliminating the problem in overhanging and protruding structures. To accomplish this, a survey of the literature was undertaken, focusing on process modeling (general, heat transfer, stress and distortion and material models, direct process control (input and environmental control, hardware-in-the-loop monitoring, parameter optimization and post-processing, experiment development (methods for evaluation, optical and mechanical process monitoring, imaging and design-of-experiments, support structure optimization and overhang feature design; approximately 143 published works were examined. The major findings of this study were that a small minority of the literature on SLM/DMLS deals explicitly with the overhanging stress problem, but some fundamental work has been done on the problem. Implications, needs and potential future research directions are discussed in-depth in light of the present review.

Simultaneous Channel and Feature Selection of Fused EEG Features Based on Sparse Group Lasso

Directory of Open Access Journals (Sweden)

Jin-Jia Wang

2015-01-01

Full Text Available Feature extraction and classification of EEG signals are core parts of brain computer interfaces (BCIs. Due to the high dimension of the EEG feature vector, an effective feature selection algorithm has become an integral part of research studies. In this paper, we present a new method based on a wrapped Sparse Group Lasso for channel and feature selection of fused EEG signals. The high-dimensional fused features are firstly obtained, which include the power spectrum, time-domain statistics, AR model, and the wavelet coefficient features extracted from the preprocessed EEG signals. The wrapped channel and feature selection method is then applied, which uses the logistical regression model with Sparse Group Lasso penalized function. The model is fitted on the training data, and parameter estimation is obtained by modified blockwise coordinate descent and coordinate gradient descent method. The best parameters and feature subset are selected by using a 10-fold cross-validation. Finally, the test data is classified using the trained model. Compared with existing channel and feature selection methods, results show that the proposed method is more suitable, more stable, and faster for high-dimensional feature fusion. It can simultaneously achieve channel and feature selection with a lower error rate. The test accuracy on the data used from international BCI Competition IV reached 84.72%.

Impact of improved Greenland ice sheet surface representation in the NASA GISS ModelE2 GCM on simulated surface mass balance and regional climate

Science.gov (United States)

Alexander, P. M.; LeGrande, A. N.; Fischer, E.; Tedesco, M.; Kelley, M.; Schmidt, G. A.; Fettweis, X.

2017-12-01

Towards achieving coupled simulations between the NASA Goddard Institute for Space Studies (GISS) ModelE2 general circulation model (GCM) and ice sheet models (ISMs), improvements have been made to the representation of the ice sheet surface in ModelE2. These include a sub-grid-scale elevation class scheme, a multi-layer snow model, a time-variable surface albedo scheme, and adjustments to parameterization of sublimation/evaporation. These changes improve the spatial resolution and physical representation of the ice sheet surface such that the surface is represented at a level of detail closer to that of Regional Climate Models (RCMs). We assess the impact of these changes on simulated Greenland Ice Sheet (GrIS) surface mass balance (SMB). We also compare ModelE2 simulations in which winds have been nudged to match the European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis with simulations from the Modèle Atmosphérique Régionale (MAR) RCM forced by the same reanalysis. Adding surface elevation classes results in a much higher spatial resolution representation of the surface necessary for coupling with ISMs, but has a negligible impact on overall SMB. Implementing a variable surface albedo scheme increases melt by 100%, bringing it closer to melt simulated by MAR. Adjustments made to the representation of topography-influenced surface roughness length in ModelE2 reduce a positive bias in evaporation relative to MAR. We also examine the impact of changes to the GrIS surface on regional atmospheric and oceanic climate in coupled ocean-atmosphere simulations with ModelE2, finding a general warming of the Arctic due to a warmer GrIS, and a cooler North Atlantic in scenarios with doubled atmospheric CO2 relative to pre-industrial levels. The substantial influence of changes to the GrIS surface on the oceans and atmosphere highlight the importance of including these processes in the GCM, in view of potential feedbacks between the ice sheet

A Research on Fast Face Feature Points Detection on Smart Mobile Devices

Directory of Open Access Journals (Sweden)

Xiaohe Li

2018-01-01

Full Text Available We explore how to leverage the performance of face feature points detection on mobile terminals from 3 aspects. First, we optimize the models used in SDM algorithms via PCA and Spectrum Clustering. Second, we propose an evaluation criterion using Linear Discriminative Analysis to choose the best local feature descriptions which plays a critical role in feature points detection. Third, we take advantage of multicore architecture of mobile terminal and parallelize the optimized SDM algorithm to improve the efficiency further. The experiment observations show that our final accomplished GPC-SDM (improved Supervised Descent Method using spectrum clustering, PCA, and GPU acceleration suppresses the memory usage, which is beneficial and efficient to meet the real-time requirements.

Research on improving image recognition robustness by combining multiple features with associative memory

Science.gov (United States)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

Feature selection toolbox software package

Czech Academy of Sciences Publication Activity Database

Pudil, Pavel; Novovičová, Jana; Somol, Petr

2002-01-01

Roč. 23, č. 4 (2002), s. 487-492 ISSN 0167-8655 R&D Projects: GA ČR GA402/01/0981 Institutional research plan: CEZ:AV0Z1075907 Keywords : pattern recognition * feature selection * loating search algorithms Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.409, year: 2002

GISS Surface Temperature Analysis

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The GISTEMP dataset is a global 2x2 gridded temperature anomaly dataset. Temperature data is updated around the middle of every month using current data files from...

Online feature selection with streaming features.

Science.gov (United States)

Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

2013-05-01

We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

Celluloid angels: a research study of nurses in feature films 1900-2007.

Science.gov (United States)

Stanley, David J

2008-10-01

This paper is a report of a study examining the influence on how nursing and nurses are portrayed in feature films made between 1900 and 2007, with a nurse as their main or a principle character and a story-line related specifically to nursing. Nurses and the nursing profession are frequently portrayed negatively or stereotypically in the media, with nurses often being portrayed as feminine and caring but not as leaders or professionals capable of autonomous practice. A mixed method approach was used to examine feature films made in the Western world. Over 36,000 feature film synopses were reviewed (via CINAHL, ProQuest and relevant movie-specific literature) for the keywords 'nurse'/'nursing'. Identified films were analysed quantitatively to determine their country of production, genre, plot(s) and other relevant data, and qualitatively to identify the emergence of themes related to the image of nurses/nursing in films. For the period from 1900 to 2007, 280 relevant feature films were identified. Most films were made in the United States of America or United Kingdom, although in recent years films have been increasingly produced in other countries. Early films portrayed nurses as self-sacrificial heroines, sex objects and romantics. More recent films increasingly portray them as strong and self-confident, professionals. Nurse-related films offer a unique insight into the image of nurses and how they have been portrayed. Nurses need to be aware of the impact the film industry has on how nurses and nursing are perceived and represented in feature films.

Understanding Legacy Features with Featureous

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2011-01-01

Java programs called Featureous that addresses this issue. Featureous allows a programmer to easily establish feature-code traceability links and to analyze their characteristics using a number of visualizations. Featureous is an extension to the NetBeans IDE, and can itself be extended by third...

The Dissociative Subtype of Post-traumatic Stress Disorder: Research Update on Clinical and Neurobiological Features.

Science.gov (United States)

van Huijstee, Jytte; Vermetten, Eric

2017-10-21

Recently, a dissociative subtype of post-traumatic stress disorder (PTSD) has been included in the DSM-5. This review focuses on the clinical and neurobiological features that distinguish the dissociative subtype of PTSD from non-dissociative PTSD. Clinically, the dissociative subtype of PTSD is associated with high PTSD severity, predominance of derealization and depersonalization symptoms, a more significant history of early life trauma, and higher levels of comorbid psychiatric disorders. Furthermore, PTSD patients with dissociative symptoms exhibit different psychophysiological and neural responses to the recall of traumatic memories. While individuals with non-dissociative PTSD exhibit an increased heart rate, decreased activation of prefrontal regions, and increased activation of the amygdala in response to traumatic reminders, individuals with the dissociative subtype of PTSD show an opposite pattern. It has been proposed that dissociation is a regulatory strategy to restrain extreme arousal in PTSD through hyperinhibition of limbic regions. In this research update, promises and pitfalls in current research studies on the dissociative subtype of PTSD are listed. Inclusion of the dissociative subtype of PTSD in the DSM-5 stimulates research on the prevalence, symptomatology, and neurobiology of the dissociative subtype of PTSD and poses a challenge to improve treatment outcome in PTSD patients with dissociative symptoms.

Internal versus external features in triggering the brain waveforms for conjunction and feature faces in recognition.

Science.gov (United States)

Nie, Aiqing; Jiang, Jingguo; Fu, Qiao

2014-08-20

Previous research has found that conjunction faces (whose internal features, e.g. eyes, nose, and mouth, and external features, e.g. hairstyle and ears, are from separate studied faces) and feature faces (partial features of these are studied) can produce higher false alarms than both old and new faces (i.e. those that are exactly the same as the studied faces and those that have not been previously presented) in recognition. The event-related potentials (ERPs) that relate to conjunction and feature faces at recognition, however, have not been described as yet; in addition, the contributions of different facial features toward ERPs have not been differentiated. To address these issues, the present study compared the ERPs elicited by old faces, conjunction faces (the internal and the external features were from two studied faces), old internal feature faces (whose internal features were studied), and old external feature faces (whose external features were studied) with those of new faces separately. The results showed that old faces not only elicited an early familiarity-related FN400, but a more anterior distributed late old/new effect that reflected recollection. Conjunction faces evoked similar late brain waveforms as old internal feature faces, but not to old external feature faces. These results suggest that, at recognition, old faces hold higher familiarity than compound faces in the profiles of ERPs and internal facial features are more crucial than external ones in triggering the brain waveforms that are characterized as reflecting the result of familiarity.

Prominent feature extraction for review analysis: an empirical study

Science.gov (United States)

Agarwal, Basant; Mittal, Namita

2016-05-01

Sentiment analysis (SA) research has increased tremendously in recent times. SA aims to determine the sentiment orientation of a given text into positive or negative polarity. Motivation for SA research is the need for the industry to know the opinion of the users about their product from online portals, blogs, discussion boards and reviews and so on. Efficient features need to be extracted for machine-learning algorithm for better sentiment classification. In this paper, initially various features are extracted such as unigrams, bi-grams and dependency features from the text. In addition, new bi-tagged features are also extracted that conform to predefined part-of-speech patterns. Furthermore, various composite features are created using these features. Information gain (IG) and minimum redundancy maximum relevancy (mRMR) feature selection methods are used to eliminate the noisy and irrelevant features from the feature vector. Finally, machine-learning algorithms are used for classifying the review document into positive or negative class. Effects of different categories of features are investigated on four standard data-sets, namely, movie review and product (book, DVD and electronics) review data-sets. Experimental results show that composite features created from prominent features of unigram and bi-tagged features perform better than other features for sentiment classification. mRMR is a better feature selection method as compared with IG for sentiment classification. Boolean Multinomial Naïve Bayes) algorithm performs better than support vector machine classifier for SA in terms of accuracy and execution time.

Characters Feature Extraction Based on Neat Oracle Bone Rubbings

OpenAIRE

Lei Guo

2013-01-01

In order to recognize characters on the neat oracle bone rubbings, a new mesh point feature extraction algorithm was put forward in this paper by researching and improving of the existing coarse mesh feature extraction algorithm and the point feature extraction algorithm. Some improvements of this algorithm were as followings: point feature was introduced into the coarse mesh feature, the absolute address was converted to relative address, and point features have been changed grid and positio...

Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals.

Science.gov (United States)

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.

Multimodal Feature Learning for Video Captioning

Directory of Open Access Journals (Sweden)

Sujin Lee

2018-01-01

Full Text Available Video captioning refers to the task of generating a natural language sentence that explains the content of the input video clips. This study proposes a deep neural network model for effective video captioning. Apart from visual features, the proposed model learns additionally semantic features that describe the video content effectively. In our model, visual features of the input video are extracted using convolutional neural networks such as C3D and ResNet, while semantic features are obtained using recurrent neural networks such as LSTM. In addition, our model includes an attention-based caption generation network to generate the correct natural language captions based on the multimodal video feature sequences. Various experiments, conducted with the two large benchmark datasets, Microsoft Video Description (MSVD and Microsoft Research Video-to-Text (MSR-VTT, demonstrate the performance of the proposed model.

Nonmotor Features in Atypical Parkinsonism.

Science.gov (United States)

Bhatia, Kailash P; Stamelou, Maria

2017-01-01

Atypical parkinsonism (AP) comprises mainly multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD), which are distinct pathological entities, presenting with a wide phenotypic spectrum. The classic syndromes are now called MSA-parkinsonism (MSA-P), MSA-cerebellar type (MSA-C), Richardson's syndrome, and corticobasal syndrome. Nonmotor features in AP have been recognized almost since the initial description of these disorders; however, research has been limited. Autonomic dysfunction is the most prominent nonmotor feature of MSA, but also gastrointestinal symptoms, sleep dysfunction, and pain, can be a feature. In PSP and CBD, the most prominent nonmotor symptoms comprise those deriving from the cognitive/neuropsychiatric domain. Apart from assisting the clinician in the differential diagnosis with Parkinson's disease, nonmotor features in AP have a big impact on quality of life and prognosis of AP and their treatment poses a major challenge for clinicians. © 2017 Elsevier Inc. All rights reserved.

Realistic Free-Spins Features Increase Preference for Slot Machines.

Science.gov (United States)

Taylor, Lorance F; Macaskill, Anne C; Hunt, Maree J

2017-06-01

Despite increasing research into how the structural characteristics of slot machines influence gambling behaviour there have been no experimental investigations into the effect of free-spins bonus features-a structural characteristic that is commonly central to the design of slot machines. This series of three experiments investigated the free-spins feature using slot machine simulations to determine whether participants allocate more wagers to a machine with free spins, and, which components of free-spins features drive this preference. In each experiment, participants were exposed to two computer-simulated slot machines-one with a free-spins feature or similar bonus feature and one without. Participants then completed a testing phase where they could freely switch between the two machines. In Experiment 1, participants did not prefer the machine with a simple free-spins feature. In Experiment 2 the free-spins feature incorporated additional elements such as sounds, animations, and an increased win frequency; participants preferred to gamble on this machine. The Experiment 3 "bonus feature" machine resembled the free spins machine in Experiment 2 except spins were not free; participants showed a clear preference for this machine also. These findings indicate that (1) free-spins features have a major influence over machine choice and (2) the "freeness" of the free-spins bonus features is not an important driver of preference, contrary to self-report and interview research with gamblers.

The Research and Application of SURF Algorithm Based on Feature Point Selection Algorithm

Directory of Open Access Journals (Sweden)

Zhang Fang Hu

2014-04-01

Full Text Available As the pixel information of depth image is derived from the distance information, when implementing SURF algorithm with KINECT sensor for static sign language recognition, there can be some mismatched pairs in palm area. This paper proposes a feature point selection algorithm, by filtering the SURF feature points step by step based on the number of feature points within adaptive radius r and the distance between the two points, it not only greatly improves the recognition rate, but also ensures the robustness under the environmental factors, such as skin color, illumination intensity, complex background, angle and scale changes. The experiment results show that the improved SURF algorithm can effectively improve the recognition rate, has a good robustness.

Features of the Manufacturing Vision Development Process

DEFF Research Database (Denmark)

Dukovska-Popovska, Iskra; Riis, Jens Ove; Boer, Harry

2005-01-01

of action research. The methodology recommends wide participation of people from different hierarchical and functional positions, who engage in a relatively short, playful and creative process and come up with a vision (concept) for the future manufacturing system in the company. Based on three case studies......This paper discusses the key features of the process of Manufacturing Vision Development, a process that enables companies to develop their future manufacturing concept. The basis for the process is a generic five-phase methodology (Riis and Johansen, 2003) developed as a result of ten years...... of companies going through the initial phases of the methodology, this research identified the key features of the Manufacturing Vision Development process. The paper elaborates the key features by defining them, discussing how and when they can appear, and how they influence the process....

Deep Complementary Bottleneck Features for Visual Speech Recognition

NARCIS (Netherlands)

Petridis, Stavros; Pantic, Maja

Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

A study on feature analysis for musical instrument classification.

Science.gov (United States)

Deng, Jeremiah D; Simmermacher, Christian; Cranefield, Stephen

2008-04-01

In tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper, we present an empirical study on feature analysis for recognition of classical instrument, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.

Integrated Phoneme Subspace Method for Speech Feature Extraction

Directory of Open Access Journals (Sweden)

Park Hyunsin

2009-01-01

Full Text Available Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA, independent component analysis (ICA, and linear discriminant analysis (LDA. Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

Process Features in Writing: Internal Structure and Incremental Value over Product Features. Research Report. ETS RR-15-27

Science.gov (United States)

Zhang, Mo; Deane, Paul

2015-01-01

In educational measurement contexts, essays have been evaluated and formative feedback has been given based on the end product. In this study, we used a large sample collected from middle school students in the United States to investigate the factor structure of the writing process features gathered from keystroke logs and the association of that…

Evaluation of methane emissions from West Siberian wetlands based on inverse modeling

Energy Technology Data Exchange (ETDEWEB)

Kim, H-S; Inoue, G [Research Institute for Humanity and Nature, 457-4 Motoyama, Kamigamo, Kita-ku, Kyoto 603-8047 (Japan); Maksyutov, S; Machida, T [National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 (Japan); Glagolev, M V [Lomonosov Moscow State University, GSP-1, Leninskie Gory, Moscow 119991 (Russian Federation); Patra, P K [Research Institute for Global Change/JAMSTEC, 3173-25 Showa-cho, Kanazawa-ku, Yokohama, Kanagawa 236-0001 (Japan); Sudo, K, E-mail: heonsook.kim@gmail.com [Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601 (Japan)

2011-07-15

West Siberia contains the largest extent of wetlands in the world, including large peat deposits; the wetland area is equivalent to 27% of the total area of West Siberia. This study used inverse modeling to refine emissions estimates for West Siberia using atmospheric CH{sub 4} observations and two wetland CH{sub 4} emissions inventories: (1) the global wetland emissions dataset of the NASA Goddard Institute for Space Studies (the GISS inventory), which includes emission seasons and emission rates based on climatology of monthly surface air temperature and precipitation, and (2) the West Siberian wetland emissions data (the Bc7 inventory), based on in situ flux measurements and a detailed wetland classification. The two inversions using the GISS and Bc7 inventories estimated annual mean flux from West Siberian wetlands to be 2.9 {+-} 1.7 and 3.0 {+-} 1.4 Tg yr{sup -1}, respectively, which are lower than the 6.3 Tg yr{sup -1} predicted in the GISS inventory, but similar to those of the Bc7 inventory (3.2 Tg yr{sup -1}). The well-constrained monthly fluxes and a comparison between the predicted CH{sub 4} concentrations in the two inversions suggest that the Bc7 inventory predicts the seasonal cycle of West Siberian wetland CH{sub 4} emissions more reasonably, indicating that the GISS inventory predicts more emissions from wetlands in northern and middle taiga.

Research on Remote Sensing Image Classification Based on Feature Level Fusion

Science.gov (United States)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Graphical matching rules for cardinality based service feature diagrams

Directory of Open Access Journals (Sweden)

Faiza Kanwal

2017-03-01

Full Text Available To provide efficient services to end-users, variability and commonality among the features of the product line is a challenge for industrialist and researchers. Feature modeling provides great services to deal with variability and commonality among the features of product line. Cardinality based service feature diagrams changed the basic framework of service feature diagrams by putting constraints to them, which make service specifications more flexible, but apart from their variation in selection third party services may have to be customizable. Although to control variability, cardinality based service feature diagrams provide high level visual notations. For specifying variability, the use of cardinality based service feature diagrams raises the problem of matching a required feature diagram against the set of provided diagrams.

Turkish Music Genre Classification using Audio and Lyrics Features

Directory of Open Access Journals (Sweden)

Önder ÇOBAN

2017-05-01

Full Text Available Music Information Retrieval (MIR has become a popular research area in recent years. In this context, researchers have developed music information systems to find solutions for such major problems as automatic playlist creation, hit song detection, and music genre or mood classification. Meta-data information, lyrics, or melodic content of music are used as feature resource in previous works. However, lyrics do not often used in MIR systems and the number of works in this field is not enough especially for Turkish. In this paper, firstly, we have extended our previously created Turkish MIR (TMIR dataset, which comprises of Turkish lyrics, by including the audio file of each song. Secondly, we have investigated the effect of using audio and textual features together or separately on automatic Music Genre Classification (MGC. We have extracted textual features from lyrics using different feature extraction models such as word2vec and traditional Bag of Words. We have conducted our experiments on Support Vector Machine (SVM algorithm and analysed the impact of feature selection and different feature groups on MGC. We have considered lyrics based MGC as a text classification task and also investigated the effect of term weighting method. Experimental results show that textual features can also be effective as well as audio features for Turkish MGC, especially when a supervised term weighting method is employed. We have achieved the highest success rate as 99,12\\% by using both audio and textual features together.

Professional activity of the researcher in the field of education: features of planning,resources for implementation, satisfaction with the results

Directory of Open Access Journals (Sweden)

Vladimir S. Sobkin

2017-12-01

Full Text Available Background. The paper examines the attitude of scientists engaged in research in the field of education to various aspects of professional activity: the features of planning, resources for implementation, and satisfaction with the results. The relevance of the study is due to a number of institutional changes in the national science. Thus, the active reform initiated in 2013 was aimed at optimizing and increasing the efficiency of various research institutes, primarily those within the structure of state academies. In this regard, it seems important to identify the scientists’ attitude to the results of the implemented initiatives within the period of the last four years. Objective. The paper is to analyze the influence of age and social indicators of professional status (academic degree, academic title, position held, publication activity on various aspects of the professional activity of the researches. Design and sample characteristics. A special questionnaire of 72 questions was developed (closed, open and scale, 721 respondents were interviewed. The sample included employees with different levels of scientific qualifications and length of professional scientific activity. Both employees of scientific research institutes and universities from different regions of the Russian Federation were interviewed. Results. The results show the manifestation of negative tendencies concerning the planning of scientific activity related to its authoritarian nature and formal requirements for reporting on the results of scientific activity. The peculiarities of well-being of scientific employees at the stage of completing a professional career are revealed. The features of the manifestation of the professional crisis, which is characteristic for the age cohort of forty-year scientific workers, are considered. The specifics of attracting personal funds and additional financial sources depending on various indicators of the professional status of the

Do responses to different anthropogenic forcings add linearly in climate models?

International Nuclear Information System (INIS)

Marvel, Kate; Schmidt, Gavin A; LeGrande, Allegra N; Nazarenko, Larissa; Shindell, Drew; Bonfils, Céline; Tsigaridis, Kostas

2015-01-01

Many detection and attribution and pattern scaling studies assume that the global climate response to multiple forcings is additive: that the response over the historical period is statistically indistinguishable from the sum of the responses to individual forcings. Here, we use the NASA Goddard Institute for Space Studies (GISS) and National Center for Atmospheric Research Community Climate System Model (CCSM4) simulations from the CMIP5 archive to test this assumption for multi-year trends in global-average, annual-average temperature and precipitation at multiple timescales. We find that responses in models forced by pre-computed aerosol and ozone concentrations are generally additive across forcings. However, we demonstrate that there are significant nonlinearities in precipitation responses to different forcings in a configuration of the GISS model that interactively computes these concentrations from precursor emissions. We attribute these to differences in ozone forcing arising from interactions between forcing agents. Our results suggest that attribution to specific forcings may be complicated in a model with fully interactive chemistry and may provide motivation for other modeling groups to conduct further single-forcing experiments. (letter)

How do personality features and skills affect entrepreneurship?

Directory of Open Access Journals (Sweden)

Malakshah Karimpoor Abdolreza

2017-01-01

Full Text Available Current research aimed to review impact of personality and psychological features on entrepreneurship skills of employees. The research has an applied objective where data were gathered in cross-sectional form. Statistical population is all employees in the Tejarat Bank in one of western cities of Iran who are 250 persons. To determine the size of the sample according to Morgan table, 152 people were determined. Sampling method is simple random sampling. Two questionnaires were used to collect data. To measure validity of the questionnaire, formal validity was used and to measure reliability Cronbach Alpha coefficient was used applying SPSS version 19. R=0/860 was calculated for questionnaire of personality and psychological features and 0/843 was obtained for entrepreneurship skills questionnaire. For data analysis, descriptive and inferential statistics (Kolmogorov-Smirnov test and binomial test were used. The results of the research indicate that personality and psychological features of employees are effective in their entrepreneurship skills in working place. So, at the end some suggestions were presented for improving entrepreneurial skills of employees.

Upgrading of analytical method of general sensitivity for feature evaluation of land disposal system. 3. Abstract of report on research entrusted by the Japan Nuclear Cycle Development Institute

International Nuclear Information System (INIS)

1999-02-01

In this research, a method to evaluate features of searching type system was analyzed statistically and deterministically by expanding it to nuclide transport in near-field host rocks. Here was described on abstract of methodology by summarizing fundamental concept on procedure and shadow-model of the feature evaluation based on investigation progress in last fiscal year, details on the nuclide transport in near-field host rocks and analytical method of sensitivity using this research, contents of statistical analysis using the shadow-model of nuclide transport, and summaries of contents and results on the deterministic analysis. (G.K.)

Towards the maturity model for feature oriented domain analysis

Directory of Open Access Journals (Sweden)

Muhammad Javed

2014-09-01

Full Text Available Assessing the quality of a model has always been a challenge for researchers in academia and industry. The quality of a feature model is a prime factor because it is used in the development of products. A degraded feature model leads the development of low quality products. Few efforts have been made on improving the quality of feature models. This paper is an effort to present our ongoing work i.e. development of FODA (Feature Oriented Domain Analysis maturity model which will help to evaluate the quality of a given feature model. In this paper, we provide the quality levels along with their descriptions. The proposed model consists of four levels starting from level 0 to level 3. Design of each level is based on the severity of errors, whereas severity of errors decreases from level 0 to level 3. We elaborate each level with the help of examples. We borrowed all examples from the material published by the research community of Software Product Lines (SPL for the application of our framework.

Geographic Information Systems: Tools for Displaying In-Library Use Data

Directory of Open Access Journals (Sweden)

Lauren H. Mandel

2010-03-01

Full Text Available In-library use data is crucial for modern libraries to understand the full spectrum of patron use, including patron self-service activities, circulation, and reference statistics. Rather than using tables and charts to display use data, a geographic information system (GIS facilitates a more visually appealing graphical display of the data in the form of a map. GISs have been used by library and information science (LIS researchers and practitioners to create maps that display analyses of service area populations and demographics, facilities space management issues, spatial distribution of in-library use of materials, planned branch consolidations, and so on. The “seating sweeps” method allows researchers and librarians to collect in-library use data regarding where patrons are locating themselves within the library and what they are doing at those locations, such as sitting and reading, studying in a group, or socializing. This paper proposes a GIS as a tool to visually display in-library use data collected via “seating sweeps” of a library. By using a GIS to store, manage, and display the data, researchers and librarians can create visually appealing maps that show areas of heavy use and evidence of the use and value of the library for a community. Example maps are included to facilitate the reader’s understanding of the possibilities afforded by using GISs in LIS research.

Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection.

Science.gov (United States)

Kipli, Kuryati; Kouzani, Abbas Z

2015-07-01

Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression. A feature selection (FS) algorithm called degree of contribution (DoC) is developed for selection of sMRI volumetric features. This algorithm uses an ensemble approach to determine the degree of contribution in detection of major depressive disorder. The DoC is the score of feature importance used for feature ranking. The algorithm involves four stages: feature ranking, subset generation, subset evaluation, and DoC analysis. The performance of DoC is evaluated on the Duke University Multi-site Imaging Research in the Analysis of Depression sMRI dataset. The dataset consists of 115 brain sMRI scans of 88 healthy controls and 27 depressed subjects. Forty-four sMRI volumetric features are used in the evaluation. The DoC score of forty-four features was determined as the accuracy threshold (Acc_Thresh) was varied. The DoC performance was compared with that of four existing FS algorithms. At all defined Acc_Threshs, DoC outperformed the four examined FS algorithms for the average classification score and the maximum classification score. DoC has a good ability to generate reduced-size subsets of important features that could yield high classification accuracy. Based on the DoC score, the most discriminant volumetric features are those from the left-brain region.

Assessing ocean vertical mixing schemes for the study of climate change

Science.gov (United States)

Howard, A. M.; Lindo, F.; Fells, J.; Tulsee, V.; Cheng, Y.; Canuto, V.

2014-12-01

understanding and prediction of climate. The PI is both a member of the turbulence group at NASA-GISS and an associate professor at Medgar Evers College of CUNY, a minority serving institution in an urban setting in central Brooklyn. This Project is supported by NSF award AGS-1359293 REU site: CUNY/GISS Center for Global Climate Research.

Parabolic Dunes Landform Features of Iowa

Data.gov (United States)

Iowa State University GIS Support and Research Facility — A landscape is a collection of land shapes or land forms. Landform Regions are a grouping of individual landscape features that have a common geomophology. In Iowa,...

Lineated Inliers Landform Features of Iowa

Data.gov (United States)

Iowa State University GIS Support and Research Facility — A landscape is a collection of land shapes or land forms. Landform regions are a grouping of individual landscape features that have a common geomophology. In Iowa,...

Paha Ridges Landform Features of Iowa

Data.gov (United States)

Iowa State University GIS Support and Research Facility — A landscape is a collection of land shapes or land forms. Landform regions are a grouping of individual landscape features that have a common geomophology. In Iowa,...

Lineated Ridges Landform Features of Iowa

Data.gov (United States)

Iowa State University GIS Support and Research Facility — A landscape is a collection of land shapes or land forms. Landform Regions are a grouping of individual landscape features that have a common geomophology. In Iowa,...

Strategy of Trade-Reliable Featured Product Supporting Regional Innovation Systems

Science.gov (United States)

Riskiawan, H. Y.; Purnomo, B. H.; Abdurahman, A.; Hariono, B.; Puspitasari, T. D.

2018-01-01

Pacitan, Ponorogo, and Magetan had planned the development of featured products as contained in the Medium Term Development Plan (MTDP) until 2020. The focus of development is almost similar to featured products derived from agribusiness, food processing, handycrafts, and tourism. The geographical proximity results characteristics of natural resources and social culture have similarities, including the type of featured products, constraints, problems, and opportunities for development. Given the characteristics and the support system of some featured products contained in these three regions have a lot in common and their functional interactions involving actors from across the region, it is necessary to develop cross-jurisdictional policy. The resulting strategy should be able to support the development of Regional Innovation System (RIS). The purpose of this research is 1) Determining featured product cross-regional between Pacitan regency; Ponorogo and Magetan districts in support of RIS development; and 2) Designing a featured product development strategy using supply chain management in order to drive the local economy. Based on the results of research conducted, featured products across the region that have potentiality to be developed are: processed products of “janggelan” leather products, and woven bamboo.

Key Features of the Manufacturing Vision Development Process

DEFF Research Database (Denmark)

Dukovska-Popovska, Iskra; Riis, Jens Ove; Boer, Harry

2005-01-01

of action research. The methodology recommends wide participation of people from different hierarchical and functional positions, who engage in a relatively short, playful and creative process and come up with a vision (concept) for the future manufacturing system in the company. Based on three case studies......This paper discusses the key features of the process of Manufacturing Vision Development, a process that enables companies to develop their future manufacturing concept. The basis for the process is a generic five-phase methodology (Riis and Johansen 2003) developed as a result of ten years...... of companies going through the initial phases of the methodology, this research identified the key features of the Manufacturing Vision Development process. The paper elaborates the key features by defining them, discussing how and when they can appear, and how they influence the process....

EXPERIMENTAL RESEARCH OF REGENERATIVE FEATURES IN BONE TISSUES AROUND IMPLANTS AFTER ONE-STAGE BILATERAL TOTAL HIP REPLACEMENT

Directory of Open Access Journals (Sweden)

V. M. Mashkov

2012-01-01

Full Text Available Objective: to research the specific features of regenerative processes of bone tissue around implants after one-stage bilateral total hip replacement in experiment. Material and methods: 27 total hip replacement operations have been performed in 18 rabbits of breed "chinchilla" to which bipolar femoral endoprosthesis made of titanic alloy PT-38, one type-size, with friction pair metal-on-metal and neck-shaft angle 165 degrees have been implanted: total unilateral hip replacement operations have been performed in 9 animals (control group, one-stage bilateral total hip replacement operations have been performed in 9 animals (experimental group. During research they have been on radiological and clinical checking-up. After the experiment the animals had histological tests of the tissues around endoprosthesis components. Results and conclusions: After one-stage bilateral total hip replacement in early terms of research more expressed changes of bone tissue in the form of its thinning and decompaction were found around implants. One-stage bilateral total hip replacement did not essentially influence on the speed of osteogenesis around endoprothesis components in comparison with unilateral total hip replacement, so in late terms of observation in both groups the fixing of endoprothesis components did not differ.

The Catchment Feature Model: A Device for Multimodal Fusion and a Bridge between Signal and Sense

Science.gov (United States)

Quek, Francis

2004-12-01

The catchment feature model addresses two questions in the field of multimodal interaction: how we bridge video and audio processing with the realities of human multimodal communication, and how information from the different modes may be fused. We argue from a detailed literature review that gestural research has clustered around manipulative and semaphoric use of the hands, motivate the catchment feature model psycholinguistic research, and present the model. In contrast to "whole gesture" recognition, the catchment feature model applies a feature decomposition approach that facilitates cross-modal fusion at the level of discourse planning and conceptualization. We present our experimental framework for catchment feature-based research, cite three concrete examples of catchment features, and propose new directions of multimodal research based on the model.

Classification of Broken Rice Kernels using 12D Features

Directory of Open Access Journals (Sweden)

SUNDER ALI KHOWAJA

2016-07-01

Full Text Available Integrating the technological aspect for assessment of rice quality is very much needed for the Asian markets where rice is one of the major exports. Methods based on image analysis has been proposed for automated quality assessment by taking into account some of the textural features. These features are good at classifying when rice grains are scanned in controlled environment but it is not suitable for practical implementation. Rice grains are placed randomly on the scanner which neither maintains the uniformity in intensity regions nor the placement strategy is kept ideal thus resulting in false classification of grains. The aim of this research is to propose a method for extracting set of features which can overcome the said issues. This paper uses morphological features along-with gray level and Hough transform based features to overcome the false classification in the existing methods. RBF (Radial Basis function is used as a classification mechanism to classify between complete grains and broken grains. Furthermore the broken grains are classified into two classes? i.e. acceptable grains and non-acceptable grains. This research also uses image enhancement technique prior to the feature extraction and classification process based on top-hat transformation. The proposed method has been simulated in MATLAB to visually analyze and validate the results.

Rhetorical Features of the Company Website

DEFF Research Database (Denmark)

Nielsen, Anne Ellerup

2002-01-01

will discuss the functional and the compositional aspects of corporate communication on the World Wide Web by comparing company websites with traditional market communication media. I will focus on linguistic and visual features of the company website and briefly account for some of the media constraints......Recent years have seen a growing body of literature ceoncerned with the World Wide Web as a new form of communication, and numerous discussions on composition, structure and design of successful company websites are being held in all kinds of forums within and outside the Internet. However, most...... these discussions seem to focus on the technological properties of the Internet or tend to serve purely practical purposes and only few researchers discuss the rhetorical features of web communication, the exception being a litited number of researchers dealing with metaphors on the Web. In this paper I...

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

Directory of Open Access Journals (Sweden)

Marc G. Berman

2017-04-01

Full Text Available Previous research has investigated ways to quantify visual information of a scene in terms of a visual processing hierarchy, i.e., making sense of visual environment by segmentation and integration of elementary sensory input. Guided by this research, studies have developed categories for low-level visual features (e.g., edges, colors, high-level visual features (scene-level entities that convey semantic information such as objects, and how models of those features predict aesthetic preference and naturalness. For example, in Kardan et al. (2015a, 52 participants provided aesthetic preference and naturalness ratings, which are used in the current study, for 307 images of mixed natural and urban content. Kardan et al. (2015a then developed a model using low-level features to predict aesthetic preference and naturalness and could do so with high accuracy. What has yet to be explored is the ability of higher-level visual features (e.g., horizon line position relative to viewer, geometry of building distribution relative to visual access to predict aesthetic preference and naturalness of scenes, and whether higher-level features mediate some of the association between the low-level features and aesthetic preference or naturalness. In this study we investigated these relationships and found that low- and high- level features explain 68.4% of the variance in aesthetic preference ratings and 88.7% of the variance in naturalness ratings. Additionally, several high-level features mediated the relationship between the low-level visual features and aaesthetic preference. In a multiple mediation analysis, the high-level feature mediators accounted for over 50% of the variance in predicting aesthetic preference. These results show that high-level visual features play a prominent role predicting aesthetic preference, but do not completely eliminate the predictive power of the low-level visual features. These strong predictors provide powerful insights for

Discriminating Induced-Microearthquakes Using New Seismic Features

Science.gov (United States)

Mousavi, S. M.; Horton, S.

2016-12-01

We studied characteristics of induced-microearthquakes on the basis of the waveforms recorded on a limited number of surface receivers using machine-learning techniques. Forty features in the time, frequency, and time-frequency domains were measured on each waveform, and several techniques such as correlation-based feature selection, Artificial Neural Networks (ANNs), Logistic Regression (LR) and X-mean were used as research tools to explore the relationship between these seismic features and source parameters. The results show that spectral features have the highest correlation to source depth. Two new measurements developed as seismic features for this study, spectral centroids and 2D cross-correlations in the time-frequency domain, performed better than the common seismic measurements. These features can be used by machine learning techniques for efficient automatic classification of low energy signals recorded at one or more seismic stations. We applied the technique to 440 microearthquakes-1.7Reference: Mousavi, S.M., S.P. Horton, C. A. Langston, B. Samei, (2016) Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression, Geophys. J. Int. doi: 10.1093/gji/ggw258.

Feature Selection Based on Mutual Correlation

Czech Academy of Sciences Publication Activity Database

Haindl, Michal; Somol, Petr; Ververidis, D.; Kotropoulos, C.

2006-01-01

Roč. 19, č. 4225 (2006), s. 569-577 ISSN 0302-9743. [Iberoamerican Congress on Pattern Recognition. CIARP 2006 /11./. Cancun, 14.11.2006-17.11.2006] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA AV ČR IAA2075302 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : feature selection Subject RIV: BD - Theory of Information Impact factor: 0.402, year: 2005 http://library.utia.cas.cz/separaty/historie/haindl-feature selection based on mutual correlation.pdf

Solid state radiation chemistry. Features important in basic research and applications

International Nuclear Information System (INIS)

Zagorski, Z.P.

1998-01-01

The basic research of chemical radiation effects has been mostly proceeded in aqueous systems. When one turns from aqueous to the 'dry solute' systems, reactions are running in a very different way. The examined compound, previously the solute, becomes then the only constituent of the system, absorbing all ionising energy. Majority of dosimeters and of radiation processed systems is solid: these are crystalline or rigid substances of high viscosity, sometimes of complicated phase-compositions being no longer homogenous like liquids. Main features of the solid (and rigid) state radiation chemistry is to be discussed in five parts: I. Character of absorption process. Absorption of radiation is in all media heterogenous on the molecular level, i.e. with formation of single- and multi-ionisation spurs. The yield of the latters is 15-25% of the total ionisations, depending on the system, even at low LET radiation. In spite of random distribution of initial ionisations, the single-ionisation spurs can turn rapidly into specifically arranged, temporal localisations. The variety of spur reactions is usually more complicated than that in aqueous systems. II. Character of transients. Intermediates in solid state radiation chemistry exhibit very different transport properties: from free electrons moving fast and far, to electrons changing the position by different physicochemical mechanisms, to easy movable H-atoms, and to practically unmovable, only vibrating, new fragments of a lattice or glass. III. Paramagnetic intermediates. Radicals living for microseconds in liquids, when created and trapped in a solid matrix are usually very stable, e.g. they can have a difference of half-life times of 12 orders of magnitude, however their chemical composition remais identical. (author)

Audio feature extraction using probability distribution function

Science.gov (United States)

Suhaib, A.; Wan, Khairunizam; Aziz, Azri A.; Hazry, D.; Razlan, Zuradzman M.; Shahriman A., B.

2015-05-01

Voice recognition has been one of the popular applications in robotic field. It is also known to be recently used for biometric and multimedia information retrieval system. This technology is attained from successive research on audio feature extraction analysis. Probability Distribution Function (PDF) is a statistical method which is usually used as one of the processes in complex feature extraction methods such as GMM and PCA. In this paper, a new method for audio feature extraction is proposed which is by using only PDF as a feature extraction method itself for speech analysis purpose. Certain pre-processing techniques are performed in prior to the proposed feature extraction method. Subsequently, the PDF result values for each frame of sampled voice signals obtained from certain numbers of individuals are plotted. From the experimental results obtained, it can be seen visually from the plotted data that each individuals' voice has comparable PDF values and shapes.

Antisocial features and "faking bad": A critical note.

Science.gov (United States)

Niesten, Isabella J M; Nentjes, Lieke; Merckelbach, Harald; Bernstein, David P

2015-01-01

We critically review the literature on antisocial personality features and symptom fabrication (i.e., faking bad; e.g., malingering). A widespread assumption is that these constructs are intimately related. Some studies have, indeed, found that antisocial individuals score higher on instruments detecting faking bad, but others have been unable to replicate this pattern. In addition, studies exploring whether antisocial individuals are especially talented in faking bad have generally come up with null results. The notion of an intrinsic link between antisocial features and faking bad is difficult to test and research in this domain is sensitive to selection bias. We argue that research on faking bad would profit from further theoretical articulation. One topic that deserves scrutiny is how antisocial features affect the cognitive dissonance typically induced by faking bad. We illustrate our points with preliminary data and discuss their implications. Copyright © 2015 Elsevier Ltd. All rights reserved.

Raman spectral feature selection using ant colony optimization for breast cancer diagnosis.

Science.gov (United States)

Fallahzadeh, Omid; Dehghani-Bidgoli, Zohreh; Assarian, Mohammad

2018-06-04

Pathology as a common diagnostic test of cancer is an invasive, time-consuming, and partially subjective method. Therefore, optical techniques, especially Raman spectroscopy, have attracted the attention of cancer diagnosis researchers. However, as Raman spectra contain numerous peaks involved in molecular bounds of the sample, finding the best features related to cancerous changes can improve the accuracy of diagnosis in this method. The present research attempted to improve the power of Raman-based cancer diagnosis by finding the best Raman features using the ACO algorithm. In the present research, 49 spectra were measured from normal, benign, and cancerous breast tissue samples using a 785-nm micro-Raman system. After preprocessing for removal of noise and background fluorescence, the intensity of 12 important Raman bands of the biological samples was extracted as features of each spectrum. Then, the ACO algorithm was applied to find the optimum features for diagnosis. As the results demonstrated, by selecting five features, the classification accuracy of the normal, benign, and cancerous groups increased by 14% and reached 87.7%. ACO feature selection can improve the diagnostic accuracy of Raman-based diagnostic models. In the present study, features corresponding to ν(C-C) αhelix proline, valine (910-940), νs(C-C) skeletal lipids (1110-1130), and δ(CH2)/δ(CH3) proteins (1445-1460) were selected as the best features in cancer diagnosis.

Air, Ocean and Climate Monitoring Enhancing Undergraduate Training in the Physical, Environmental and Computer Sciences

Science.gov (United States)

Hope, W. W.; Johnson, L. P.; Obl, W.; Stewart, A.; Harris, W. C.; Craig, R. D.

2000-01-01

Faculty in the Department of Physical, Environmental and Computer Sciences strongly believe in the concept that undergraduate research and research-related activities must be integrated into the fabric of our undergraduate Science and Technology curricula. High level skills, such as problem solving, reasoning, collaboration and the ability to engage in research, are learned for advanced study in graduate school or for competing for well paying positions in the scientific community. One goal of our academic programs is to have a pipeline of research activities from high school to four year college, to graduate school, based on the GISS Institute on Climate and Planets model.

The Catchment Feature Model: A Device for Multimodal Fusion and a Bridge between Signal and Sense

Directory of Open Access Journals (Sweden)

Francis Quek

2004-09-01

Full Text Available The catchment feature model addresses two questions in the field of multimodal interaction: how we bridge video and audio processing with the realities of human multimodal communication, and how information from the different modes may be fused. We argue from a detailed literature review that gestural research has clustered around manipulative and semaphoric use of the hands, motivate the catchment feature model psycholinguistic research, and present the model. In contrast to “whole gesture” recognition, the catchment feature model applies a feature decomposition approach that facilitates cross-modal fusion at the level of discourse planning and conceptualization. We present our experimental framework for catchment feature-based research, cite three concrete examples of catchment features, and propose new directions of multimodal research based on the model.

Special feature on imaging systems and techniques

Science.gov (United States)

Yang, Wuqiang; Giakos, George

2013-07-01

The IEEE International Conference on Imaging Systems and Techniques (IST'2012) was held in Manchester, UK, on 16-17 July 2012. The participants came from 26 countries or regions: Austria, Brazil, Canada, China, Denmark, France, Germany, Greece, India, Iran, Iraq, Italy, Japan, Korea, Latvia, Malaysia, Norway, Poland, Portugal, Sweden, Switzerland, Taiwan, Tunisia, UAE, UK and USA. The technical program of the conference consisted of a series of scientific and technical sessions, exploring physical principles, engineering and applications of new imaging systems and techniques, as reflected by the diversity of the submitted papers. Following a rigorous review process, a total of 123 papers were accepted, and they were organized into 30 oral presentation sessions and a poster session. In addition, six invited keynotes were arranged. The conference not only provided the participants with a unique opportunity to exchange ideas and disseminate research outcomes but also paved a way to establish global collaboration. Following the IST'2012, a total of 55 papers, which were technically extended substantially from their versions in the conference proceeding, were submitted as regular papers to this special feature of Measurement Science and Technology . Following a rigorous reviewing process, 25 papers have been finally accepted for publication in this special feature and they are organized into three categories: (1) industrial tomography, (2) imaging systems and techniques and (3) image processing. These papers not only present the latest developments in the field of imaging systems and techniques but also offer potential solutions to existing problems. We hope that this special feature provides a good reference for researchers who are active in the field and will serve as a catalyst to trigger further research. It has been our great pleasure to be the guest editors of this special feature. We would like to thank the authors for their contributions, without which it would

Asia Research News features IDRC-funded projects | CRDI - Centre ...

International Development Research Centre (IDRC) Digital Library (Canada)

18 juin 2014 ... From combating chronic malnutrition to improving health care for women, exploring the causes of violence in cities, or understanding the needs of small and medium enterprises, the 2014 edition of Asia Research News provides a snapshot of IDRC-funded research in Asia.

Extracted facial feature of racial closely related faces

Science.gov (United States)

Liewchavalit, Chalothorn; Akiba, Masakazu; Kanno, Tsuneo; Nagao, Tomoharu

2010-02-01

Human faces contain a lot of demographic information such as identity, gender, age, race and emotion. Human being can perceive these pieces of information and use it as an important clue in social interaction with other people. Race perception is considered the most delicacy and sensitive parts of face perception. There are many research concerning image-base race recognition, but most of them are focus on major race group such as Caucasoid, Negroid and Mongoloid. This paper focuses on how people classify race of the racial closely related group. As a sample of racial closely related group, we choose Japanese and Thai face to represents difference between Northern and Southern Mongoloid. Three psychological experiment was performed to study the strategies of face perception on race classification. As a result of psychological experiment, it can be suggested that race perception is an ability that can be learn. Eyes and eyebrows are the most attention point and eyes is a significant factor in race perception. The Principal Component Analysis (PCA) was performed to extract facial features of sample race group. Extracted race features of texture and shape were used to synthesize faces. As the result, it can be suggested that racial feature is rely on detailed texture rather than shape feature. This research is a indispensable important fundamental research on the race perception which are essential in the establishment of human-like race recognition system.

Feature-Based versus Category-Based Induction with Uncertain Categories

Science.gov (United States)

Griffiths, Oren; Hayes, Brett K.; Newell, Ben R.

2012-01-01

Previous research has suggested that when feature inferences have to be made about an instance whose category membership is uncertain, feature-based inductive reasoning is used to the exclusion of category-based induction. These results contrast with the observation that people can and do use category-based induction when category membership is…

Feature Optimization for Long-Range Visual Homing in Changing Environments

Directory of Open Access Journals (Sweden)

Qidan Zhu

2014-02-01

Full Text Available This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance. This paper presents a modified feature extraction algorithm to decrease the influence of anisotropic feature distribution. In addition, the feature selection and updating mechanisms, which have hardly drawn any attention in the domain of feature-based visual homing, are crucial in improving homing accuracy and in maintaining the representation of changing environments. To verify the feasibility of the proposal, several comprehensive evaluations are conducted. The results indicate that the feature optimization method can find optimal feature sets for feature-based visual homing, and adapt the appearance representation to the changing environments as well.

Acoustic Features Influence Musical Choices Across Multiple Genres.

Science.gov (United States)

Barone, Michael D; Bansal, Jotthi; Woolhouse, Matthew H

2017-01-01

Based on a large behavioral dataset of music downloads, two analyses investigate whether the acoustic features of listeners' preferred musical genres influence their choice of tracks within non-preferred, secondary musical styles. Analysis 1 identifies feature distributions for pairs of genre-defined subgroups that are distinct. Using correlation analysis, these distributions are used to test the degree of similarity between subgroups' main genres and the other music within their download collections. Analysis 2 explores the issue of main-to-secondary genre influence through the production of 10 feature-influence matrices, one per acoustic feature, in which cell values indicate the percentage change in features for genres and subgroups compared to overall population averages. In total, 10 acoustic features and 10 genre-defined subgroups are explored within the two analyses. Results strongly indicate that the acoustic features of people's main genres influence the tracks they download within non-preferred, secondary musical styles. The nature of this influence and its possible actuating mechanisms are discussed with respect to research on musical preference, personality, and statistical learning.

A prototype feature system for feature retrieval using relationships

Science.gov (United States)

Choi, J.; Usery, E.L.

2009-01-01

Using a feature data model, geographic phenomena can be represented effectively by integrating space, theme, and time. This paper extends and implements a feature data model that supports query and visualization of geographic features using their non-spatial and temporal relationships. A prototype feature-oriented geographic information system (FOGIS) is then developed and storage of features named Feature Database is designed. Buildings from the U.S. Marine Corps Base, Camp Lejeune, North Carolina and subways in Chicago, Illinois are used to test the developed system. The results of the applications show the strength of the feature data model and the developed system 'FOGIS' when they utilize non-spatial and temporal relationships in order to retrieve and visualize individual features.

Text Mining in Python through the HTRC Feature Reader

Directory of Open Access Journals (Sweden)

Peter Organisciak

2016-11-01

Full Text Available We introduce a toolkit for working with the 13.6 million volume Extracted Features Dataset from the HathiTrust Research Center. You will learn how to peer at the words and trends of any book in the collection, while developing broadly useful Python data analysis skills. The HathiTrust holds nearly 15 million digitized volumes from libraries around the world. In addition to their individual value, these works in aggregate are extremely valuable for historians. Spanning many centuries and genres, they offer a way to learn about large-scale trends in history and culture, as well as evidence for changes in language or even the structure of the book. To simplify access to this collection the HathiTrust Research Center (HTRC has released the Extracted Features dataset (Capitanu et al. 2015: a dataset that provides quantitative information describing every page of every volume in the collection. In this lesson, we introduce the HTRC Feature Reader, a library for working with the HTRC Extracted Features dataset using the Python programming language. The HTRC Feature Reader is structured to support work using popular data science libraries, particularly Pandas. Pandas provides simple structures for holding data and powerful ways to interact with it. The HTRC Feature Reader uses these data structures, so learning how to use it will also cover general data analysis skills in Python.

Features fusion based approach for handwritten Gujarati character recognition

Directory of Open Access Journals (Sweden)

Ankit Sharma

2017-02-01

Full Text Available Handwritten character recognition is a challenging area of research. Lots of research activities in the area of character recognition are already done for Indian languages such as Hindi, Bangla, Kannada, Tamil and Telugu. Literature review on handwritten character recognition indicates that in comparison with other Indian scripts research activities on Gujarati handwritten character recognition are very less.  This paper aims to bring Gujarati character recognition in attention. Recognition of isolated Gujarati handwritten characters is proposed using three different kinds of features and their fusion. Chain code based, zone based and projection profiles based features are utilized as individual features. One of the significant contribution of proposed work is towards the generation of large and representative dataset of 88,000 handwritten Gujarati characters. Experiments are carried out on this developed dataset. Artificial Neural Network (ANN, Support Vector Machine (SVM and Naive Bayes (NB classifier based methods are implemented for handwritten Gujarati character recognition. Experimental results show substantial enhancement over state-of-the-art and authenticate our proposals.

Discriminative semi-supervised feature selection via manifold regularization.

Science.gov (United States)

Xu, Zenglin; King, Irwin; Lyu, Michael Rung-Tsong; Jin, Rong

2010-07-01

Feature selection has attracted a huge amount of interest in both research and application communities of data mining. We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data. To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. The proposed approach selects features through maximizing the classification margin between different classes and simultaneously exploiting the geometry of the probability distribution that generates both labeled and unlabeled data. In comparison with previous semi-supervised feature selection algorithms, our proposed semi-supervised feature selection method is an embedded feature selection method and is able to find more discriminative features. We formulate the proposed feature selection method into a convex-concave optimization problem, where the saddle point corresponds to the optimal solution. To find the optimal solution, the level method, a fairly recent optimization method, is employed. We also present a theoretic proof of the convergence rate for the application of the level method to our problem. Empirical evaluation on several benchmark data sets demonstrates the effectiveness of the proposed semi-supervised feature selection method.

Ethnicity distinctiveness through iris texture features using Gabor filters

CSIR Research Space (South Africa)

Mabuza-Hocquet, Gugulethu P

2017-02-01

Full Text Available Research in iris biometrics has been focused on utilizing iris features as a means of identity verification and authentication. However, not enough research work has been done to explore iris textures to determine soft biometrics such as gender...

Spring Research Festival Features Visit from FCPS Superintendent | Poster

Science.gov (United States)

One of the highlights of the 19th annual Spring Research Festival (SRF), held May 4–7, was a visit from Terry Alban, Ph.D., superintendent of Frederick County Public Schools (FCPS), and Mike Markoe, deputy superintendent, FCPS. They toured the event on May 7, talking to researchers and students about their posters. “Dr. Alban was very interested in hearing what the Werner H.

AGSuite: Software to conduct feature analysis of artificial grammar learning performance.

Science.gov (United States)

Cook, Matthew T; Chubala, Chrissy M; Jamieson, Randall K

2017-10-01

To simplify the problem of studying how people learn natural language, researchers use the artificial grammar learning (AGL) task. In this task, participants study letter strings constructed according to the rules of an artificial grammar and subsequently attempt to discriminate grammatical from ungrammatical test strings. Although the data from these experiments are usually analyzed by comparing the mean discrimination performance between experimental conditions, this practice discards information about the individual items and participants that could otherwise help uncover the particular features of strings associated with grammaticality judgments. However, feature analysis is tedious to compute, often complicated, and ill-defined in the literature. Moreover, the data violate the assumption of independence underlying standard linear regression models, leading to Type I error inflation. To solve these problems, we present AGSuite, a free Shiny application for researchers studying AGL. The suite's intuitive Web-based user interface allows researchers to generate strings from a database of published grammars, compute feature measures (e.g., Levenshtein distance) for each letter string, and conduct a feature analysis on the strings using linear mixed effects (LME) analyses. The LME analysis solves the inflation of Type I errors that afflicts more common methods of repeated measures regression analysis. Finally, the software can generate a number of graphical representations of the data to support an accurate interpretation of results. We hope the ease and availability of these tools will encourage researchers to take full advantage of item-level variance in their datasets in the study of AGL. We moreover discuss the broader applicability of the tools for researchers looking to conduct feature analysis in any field.

Enhancing facial features by using clear facial features

Science.gov (United States)

Rofoo, Fanar Fareed Hanna

2017-09-01

The similarity of features between individuals of same ethnicity motivated the idea of this project. The idea of this project is to extract features of clear facial image and impose them on blurred facial image of same ethnic origin as an approach to enhance a blurred facial image. A database of clear images containing 30 individuals equally divided to five different ethnicities which were Arab, African, Chines, European and Indian. Software was built to perform pre-processing on images in order to align the features of clear and blurred images. And the idea was to extract features of clear facial image or template built from clear facial images using wavelet transformation to impose them on blurred image by using reverse wavelet. The results of this approach did not come well as all the features did not align together as in most cases the eyes were aligned but the nose or mouth were not aligned. Then we decided in the next approach to deal with features separately but in the result in some cases a blocky effect was present on features due to not having close matching features. In general the available small database did not help to achieve the goal results, because of the number of available individuals. The color information and features similarity could be more investigated to achieve better results by having larger database as well as improving the process of enhancement by the availability of closer matches in each ethnicity.

Spatial features register: toward standardization of spatial features

Science.gov (United States)

Cascio, Janette

1994-01-01

As the need to share spatial data increases, more than agreement on a common format is needed to ensure that the data is meaningful to both the importer and the exporter. Effective data transfer also requires common definitions of spatial features. To achieve this, part 2 of the Spatial Data Transfer Standard (SDTS) provides a model for a spatial features data content specification and a glossary of features and attributes that fit this model. The model provides a foundation for standardizing spatial features. The glossary now contains only a limited subset of hydrographic and topographic features. For it to be useful, terms and definitions must be included for other categories, such as base cartographic, bathymetric, cadastral, cultural and demographic, geodetic, geologic, ground transportation, international boundaries, soils, vegetation, water, and wetlands, and the set of hydrographic and topographic features must be expanded. This paper will review the philosophy of the SDTS part 2 and the current plans for creating a national spatial features register as one mechanism for maintaining part 2.

Research on the features of aeromagnetic field in Guyuan-Duolun region

International Nuclear Information System (INIS)

Fan Ai

1996-01-01

On the basis of the physical properties of rocks and uranium ore and geological information, this paper has analysed and studied the aeromagnetic field in Guyuan-Duolun region. The aeromagnetic field is divided into eight magnetic field sub-regions according to its distribution features and amplitude differences. Then, geological interpretation has been carried out in each of magnetic field subregions. This work has provided basic materials for predicting uranium prospects in this region using aeromagnetic information

Dynamic binding of visual features by neuronal/stimulus synchrony.

Science.gov (United States)

Iwabuchi, A

1998-05-01

When people see a visual scene, certain parts of the visual scene are treated as belonging together and we regard them as a perceptual unit, which is called a "figure". People focus on figures, and the remaining parts of the scene are disregarded as "ground". In Gestalt psychology this process is called "figure-ground segregation". According to current perceptual psychology, a figure is formed by binding various visual features in a scene, and developments in neuroscience have revealed that there are many feature-encoding neurons, which respond to such features specifically. It is not known, however, how the brain binds different features of an object into a coherent visual object representation. Recently, the theory of binding by neuronal synchrony, which argues that feature binding is dynamically mediated by neuronal synchrony of feature-encoding neurons, has been proposed. This review article portrays the problem of figure-ground segregation and features binding, summarizes neurophysiological and psychophysical experiments and theory relevant to feature binding by neuronal/stimulus synchrony, and suggests possible directions for future research on this topic.

Statistical and Measurement Properties of Features Used in Essay Assessment. Research Report. ETS RR-04-21

Science.gov (United States)

Haberman, Shelby J.

2004-01-01

Statistical and measurement properties are examined for features used in essay assessment to determine the generalizability of the features across populations, prompts, and individuals. Data are employed from TOEFL® and GMAT® examinations and from writing for Criterion?.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

Science.gov (United States)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

ALEXNET FEATURE EXTRACTION AND MULTI-KERNEL LEARNING FOR OBJECTORIENTED CLASSIFICATION

Directory of Open Access Journals (Sweden)

L. Ding

2018-04-01

Full Text Available In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

Reductio ad discrimen: Where features come from

Directory of Open Access Journals (Sweden)

Elizabeth Cowper

2015-04-01

Full Text Available This paper addresses two fundamental questions about the nature of formal features in phonology and morphosyntax: what is their expressive power, and where do they come from? To answer these questions, we begin with the most restrictive possible hypothesis (all features are privative, and are wholly dictated by Universal Grammar, with no room for cross-linguistic variation, and examine the extent to which empirical evidence from a variety of languages compels a retreat from this position. We argue that there is little to be gained by positing a universal set of specific features, and propose instead that the crucial contribution of UG is the language learner's ability to construct features by identifying correlations between contrasts at different levels of linguistic structure. This view resonates with current research on how the interaction between UG and external 'third factors' shapes the structure of language, while at the same time harking back to the Saussurean notion that contrast is the central function of linguistic representations.

Modeling crash injury severity by road feature to improve safety.

Science.gov (United States)

Penmetsa, Praveena; Pulugurtha, Srinivas S

2018-01-02

The objective of this research is 2-fold: to (a) model and identify critical road features (or locations) based on crash injury severity and compare it with crash frequency and (b) model and identify drivers who are more likely to contribute to crashes by road feature. Crash data from 2011 to 2013 were obtained from the Highway Safety Information System (HSIS) for the state of North Carolina. Twenty-three different road features were considered, analyzed, and compared with each other as well as no road feature. A multinomial logit (MNL) model was developed and odds ratios were estimated to investigate the effect of road features on crash injury severity. Among the many road features, underpass, end or beginning of a divided highway, and on-ramp terminal on crossroad are the top 3 critical road features. Intersection crashes are frequent but are not highly likely to result in severe injuries compared to critical road features. Roundabouts are least likely to result in both severe and moderate injuries. Female drivers are more likely to be involved in crashes at intersections (4-way and T) compared to male drivers. Adult drivers are more likely to be involved in crashes at underpasses. Older drivers are 1.6 times more likely to be involved in a crash at the end or beginning of a divided highway. The findings from this research help to identify critical road features that need to be given priority. As an example, additional advanced warning signs and providing enlarged or highly retroreflective signs that grab the attention of older drivers may help in making locations such as end or beginning of a divided highway much safer. Educating drivers about the necessary skill sets required at critical road features in addition to engineering solutions may further help them adopt safe driving behaviors on the road.

FEATURE SELECTION METHODS BASED ON MUTUAL INFORMATION FOR CLASSIFYING HETEROGENEOUS FEATURES

Directory of Open Access Journals (Sweden)

Ratri Enggar Pawening

2016-06-01

Full Text Available Datasets with heterogeneous features can affect feature selection results that are not appropriate because it is difficult to evaluate heterogeneous features concurrently. Feature transformation (FT is another way to handle heterogeneous features subset selection. The results of transformation from non-numerical into numerical features may produce redundancy to the original numerical features. In this paper, we propose a method to select feature subset based on mutual information (MI for classifying heterogeneous features. We use unsupervised feature transformation (UFT methods and joint mutual information maximation (JMIM methods. UFT methods is used to transform non-numerical features into numerical features. JMIM methods is used to select feature subset with a consideration of the class label. The transformed and the original features are combined entirely, then determine features subset by using JMIM methods, and classify them using support vector machine (SVM algorithm. The classification accuracy are measured for any number of selected feature subset and compared between UFT-JMIM methods and Dummy-JMIM methods. The average classification accuracy for all experiments in this study that can be achieved by UFT-JMIM methods is about 84.47% and Dummy-JMIM methods is about 84.24%. This result shows that UFT-JMIM methods can minimize information loss between transformed and original features, and select feature subset to avoid redundant and irrelevant features.

New perspectives in ocean acidification research: editor's introduction to the special feature on ocean acidification.

Science.gov (United States)

Munday, Philip L

2017-09-01

Special Feature are from authors who attended the symposium and address cutting-edge questions and emerging topics in ocean acidification research, across the taxonomic spectrum from plankton to top predators. They cover the three streams of research identified as crucial to understanding the biological impacts of ocean acidification: (i) the relationship with other environmental drivers, (ii) the effects on ecological process and species interactions, and (iii) the role that individual variation, phenotypic plasticity and adaptation will have in shaping the impacts of ocean acidification and warming on marine ecosystems. © 2017 The Author(s).

Photonic crystals: features and applications (physics research and technology)

CERN Document Server

2013-01-01

The present book is focused on the study of unprecedented control and manipulation of light by photonic crystals (PCs) and their applications. These are micro- or usually nano-structures composed of periodic indexes of refraction of dielectrics with high refractive index contrast. They exhibit optical frequency band gaps in analogy to electronic bands for a periodic potential of a semiconductor crystal lattice. The gemstone opal and butterflys feathers colours are already referred to as natural examples of photonic crystals. The characteristics of such supper-lattices were first reported by Yablonovitch in 1987. The exploitation of photonic crystals is a promising tool in communication, sensors, optical computing, and nanophotonics. Discussed are the various features of one-dimensional (1D) and two-dimensional (2D) photonic crystals, photonic quasi crystals, heterostuctures and PC fibres under a variety of conditions using several materials, and metamaterials. It also focuses on the applications of PCs in opt...

Origin, extent and health impacts of air pollution in Sub-Saharan Africa

Science.gov (United States)

Bauer, S.; Im, U.; Mezuman, K.

2017-12-01

Southern Africa produces about a third of the Earth's biomass burning aerosol particles, yet the fate of these particles, their origin, chemical composition and their influence on regional and global climate is poorly understood. These research questions motivated the NASA field campaign ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS). ORACLES is a five year investigation with three Intensive Observation Periods (IOP) designed to study key processes that determine the climate impacts of African biomass burning aerosols. The first IOP has been carried out in 2016. The main focus of the field campaign are aerosol-cloud interactions, however in our first study related to this area we will investigate the aerosol plume itself, its origin, extend and its resulting health impacts. Here we will discuss results using the global mesoscale model NASA GEOS-5 in conjunction with the NASA GISS-E2 climate model to investigate climate and health impacts that are directly related to the anthropogenic fire activities in Sub-Saharan Africa. Focus will be on the SH winter seasons biomass burning events, its contribution to Sub-Saharan air pollution in relationship to other air-pollution sources and its resulting premature mortality.

Origin, extend and health impacts of air pollution in Sub-Saharan Africa

Science.gov (United States)

Bauer, Susanne E.; Mezuman, Keren; Longo, Karla; da Silva, Arlindo

2017-04-01

Southern Africa produces about a third of the Earth's biomass burning aerosol particles, yet the fate of these particles, their origin, chemical composition and their influence on regional and global climate is poorly understood. These research questions motivated the NASA field campaign ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS). ORACLES is a five year investigation with three Intensive Observation Periods (IOP) designed to study key processes that determine the climate impacts of African biomass burning aerosols. The first IOP has been carried out in 2016. The main focus of the field campaign are aerosol-cloud interactions, however in our first study related to this area we will investigate the aerosol plume itself, its origin, extend and its resulting health impacts. Here we will discuss results using the global mesoscale model NASA GEOS-5 in conjunction with the NASA GISS-E2 climate model to investigate climate and health impacts that are directly related to the anthropogenic fire activities in Sub-Saharan Africa. Focus will be on the SH winter seasons biomass burning events, its contribution to Sub-Saharan air pollution in relationship to other air-pollution sources and its resulting premature mortality.

Morphological features in children with autism

NARCIS (Netherlands)

Özgen, Mihriban Heval

2008-01-01

The central research aim in the present thesis was to extend the insight in several aspects of the role of the morphological features in autism. Clinical morphology might be used as a biomarker for ASD to reveal insight into the complexity of the disorder. In Chapter 1 current terminology and

Relevant test set using feature selection algorithm for early detection ...

African Journals Online (AJOL)

The objective of feature selection is to find the most relevant features for classification. Thus, the dimensionality of the information will be reduced and may improve classification's accuracy. This paper proposed a minimum set of relevant questions that can be used for early detection of dyslexia. In this research, we ...

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review.

Science.gov (United States)

Elaheebocus, Sheik Mohammad Roushdat Ally; Weal, Mark; Morrison, Leanne; Yardley, Lucy

2018-02-22

Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features' suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed

Determination of morphological features and molecular interactions ...

African Journals Online (AJOL)

This research focused on identifying the morphological features and molecular interactions of the Nigerian Bentonitic clays using Scanning Electron Microscope (SEM) characterisation technique. The SEM microstructure images indicated that the bentonite samples are generally moderately dispersive to dispersive with ...

First Materials Science Research Rack Capabilities and Design Features

Science.gov (United States)

Schaefer, D.; King, R.; Cobb, S.; Whitaker, Ann F. (Technical Monitor)

2001-01-01

The first Materials Science Research Rack (MSRR-1) will accommodate dual Experiment Modules (EM's) and provide simultaneous on-orbit processing operations capability. The first international Materials Science Experiment Module for the MSRR-1 is an international cooperative research activity between NASA's Marshall Space Flight Center (MSFC) and the European Space Agency's (ESA) European Space Research and Technology Center. (ESTEC). This International Standard Payload Rack (ISPR) will contain the Materials Science Laboratory (MSL) developed by ESA as an Experiment Module. The MSL Experiment Module will accommodate several on-orbit exchangeable experiment-specific Module Inserts. Module Inserts currently planned are a Quench Module Insert, Low Gradient Furnace, Solidification with Quench Furnace, and Diffusion Module Insert. The second Experiment Module for the MSRR-1 configuration is a commercial device supplied by MSFC's Space Products Department (SPD). It includes capabilities for vapor transport processes and liquid metal sintering. This Experiment Module will be replaced on-orbit with other NASA Materials Science EMs.

Face-iris multimodal biometric scheme based on feature level fusion

Science.gov (United States)

Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing; He, Fei

2015-11-01

Unlike score level fusion, feature level fusion demands all the features extracted from unimodal traits with high distinguishability, as well as homogeneity and compatibility, which is difficult to achieve. Therefore, most multimodal biometric research focuses on score level fusion, whereas few investigate feature level fusion. We propose a face-iris recognition method based on feature level fusion. We build a special two-dimensional-Gabor filter bank to extract local texture features from face and iris images, and then transform them by histogram statistics into an energy-orientation variance histogram feature with lower dimensions and higher distinguishability. Finally, through a fusion-recognition strategy based on principal components analysis and support vector machine (FRSPS), feature level fusion and one-to-n identification are accomplished. The experimental results demonstrate that this method can not only effectively extract face and iris features but also provide higher recognition accuracy. Compared with some state-of-the-art fusion methods, the proposed method has a significant performance advantage.

The NASA Climate Change Research Initiative - A Scientist's Perspective

Science.gov (United States)

LeGrande, A. N.; Pearce, M. D.; Dulaney, N.; Kelly, S. M.

2017-12-01

For the last four years, I have been a lead mentor in the NASA GISS Climate Change Research Initiative (CCRI) program, a component in the NASA GSFC Office of Education portfolio. It creates a multidisciplinary; vertical research team including a NYC metropolitan teacher, graduate student, undergraduate student, and high school student. While the college and high school members of this research team function like a more traditional internship component, the teacher component provides a powerful, direct way to connect state-of-the art research with students in the classroom. Because the teacher internship lasts a full year, it affords a similar relationship with a teacher that normally only exists between a PhD student and scientist. It also provides an opportunity to train the teacher in using the extensive data archives and other information maintained on NASA's publicly available websites. This time and access provide PhD-level training in the techniques and tools used in my climate research to the high school teacher. The teacher then uses his/her own pedagogical expertise to translate these techniques into age/level appropriate lesson plans for the classroom aligned with current STEM education trends and expectations. Throughout the process, there is an exchange of knowledge between the teacher and scientist that is very similar to the training given to PhD level graduate students. The teacher's understanding of the topic and implementation of the tools is done under a very close collaboration with the scientist supervisor and the NASA Education Program Specialist. This vertical team model encourages collegial communication between teachers and learners from many different educational levels and capitalizes on the efficacy of near peer mentoring strategies. This relationship is important in building trust through the difficult, iterative process that results in the development of highly accurate and quality (continuously discussed and vetted) curriculum composed

Feature Fatigue, IT Fashion and IT Consumerization - Is There a Relationship?

Directory of Open Access Journals (Sweden)

Luiz Antonio Slongo

2015-12-01

Full Text Available Based on the concepts of Feature Fatigue, IT Fashion and IT Consumerization, this paper aims to investigate the relationships between them answering two questions: (1 does the phenomenon of IT Fashion result in Feature Fatigue? (2 Will the concept of Feature Fatigue cause the same effect when looking from the point of view of the IT Consumerization in the corporate environment? The research addresses these questions through two techniques: a laddering and a survey. Albeit tenuously, the results provide evidence that consumption motivated by IT Fashion leads to Feature Fatigue. This study contributes to management research by attempting at the phenomenon described from a multidisciplinary perspective, also contributing to management practice, specifically for marketing managers trying to understand the experiences and expectations of consumers, and also for IT managers engaged in the design of governance policies regarding the use of personal devices by employees in this context.

Attention in the processing of complex visual displays: detecting features and their combinations.

Science.gov (United States)

Farell, B

1984-02-01

The distinction between operations in visual processing that are parallel and preattentive and those that are serial and attentional receives both theoretical and empirical support. According to Treisman's feature-integration theory, independent features are available preattentively, but attention is required to veridically combine features into objects. Certain evidence supporting this theory is consistent with a different interpretation, which was tested in four experiments. The first experiment compared the detection of features and feature combinations while eliminating a factor that confounded earlier comparisons. The resulting priority of access to combinatorial information suggests that features and nonlocal combinations of features are not connected solely by a bottom-up hierarchical convergence. Causes of the disparity between the results of Experiment 1 and the results of previous research were investigated in three subsequent experiments. The results showed that of the two confounded factors, it was the difference in the mapping of alternatives onto responses, not the differing attentional demands of features and objects, that underlaid the results of the previous research. The present results are thus counterexamples to the feature-integration theory. Aspects of this theory are shown to be subsumed by more general principles, which are discussed in terms of attentional processes in the detection of features, objects, and stimulus alternatives.

Identifying significant environmental features using feature recognition.

Science.gov (United States)

2015-10-01

The Department of Environmental Analysis at the Kentucky Transportation Cabinet has expressed an interest in feature-recognition capability because it may help analysts identify environmentally sensitive features in the landscape, : including those r...

Feature hashing for fast image retrieval

Science.gov (United States)

Yan, Lingyu; Fu, Jiarun; Zhang, Hongxin; Yuan, Lu; Xu, Hui

2018-03-01

Currently, researches on content based image retrieval mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is necessary to consider searching among large scale images, which is very timeconsuming and unscalable. Hence, we need to pay much attention to the efficiency of image retrieval. In this paper, we propose a feature hashing method for image retrieval which not only generates compact fingerprint for image representation, but also prevents huge semantic loss during the process of hashing. To generate the fingerprint, an objective function of semantic loss is constructed and minimized, which combine the influence of both the neighborhood structure of feature data and mapping error. Since the machine learning based hashing effectively preserves neighborhood structure of data, it yields visual words with strong discriminability. Furthermore, the generated binary codes leads image representation building to be of low-complexity, making it efficient and scalable to large scale databases. Experimental results show good performance of our approach.

The meaning of dwelling features : Conceptual and methodological issues

NARCIS (Netherlands)

Coolen, H.C.C.H.

2008-01-01

This study is about the meaning of dwelling features. It relates the research areas of housing preferences and the meaning of a dwelling with each other and with aspects of the means-end approach as applied in marketing research. It results in a conceptual and methodological framework for studying

a Performance Comparison of Feature Detectors for Planetary Rover Mapping and Localization

Science.gov (United States)

Wan, W.; Peng, M.; Xing, Y.; Wang, Y.; Liu, Z.; Di, K.; Teng, B.; Mao, X.; Zhao, Q.; Xin, X.; Jia, M.

2017-07-01

Feature detection and matching are key techniques in computer vision and robotics, and have been successfully implemented in many fields. So far there is no performance comparison of feature detectors and matching methods for planetary mapping and rover localization using rover stereo images. In this research, we present a comprehensive evaluation and comparison of six feature detectors, including Moravec, Förstner, Harris, FAST, SIFT and SURF, aiming for optimal implementation of feature-based matching in planetary surface environment. To facilitate quantitative analysis, a series of evaluation criteria, including distribution evenness of matched points, coverage of detected points, and feature matching accuracy, are developed in the research. In order to perform exhaustive evaluation, stereo images, simulated under different baseline, pitch angle, and interval of adjacent rover locations, are taken as experimental data source. The comparison results show that SIFT offers the best overall performance, especially it is less sensitive to changes of image taken at adjacent locations.

A PERFORMANCE COMPARISON OF FEATURE DETECTORS FOR PLANETARY ROVER MAPPING AND LOCALIZATION

Directory of Open Access Journals (Sweden)

W. Wan

2017-07-01

Full Text Available Feature detection and matching are key techniques in computer vision and robotics, and have been successfully implemented in many fields. So far there is no performance comparison of feature detectors and matching methods for planetary mapping and rover localization using rover stereo images. In this research, we present a comprehensive evaluation and comparison of six feature detectors, including Moravec, Förstner, Harris, FAST, SIFT and SURF, aiming for optimal implementation of feature-based matching in planetary surface environment. To facilitate quantitative analysis, a series of evaluation criteria, including distribution evenness of matched points, coverage of detected points, and feature matching accuracy, are developed in the research. In order to perform exhaustive evaluation, stereo images, simulated under different baseline, pitch angle, and interval of adjacent rover locations, are taken as experimental data source. The comparison results show that SIFT offers the best overall performance, especially it is less sensitive to changes of image taken at adjacent locations.

A proposed framework on hybrid feature selection techniques for handling high dimensional educational data

Science.gov (United States)

Shahiri, Amirah Mohamed; Husain, Wahidah; Rashid, Nur'Aini Abd

2017-10-01

Huge amounts of data in educational datasets may cause the problem in producing quality data. Recently, data mining approach are increasingly used by educational data mining researchers for analyzing the data patterns. However, many research studies have concentrated on selecting suitable learning algorithms instead of performing feature selection process. As a result, these data has problem with computational complexity and spend longer computational time for classification. The main objective of this research is to provide an overview of feature selection techniques that have been used to analyze the most significant features. Then, this research will propose a framework to improve the quality of students' dataset. The proposed framework uses filter and wrapper based technique to support prediction process in future study.

PSYCHOLOGICAL FEATURES OF CONFLICT BEHAVIOUR AMONG FEMALE INMATES

Directory of Open Access Journals (Sweden)

Anna Leonidovna Plotnikova

2017-04-01

Full Text Available In this article the results of the research concerning the special features of behaviour in the conflicts among the female inmates are given, psychological features of the female inmates are analysed, their psychological typology is presented, inmates gender differences are characterized as well as psychological reasons of interpersonal conflicts among the female inmates in the correctional facilities, the most conflict categories of female inmates are distinguished. Purpose: revealing of dominant strategies of behaviour in the conflict of the female inmates, specific features of female inmates conflict behaviour according to age and type of committed crime. Method: ascertaining experiment Results: dominant strategies of female inmates conflict behaviour are compromise and adjustment. Areas of use: the penitentiary system.

DIAGNOSTIC FEATURES RESEARCH OF AC ELECTRIC POINT MOTORS

Directory of Open Access Journals (Sweden)

S. YU. Buryak

2014-05-01

Full Text Available Purpose.Considerable responsibility for safety of operation rests on signal telephone and telegraph department of railway. One of the most attackable nodes (both automation systems, and railway in whole is track switches. The aim of this investigation is developing such system for monitoring and diagnostics of track switches, which would fully meet the requirements of modern conditions of high-speed motion and heavy trains and producing diagnostics, collection and systematization of data in an automated way. Methodology. In order to achieve the desired objectives research of a structure and the operating principle description of the switch electric drive, sequence of triggering its main units were carried out. The operating characteristics and settings, operating conditions, the causes of failures in the work, andrequirements for electric drives technology and their service were considered and analyzed. Basic analysis principles of dependence of nature of the changes the current waveform, which flows in the working circuit of AC electric point motor were determined. Technical implementation of the monitoring and diagnosing system the state of AC electric point motors was carried out. Findings. Signals taken from serviceable and defective electric turnouts were researched. Originality. Identified a strong interconnectionbetween the technical condition of the track switchand curve shape that describes the current in the circuit of AC electric point motor during operation which is based on the research processes that have influence on it during operation. Practical value. Shown the principles of the technical approach to the transition from scheduled preventive maintenance to maintenance of real condition for a more objective assessment and thus more rapid response to emerging or failures when they occur gradually, damages and any other shortcomings in the work track switch AC drives.

A Feature Fusion Based Forecasting Model for Financial Time Series

Science.gov (United States)

Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

2014-01-01

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

OpenAIRE

Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

2015-01-01

Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

Bridging the Research-Practice Gap: Research Translation and/or Research Transformation

Science.gov (United States)

Hirschkorn, Mark; Geelan, David

2008-01-01

The issue of the "research-practice gap"--the problematic relationship between research in education and educational practice--has been widely reported in the literature. This critical literature review explores some of the causes and features of the gap and suggests some possible approaches for addressing it. These solutions involve changes in…

Audiovisual laughter detection based on temporal features

NARCIS (Netherlands)

Petridis, Stavros; Nijholt, Antinus; Nijholt, A.; Pantic, M.; Pantic, Maja; Poel, Mannes; Poel, M.; Hondorp, G.H.W.

2008-01-01

Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved

Research as Profession and Practice: Frameworks for Guiding the Responsible Conduct of Research.

Science.gov (United States)

Chen, Jiin-Yu

2016-01-01

Programs in the responsible conduct of research (RCR) vary between institutions, demonstrated by disparate structures and goals. These variations may be attributed to the absence of grounding frameworks within which to examine research and RCR education programs. This article examines research as a practice and a profession, using these frames to draw out defining features of research and the moral obligations entailed. Situating research within virtue ethics can clarify how researchers might cultivate the virtues necessary for meeting its obligations and aims. By elucidating these features, these perspectives can serve to guide the development of RCR education programs.

The effect of website features in online relationship marketing: A case of online hotel booking

OpenAIRE

Bilgihan, A.

2015-01-01

The primary objective of this research is to develop a theory-based model of utilitarian and hedonic website features, customer commitment, trust, and e-loyalty in an online hotel booking context. Structural Equation Modeling was deployed to test research hypotheses. Findings highlight the importance of creating loyalty by focusing on both hedonic and utilitarian features. Affective commitment is more influenced by hedonic features whereas calculative commitment is driven by utilitarian ones....

Analytical Features: A Knowledge-Based Approach to Audio Feature Generation

Directory of Open Access Journals (Sweden)

Pachet François

2009-01-01

Full Text Available We present a feature generation system designed to create audio features for supervised classification tasks. The main contribution to feature generation studies is the notion of analytical features (AFs, a construct designed to support the representation of knowledge about audio signal processing. We describe the most important aspects of AFs, in particular their dimensional type system, on which are based pattern-based random generators, heuristics, and rewriting rules. We show how AFs generalize or improve previous approaches used in feature generation. We report on several projects using AFs for difficult audio classification tasks, demonstrating their advantage over standard audio features. More generally, we propose analytical features as a paradigm to bring raw signals into the world of symbolic computation.

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

Science.gov (United States)

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

Eye movement identification based on accumulated time feature

Science.gov (United States)

Guo, Baobao; Wu, Qiang; Sun, Jiande; Yan, Hua

2017-06-01

Eye movement is a new kind of feature for biometrical recognition, it has many advantages compared with other features such as fingerprint, face, and iris. It is not only a sort of static characteristics, but also a combination of brain activity and muscle behavior, which makes it effective to prevent spoofing attack. In addition, eye movements can be incorporated with faces, iris and other features recorded from the face region into multimode systems. In this paper, we do an exploring study on eye movement identification based on the eye movement datasets provided by Komogortsev et al. in 2011 with different classification methods. The time of saccade and fixation are extracted from the eye movement data as the eye movement features. Furthermore, the performance analysis was conducted on different classification methods such as the BP, RBF, ELMAN and SVM in order to provide a reference to the future research in this field.

Music genre classification using temporal domain features

Science.gov (United States)

Shiu, Yu; Kuo, C.-C. Jay

2004-10-01

Music genre provides an efficient way to index songs in the music database, and can be used as an effective means to retrieval music of a similar type, i.e. content-based music retrieval. In addition to other features, the temporal domain features of a music signal are exploited so as to increase the classification rate in this research. Three temporal techniques are examined in depth. First, the hidden Markov model (HMM) is used to emulate the time-varying properties of music signals. Second, to further increase the classification rate, we propose another feature set that focuses on the residual part of music signals. Third, the overall classification rate is enhanced by classifying smaller segments from a test material individually and making decision via majority voting. Experimental results are given to demonstrate the performance of the proposed techniques.

Featureous: infrastructure for feature-centric analysis of object-oriented software

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2010-01-01

The decentralized nature of collaborations between objects in object-oriented software makes it difficult to understand how user-observable program features are implemented and how their implementations relate to each other. It is worthwhile to improve this situation, since feature-centric program...... understanding and modification are essential during software evolution and maintenance. In this paper, we present an infrastructure built on top of the NetBeans IDE called Featureous that allows for rapid construction of tools for feature-centric analysis of object-oriented software. Our infrastructure...... encompasses a lightweight feature location mechanism, a number of analytical views and an API allowing for addition of third-party extensions. To form a common conceptual framework for future feature-centric extensions, we propose to structure feature centric analysis along three dimensions: perspective...

Quantitative imaging features: extension of the oncology medical image database

Science.gov (United States)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

OASIS 2: online application for survival analysis 2 with features for the analysis of maximal lifespan and healthspan in aging research.

Science.gov (United States)

Han, Seong Kyu; Lee, Dongyeop; Lee, Heetak; Kim, Donghyo; Son, Heehwa G; Yang, Jae-Seong; Lee, Seung-Jae V; Kim, Sanguk

2016-08-30

Online application for survival analysis (OASIS) has served as a popular and convenient platform for the statistical analysis of various survival data, particularly in the field of aging research. With the recent advances in the fields of aging research that deal with complex survival data, we noticed a need for updates to the current version of OASIS. Here, we report OASIS 2 (http://sbi.postech.ac.kr/oasis2), which provides extended statistical tools for survival data and an enhanced user interface. In particular, OASIS 2 enables the statistical comparison of maximal lifespans, which is potentially useful for determining key factors that limit the lifespan of a population. Furthermore, OASIS 2 provides statistical and graphical tools that compare values in different conditions and times. That feature is useful for comparing age-associated changes in physiological activities, which can be used as indicators of "healthspan." We believe that OASIS 2 will serve as a standard platform for survival analysis with advanced and user-friendly statistical tools for experimental biologists in the field of aging research.

FEATURES OF CONSOLIDATED FINANCIAL STATEMENTS: FOREIGN EXPERIENCE

Directory of Open Access Journals (Sweden)

S. V. KUCHER

2016-12-01

Full Text Available The article researches the features of preparation and submission of the consolidated financial statements of the world countries of different systems of accounting standardization in order to identify the areas of accounting improvement for the process of consolidation of financial reporting indicators. The main problems of consolidated financial statements preparation by business entities in Ukraine are determined. The author determines the theoretical and practical problems of consolidation of financial statements of organizational and methodical character. The comparative analysis of the features of standardization process of financial statements consolidation in the world countries is carried out. The main differences in the requirements for the formation of consolidated financial statements indicators of such countries as the French Republic, the Federal Republic of Germany, the Republic of Belarus and the People’s Republic of China are outlined. The main directions of scientific researches on the improvement of accounting and analytical support for the preparation of consolidated financial statements are formed.

Beyond breakthrough research: Epistemic properties of research and their consequences for research funding

NARCIS (Netherlands)

Laudel, Grit; Gläser, Jochen

2014-01-01

The aim of this paper is to initiate a discussion about links between epistemic properties and institutional conditions for research by providing an exploratory analysis of such links featured by projects funded by the European Research Council (ERC). Our analysis identifies epistemic properties of

Oscillating feature subset search algorithm for text categorization

Czech Academy of Sciences Publication Activity Database

Novovičová, Jana; Somol, Petr; Pudil, Pavel

2006-01-01

Roč. 44, č. 4225 (2006), s. 578-587 ISSN 0302-9743 R&D Projects: GA AV ČR IAA2075302; GA MŠk 2C06019 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : text classification * feature selection * oscillating search algorithm * Bhattacharyya distance Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.402, year: 2005

Associations Between PET Textural Features and GLUT1 Expression, and the Prognostic Significance of Textural Features in Lung Adenocarcinoma.

Science.gov (United States)

Koh, Young Wha; Park, Seong Yong; Hyun, Seung Hyup; Lee, Su Jin

2018-02-01

We evaluated the association between positron emission tomography (PET) textural features and glucose transporter 1 (GLUT1) expression level and further investigated the prognostic significance of textural features in lung adenocarcinoma. We evaluated 105 adenocarcinoma patients. We extracted texture-based PET parameters of primary tumors. Conventional PET parameters were also measured. The relationships between PET parameters and GLUT1 expression levels were evaluated. The association between PET parameters and overall survival (OS) was assessed using Cox's proportional hazard regression models. In terms of PET textural features, tumors expressing high levels of GLUT1 exhibited significantly lower coarseness, contrast, complexity, and strength, but significantly higher busyness. On univariate analysis, the metabolic tumor volume, total lesion glycolysis, contrast, busyness, complexity, and strength were significant predictors of OS. Multivariate analysis showed that lower complexity (HR=2.017, 95%CI=1.032-3.942, p=0.040) was independently associated with poorer survival. PET textural features may aid risk stratification in lung adenocarcinoma patients. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

A Probabilistic Feature Map-Based Localization System Using a Monocular Camera

Directory of Open Access Journals (Sweden)

Hyungjin Kim

2015-08-01

Full Text Available Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments

A Probabilistic Feature Map-Based Localization System Using a Monocular Camera.

Science.gov (United States)

Kim, Hyungjin; Lee, Donghwa; Oh, Taekjun; Choi, Hyun-Taek; Myung, Hyun

2015-08-31

Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments.

Feature: Controlling Seasonal Allergies | NIH Medlineplus the Magazine

Science.gov (United States)

... this page please turn Javascript on. Feature: Seasonal Allergies Controlling Seasonal Allergies Past Issues / Spring 2012 Table of Contents In ... to allergens, helping to prevent allergic reactions. Seasonal Allergy Research at NIH Allergen and T-Cell Reagent ...

Feature Extraction of Weld Defectology in Digital Image of Radiographic Film Using Geometric Invariant Moment and Statistical Texture

International Nuclear Information System (INIS)

Muhtadan

2009-01-01

The purpose of this research is to perform feature extraction in weld defect of digital image of radiographic film using geometric invariant moment and statistical texture method. Feature extraction values can be use as values that used to classify and pattern recognition on interpretation of weld defect in digital image of radiographic film by computer automatically. Weld defectology type that used in this research are longitudinal crack, transversal crack, distributed porosity, clustered porosity, wormhole, and no defect. Research methodology on this research are program development to read digital image, then performing image cropping to localize weld position, and then applying geometric invariant moment and statistical texture formulas to find feature values. The result of this research are feature extraction values that have tested with RST (rotation, scale, transformation) treatment and yield moment values that more invariant there are ϕ 3 , ϕ 4 , ϕ 5 from geometric invariant moment method. Feature values from statistical texture that are average intensity, average contrast, smoothness, 3 rd moment, uniformity, and entropy, they used as feature extraction values. (author)

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Science.gov (United States)

Wei, Wei; Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.

Prototypic Features of Loneliness in a Stratified Sample of Adolescents

DEFF Research Database (Denmark)

Lasgaard, Mathias; Elklit, Ask

2009-01-01

Dominant theoretical approaches in loneliness research emphasize the value of personality characteristics in explaining loneliness. The present study examines whether dysfunctional social strategies and attributions in lonely adolescents can be explained by personality characteristics...... guidance and intervention. Thus, professionals need to be knowledgeable about prototypic features of loneliness in addition to employing a pro-active approach when assisting adolescents who display prototypic features....

Quantitative research.

Science.gov (United States)

Watson, Roger

2015-04-01

This article describes the basic tenets of quantitative research. The concepts of dependent and independent variables are addressed and the concept of measurement and its associated issues, such as error, reliability and validity, are explored. Experiments and surveys – the principal research designs in quantitative research – are described and key features explained. The importance of the double-blind randomised controlled trial is emphasised, alongside the importance of longitudinal surveys, as opposed to cross-sectional surveys. Essential features of data storage are covered, with an emphasis on safe, anonymous storage. Finally, the article explores the analysis of quantitative data, considering what may be analysed and the main uses of statistics in analysis.

Feature Article

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education. Feature Article. Articles in Resonance – Journal of Science Education. Volume 1 Issue 1 January 1996 pp 80-85 Feature Article. What's New in Computers Windows 95 · Vijnan Shastri · More Details Fulltext PDF. Volume 1 Issue 1 January 1996 pp 86-89 Feature ...

Featureous: A Tool for Feature-Centric Analysis of Java Software

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2010-01-01

Feature-centric comprehension of source code is necessary for incorporating user-requested modifications during software evolution and maintenance. However, such comprehension is difficult to achieve in case of large object-oriented programs due to the size, complexity, and implicit character...... of mappings between features and source code. To support programmers in overcoming these difficulties, we present a feature-centric analysis tool, Featureous. Our tool extends the NetBeans IDE with mechanisms for efficient location of feature implementations in legacy source code, and an extensive analysis...

Feature extraction for dynamic integration of classifiers

NARCIS (Netherlands)

Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.; Patterson, D.W.

2007-01-01

Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique

Color vision: introduction by the feature editors.

Science.gov (United States)

Buck, Steven L; Baraas, Rigmor; Lee, Barry B; Lindsey, Delwin T; Uchikawa, Keiji; Webster, Michael A; Werner, John S

2016-03-01

This feature issue of the Journal of the Optical Society of America A (JOSA A) reflects the basic and applied research interests of members of the color vision community. Most of the articles stem from presentations at the 23rd Biennial Symposium of the International Colour Vision Society (ICVS).

Sociolinguistics features of humor in american linguoculture ...

African Journals Online (AJOL)

In this article we study the characteristics of the language of representation and perception of American humour, its linguistic and cultural features in humorous texts of American comics from the American linguistic culture. The material for research is the humorous texts and fragments of the performances of American ...

Feature selection in classification of eye movements using electrooculography for activity recognition.

Science.gov (United States)

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

Research organizations

DEFF Research Database (Denmark)

Larsen, Bøje; Aagaard, Peter

in Copenhagen - we argue that a post-rational form of research activity is emerging which revert these features. We term this new type of research "enchanted research", "sciencetainment" and "Mode2-b research". The factors that facilitate this development include the boring style of conventional research......, growing competition for research funds, more project funding compared to institutional funding and a demand for accountability. Countervailing forces also exist, however....

Assessing and Upgrading Ocean Mixing for the Study of Climate Change

Science.gov (United States)

Howard, A. M.; Fells, J.; Lindo, F.; Tulsee, V.; Canuto, V.; Cheng, Y.; Dubovikov, M. S.; Leboissetier, A.

2016-12-01

Climate is critical. Climate variability affects us all; Climate Change is a burning issue. Droughts, floods, other extreme events, and Global Warming's effects on these and problems such as sea-level rise and ecosystem disruption threaten lives. Citizens must be informed to make decisions concerning climate such as "business as usual" vs. mitigating emissions to keep warming within bounds. Medgar Evers undergraduates aid NASA research while learning climate science and developing computer&math skills. To make useful predictions we must realistically model each component of the climate system, including the ocean, whose critical role includes transporting&storing heat and dissolved CO2. We need physically based parameterizations of key ocean processes that can't be put explicitly in a global climate model, e.g. vertical&lateral mixing. The NASA-GISS turbulence group uses theory to model mixing including: 1) a comprehensive scheme for small scale vertical mixing, including convection&shear, internal waves & double-diffusion, and bottom tides 2) a new parameterization for the lateral&vertical mixing by mesoscale eddies. For better understanding we write our own programs. To assess the modelling MATLAB programs visualize and calculate statistics, including means, standard deviations and correlations, on NASA-GISS OGCM output with different mixing schemes and help us study drift from observations. We also try to upgrade the schemes, e.g. the bottom tidal mixing parameterizations' roughness, calculated from high resolution topographic data using Gaussian weighting functions with cut-offs. We study the effects of their parameters to improve them. A FORTRAN program extracts topography data subsets of manageable size for a MATLAB program, tested on idealized cases, to visualize&calculate roughness on. Students are introduced to modeling a complex system, gain a deeper appreciation of climate science, programming skills and familiarity with MATLAB, while furthering climate

Research reactor DHRUVA

International Nuclear Information System (INIS)

Veeraraghaven, N.

1990-01-01

DHRUVA, a 100 MWt research reactor located at the Bhabha Atomic Research Centre, Bombay, attained first criticality during August, 1985. The reactor is fuelled with natural uranium and is cooled, moderated and reflected by heavy water. Maximum thermal neutron flux obtained in the reactor is 1.8 X 10 14 n/cm 2 /sec. Some of the salient design features of the reactor are discussed in this paper. Some important features of the reactor coolant system, regulation and protection systems and experimental facilities are presented. A short account of the engineered safety features is provided. Some of the problems that were faced during commissioning and the initial phase of power operation are also dealt upon

First Materials Science Research Facility Rack Capabilities and Design Features

Science.gov (United States)

Cobb, S.; Higgins, D.; Kitchens, L.; Curreri, Peter (Technical Monitor)

2002-01-01

The first Materials Science Research Rack (MSRR-1) is the primary facility for U.S. sponsored materials science research on the International Space Station. MSRR-1 is contained in an International Standard Payload Rack (ISPR) equipped with the Active Rack Isolation System (ARIS) for the best possible microgravity environment. MSRR-1 will accommodate dual Experiment Modules and provide simultaneous on-orbit processing operations capability. The first Experiment Module for the MSRR-1, the Materials Science Laboratory (MSL), is an international cooperative activity between NASA's Marshall Space Flight Center (MSFC) and the European Space Agency's (ESA) European Space Research and Technology Center (ESTEC). The MSL Experiment Module will accommodate several on-orbit exchangeable experiment-specific Module Inserts which provide distinct thermal processing capabilities. Module Inserts currently planned for the MSL are a Quench Module Insert, Low Gradient Furnace, and a Solidification with Quench Furnace. The second Experiment Module for the MSRR-1 configuration is a commercial device supplied by MSFC's Space Products Development (SPD) Group. Transparent furnace assemblies include capabilities for vapor transport processes and annealing of glass fiber preforms. This Experiment Module is replaceable on-orbit. This paper will describe facility capabilities, schedule to flight and research opportunities.

European Nuclear Features

International Nuclear Information System (INIS)

Barre, B.; Gonzalez, E.; Diaz Diaz, J.L.; Jimenez, J.L.; Velarde, G.; Navarro, J.M.; Hittner, D.; Dominguez, M.T.; Bollini, G.; Martin, A.; Suarez, J.; Traini, E.; Lang-Lenton, J.

2004-01-01

''European Nuclear Features - ENF'' is a joint publication of the three specialized technical journals, Nuclear Espana (Spain), Revue General Nucleaire (France), and atw - International Journal of Nuclear Power (Germany). The ENF support the international Europeen exchange of information and news about energy and nuclear power. News items, comments, and scientific and technical contributions will cover important aspects of the field. The second issue of ENF contains contributions about theses topics, among others: Institutional and Political Changes in the EU. - CIEMAT Department of Nuclear Fission: A General Overview. - Inertial Fusion Energy at DENIM. - High Temperature Reactors. European Research Programme. - On Site Assistance to Khmelnitsky NPP 1 and 2 (Ukraine). - Dismantling and Decommissioning of Vandellos I. (orig.)

Feature selection, statistical modeling and its applications to universal JPEG steganalyzer

Energy Technology Data Exchange (ETDEWEB)

Jalan, Jaikishan [Iowa State Univ., Ames, IA (United States)

2009-01-01

Steganalysis deals with identifying the instances of medium(s) which carry a message for communication by concealing their exisitence. This research focuses on steganalysis of JPEG images, because of its ubiquitous nature and low bandwidth requirement for storage and transmission. JPEG image steganalysis is generally addressed by representing an image with lower-dimensional features such as statistical properties, and then training a classifier on the feature set to differentiate between an innocent and stego image. Our approach is two fold: first, we propose a new feature reduction technique by applying Mahalanobis distance to rank the features for steganalysis. Many successful steganalysis algorithms use a large number of features relative to the size of the training set and suffer from a ”curse of dimensionality”: large number of feature values relative to training data size. We apply this technique to state-of-the-art steganalyzer proposed by Tom´as Pevn´y (54) to understand the feature space complexity and effectiveness of features for steganalysis. We show that using our approach, reduced-feature steganalyzers can be obtained that perform as well as the original steganalyzer. Based on our experimental observation, we then propose a new modeling technique for steganalysis by developing a Partially Ordered Markov Model (POMM) (23) to JPEG images and use its properties to train a Support Vector Machine. POMM generalizes the concept of local neighborhood directionality by using a partial order underlying the pixel locations. We show that the proposed steganalyzer outperforms a state-of-the-art steganalyzer by testing our approach with many different image databases, having a total of 20000 images. Finally, we provide a software package with a Graphical User Interface that has been developed to make this research accessible to local state forensic departments.

Oversampling the Minority Class in the Feature Space.

Science.gov (United States)

Perez-Ortiz, Maria; Gutierrez, Pedro Antonio; Tino, Peter; Hervas-Martinez, Cesar

2016-09-01

The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the classes will be linearly separable and synthetically generated patterns will lie on the minority class region. Since the feature space is not directly accessible, we use the empirical feature space (EFS) (a Euclidean space isomorphic to the feature space) for oversampling purposes. The proposed method is framed in the context of support vector machines, where the imbalanced data sets can pose a serious hindrance. The idea is investigated in three scenarios: 1) oversampling in the full and reduced-rank EFSs; 2) a kernel learning technique maximizing the data class separation to study the influence of the feature space structure (implicitly defined by the kernel function); and 3) a unified framework for preferential oversampling that spans some of the previous approaches in the literature. We support our investigation with extensive experiments over 50 imbalanced data sets.

Large-scale lithography for sub-500nm features

International Nuclear Information System (INIS)

Pelzer, R L; Steininger, T; Belier, Benoit; Julie, Gwenaelle

2006-01-01

The interest in micro- and nanotechnologies has grown rapidly in the last years. The applications are versatile and different techniques found its way into several research domains as optics, electronics, magnetism, fluidics, etc. In all of these fields integration of more and more functions on steadily decreasing device dimensions lead to an increase in structural density and feature size. Expensive and slow processes utilizing projection steppers or e-beam direct writer equipment are used to fabricate nm features today. A high throughput and cost effective method adapted on a standard mask aligner will be demonstrated, making features of below 300nm available on wafer-level. We will demonstrate results of 4 different resists exposed on a DUV proximity aligner and plasma etched for optical and biological applications in the sub-300nm range

Large-scale lithography for sub-500nm features

Energy Technology Data Exchange (ETDEWEB)

Pelzer, R L [Technology group, EV Group, DI Erich Thallner Str. 1, A-4780 Schaerding (Austria); Steininger, T [Technology group, EV Group, DI Erich Thallner Str. 1, A-4780 Schaerding (Austria); Belier, Benoit [CNRS, Institut d' Electronique Fondamentale, Universite Paris-Sud Bat 220, F- 91405 Orsay Cedex (France); Julie, Gwenaelle [CNRS, Institut d' Electronique Fondamentale, Universite Paris-Sud Bat 220, F- 91405 Orsay Cedex (France)

2006-04-01

The interest in micro- and nanotechnologies has grown rapidly in the last years. The applications are versatile and different techniques found its way into several research domains as optics, electronics, magnetism, fluidics, etc. In all of these fields integration of more and more functions on steadily decreasing device dimensions lead to an increase in structural density and feature size. Expensive and slow processes utilizing projection steppers or e-beam direct writer equipment are used to fabricate nm features today. A high throughput and cost effective method adapted on a standard mask aligner will be demonstrated, making features of below 300nm available on wafer-level. We will demonstrate results of 4 different resists exposed on a DUV proximity aligner and plasma etched for optical and biological applications in the sub-300nm range.

NREL: A Year in Clean Energy Innovations; A Review of NREL's 2011 Feature Stories

Energy Technology Data Exchange (ETDEWEB)

2012-04-01

This document is a compilation of articles featuring NREL research and development, deployment, commercialization, and outreach activities in 2011. The feature stories can be found online at http:www.nrel.gov/features/.

Treatment recommendations for DSM-5-defined mixed features.

Science.gov (United States)

Rosenblat, Joshua D; McIntyre, Roger S

2017-04-01

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) mixed features specifier provides a less restrictive definition of mixed mood states, compared to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR), including mood episodes that manifest with subthreshold symptoms of the opposite mood state. A limited number of studies have assessed the efficacy of treatments specifically for DSM-5-defined mixed features in mood disorders. As such, there is currently an inadequate amount of data to appropriately inform evidence-based treatment guidelines of DSM-5 defined mixed features. However, given the high prevalence and morbidity of mixed features, treatment recommendations based on the currently available evidence along with expert opinion may be of benefit. This article serves to provide these interim treatment recommendations while humbly acknowledging the limited amount of evidence currently available. Second-generation antipsychotics (SGAs) appear to have the greatest promise in the treatment of bipolar disorder (BD) with mixed features. Conventional mood stabilizing agents (ie, lithium and divalproex) may also be of benefit; however, they have been inadequately studied. In the treatment of major depressive disorder (MDD) with mixed features, the comparable efficacy of antidepressants versus other treatments, such as SGAs, remains unknown. As such, antidepressants remain first-line treatment of MDD with or without mixed features; however, there are significant safety concerns associated with antidepressant monotherapy when mixed features are present, which merits increased monitoring. Lurasidone is the only SGA monotherapy that has been shown to be efficacious specifically in the treatment of MDD with mixed features. Further research is needed to accurately determine the efficacy, safety, and tolerability of treatments specifically for mood episodes with mixed features to adequately inform

The New York City Research Initiative: A Model for Undergraduate and High School Student Research in Earth and Space Sciences and Space Technology

Science.gov (United States)

Scalzo, F.; Frost, J.; Carlson, B. E.; Marchese, P.; Rosenzweig, C.; Austin, S. A.; Peteet, D. M.; Druyan, L.; Fulakeza, M.; Gaffin, S.; Baruh, H.; Decker, S.; Thangam, S.; Miles, J.; Moshary, F.; Rossow, W.; Greenbaum, S.; Cheung, T. K.; Johnson, L. P.

2010-12-01

, Optimization Model for Future Lunar Colony, Models of Space Travel, and NMR Investigation of MnO2 Infused Carbon Nanofoams. We describe student research, significant results and enrichment activities during the Summer 2010. The NYCRI partners with the CUNY-GISS Center for Global Climate Change, an NSF REU Site. The NYCRI is supported by NASAâ^À^Ùs Earth Science Office, GSFC Education Office, as well as NASA and NSF awards to NYCRI College/University Principal Investigators.

Youth with Psychopathy Features Are Not a Discrete Class: A Taxometric Analysis

Science.gov (United States)

Murrie, Daniel C.; Marcus, David K.; Douglas, Kevin S.; Lee, Zina; Salekin, Randall T.; Vincent, Gina

2007-01-01

Background: Recently, researchers have sought to measure psychopathy-like features among youth in hopes of identifying children who may be progressing toward a particularly destructive form of adult pathology. However, it remains unclear whether psychopathy-like personality features among youth are best conceptualized as dimensional (distributed…

Elementary epistemological features of machine intelligence

OpenAIRE

Horvat, Marko

2008-01-01

Theoretical analysis of machine intelligence (MI) is useful for defining a common platform in both theoretical and applied artificial intelligence (AI). The goal of this paper is to set canonical definitions that can assist pragmatic research in both strong and weak AI. Described epistemological features of machine intelligence include relationship between intelligent behavior, intelligent and unintelligent machine characteristics, observable and unobservable entities and classification of in...

Attachment insecurity and perceived importance of relational features

NARCIS (Netherlands)

Ren, D.; Arriaga, X.B.; Mahan, E.R.

2017-01-01

Chronic attachment insecurity can affect the outlook people have on relationships. This research examines how attachment insecurity relates to perceived importance of various features in a romantic relationship (e.g., intimacy, independence). Consistent with predictions, the results from Studies 1–3

Language Recognition Using Latent Dynamic Conditional Random Field Model with Phonological Features

Directory of Open Access Journals (Sweden)

Sirinoot Boonsuk

2014-01-01

Full Text Available Spoken language recognition (SLR has been of increasing interest in multilingual speech recognition for identifying the languages of speech utterances. Most existing SLR approaches apply statistical modeling techniques with acoustic and phonotactic features. Among the popular approaches, the acoustic approach has become of greater interest than others because it does not require any prior language-specific knowledge. Previous research on the acoustic approach has shown less interest in applying linguistic knowledge; it was only used as supplementary features, while the current state-of-the-art system assumes independency among features. This paper proposes an SLR system based on the latent-dynamic conditional random field (LDCRF model using phonological features (PFs. We use PFs to represent acoustic characteristics and linguistic knowledge. The LDCRF model was employed to capture the dynamics of the PFs sequences for language classification. Baseline systems were conducted to evaluate the features and methods including Gaussian mixture model (GMM based systems using PFs, GMM using cepstral features, and the CRF model using PFs. Evaluated on the NIST LRE 2007 corpus, the proposed method showed an improvement over the baseline systems. Additionally, it showed comparable result with the acoustic system based on i-vector. This research demonstrates that utilizing PFs can enhance the performance.

The relation between intercultural competence, personality features and students’ intellectual development

Directory of Open Access Journals (Sweden)

Gridunova Marina V.

2017-01-01

Full Text Available In the light of globalisation processes accompanied by an increase in interethnic tensions, the research on personality features that contribute to a more efficient functioning in the intercultural context has become fairly topical. The aim of the conducted research was to explore the relation between intercultural competence, personality features and the level of intellectual development of students (N=121, 45% male students of a general education secondary school in Moscow. Bennett’s developmental model of intercultural sensitivity was used as the basis for studying intercultural competence, while the Scale of intercultural sensitivity was used as a diagnostic instrument. Personality features were defined in accordance with the Five Factor Model and diagnosed via the shorter version of the Five Factors questionnaire. The level of mental (intellectual development was measured using the normative School test of intellectual development (STID-2. Based on research results, it has been established that personality features such as conscientiousness, extraversion and neuroticism are related to the indicators of intercultural competence in the examined students, whereby the intensity of the relations is by far higher in the group of students with the lower level of intellectual development. At the same time, the students whose level of intellectual development is higher are more inclined towards accepting cultural differences, while those with the lower level of intellectual development tend to absolutise them.

Face Alignment via Regressing Local Binary Features.

Science.gov (United States)

Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian

2016-03-01

This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.

Featured collection introduction: contaminants of emerging concern II

Science.gov (United States)

Battaglin, William A.; Kolok, Alan; Battaglin, William; Kolok, Alan

2014-01-01

This collection of 13 articles focuses on CECs, and each of the articles highlights a specific aspect of this broad topic. The articles were solicited from researchers who participated in the second summer specialty conference on this topic, organized by the American Water Resources Association. The title of the conference was “CECs in Water Resources II: Research, Engineering and Community Action,” and the conference, as well as the articles in this featured collection, focus on a better and more comprehensive understanding of these contaminants. The conference was held in Denver, Colorado, on June 25-27, 2012, and approximately 125 conference attendees participated in an interdisciplinary forum of more than 75 presentations including keynote or plenary presentations by Dana Kolpin, Jorg Drewes, Heiko Schoenfuss, Chris Metcalfe, Vicki Blazer, and Tyrone Hayes. The first conference was held in 2007 and also produced a featured collection of articles (Battaglin and Kolpin, 2009).

Main Features in the Concept of Digital Bildung

DEFF Research Database (Denmark)

Tække, Jesper; Paulsen, Michael

The question of this paper is how we can understand the concept of Bildung in the time of digital media seen from a Klafkian perspective. It draws on Klafki (2014) by extrapolating what he suggest is the main features of Bildung, answering six questions: how can education 1. Foster persons who can......, bringing about different levels of what we from a Klafkian perspective call Digital Bildung. (2) We relate the Klafkian concept of Bildung to an action research experiment called Socio Media Education (SME). In this research project we have worked together with teachers in an upper secondary school...... features of Bildung are then discussed in regard to digital media. We do this by relating the Klafkian concept of Bildung to (1) a general theory about how schools seem to respond to the new digital challenges and possibilities. Our main point is that these responses can be divided into three waves...

Evaluating public awareness of new currency design features

Science.gov (United States)

DiNunzio, Lisa; Church, Sara E.

2002-04-01

One of the goals of the 1996 series design was to integrate highly recognizable features that enable the general public to more easily distinguish counterfeit from genuine notes, thereby reducing the chance of counterfeit notes being passed. The purpose of this study is to evaluate how knowledgeable the public is concerning the new currency, to identify the channels through which the public learns about new currency design, and to assess the usefulness of the new currency's authentication features. Also, the study will serve as a baseline measurement for future design studies and in comparative analysis with other countries. The results of the qualitative research will be described in the following sections of this paper. The quantitative research is scheduled to begin in February 2002, at the same time as the Netherlands' opinion poll of the Euro and NLG-notes in an effort to compare results.

Features of TMR for a Successful Clinical and Research Database

OpenAIRE

Pryor, David B.; Stead, William W.; Hammond, W. Edward; Califf, Robert M.; Rosati, Robert A.

1982-01-01

A database can be used for clinical practice and for research. The design of the database is important if both uses are to succeed. A clinical database must be efficient and flexible. A research database requires consistent observations recorded in a format which permits complete recall of the experience. In addition, the database should be designed to distinguish between missing data and negative responses, and to minimize transcription errors during the recording process.

Research on Optimal Observation Scale for Damaged Buildings after Earthquake Based on Optimal Feature Space

Science.gov (United States)

Chen, J.; Chen, W.; Dou, A.; Li, W.; Sun, Y.

2018-04-01

A new information extraction method of damaged buildings rooted in optimal feature space is put forward on the basis of the traditional object-oriented method. In this new method, ESP (estimate of scale parameter) tool is used to optimize the segmentation of image. Then the distance matrix and minimum separation distance of all kinds of surface features are calculated through sample selection to find the optimal feature space, which is finally applied to extract the image of damaged buildings after earthquake. The overall extraction accuracy reaches 83.1 %, the kappa coefficient 0.813. The new information extraction method greatly improves the extraction accuracy and efficiency, compared with the traditional object-oriented method, and owns a good promotional value in the information extraction of damaged buildings. In addition, the new method can be used for the information extraction of different-resolution images of damaged buildings after earthquake, then to seek the optimal observation scale of damaged buildings through accuracy evaluation. It is supposed that the optimal observation scale of damaged buildings is between 1 m and 1.2 m, which provides a reference for future information extraction of damaged buildings.

Definitions of engineered safety features and related features for nuclear power plants

International Nuclear Information System (INIS)

1986-01-01

In light water moderated, light water cooled nuclear power plants, definitions are given of engineered safety features which are designed to suppress or prevent dispersion of radioactive materials due to damage etc. of fuel at the times of power plant failures, and of related features which are designed to actuate or operate the engineered safety features. Contents are the following: scope of engineered safety features and of related features; classification of engineered safety features (direct systems and indirect systems) and of related features (auxiliaries, emergency power supply, and protective means). (Mori, K.)

TU-CD-BRB-07: Identification of Associations Between Radiologist-Annotated Imaging Features and Genomic Alterations in Breast Invasive Carcinoma, a TCGA Phenotype Research Group Study

Energy Technology Data Exchange (ETDEWEB)

Rao, A; Net, J [University of Miami, Miami, Florida (United States); Brandt, K [Mayo Clinic, Rochester, Minnesota (United States); Huang, E [National Cancer Institute, NIH, Bethesda, MD (United States); Freymann, J; Kirby, J [Leidos Biomedical Research Inc., Frederick, MD (United States); Burnside, E [University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin (United States); Morris, E; Sutton, E [Memorial Sloan Kettering Cancer Center, New York, NY (United States); Bonaccio, E [Roswell Park Cancer Institute, Buffalo, NY (United States); Giger, M; Jaffe, C [Univ Chicago, Chicago, IL (United States); Ganott, M; Zuley, M [University of Pittsburgh Medical Center - Magee Womens Hospital, Pittsburgh, Pennsylvania (United States); Le-Petross, H [MD Anderson Cancer Center, Houston, TX (United States); Dogan, B [UT MDACC, Houston, TX (United States); Whitman, G [UTMDACC, Houston, TX (United States)

2015-06-15

Purpose: To determine associations between radiologist-annotated MRI features and genomic measurements in breast invasive carcinoma (BRCA) from the Cancer Genome Atlas (TCGA). Methods: 98 TCGA patients with BRCA were assessed by a panel of radiologists (TCGA Breast Phenotype Research Group) based on a variety of mass and non-mass features according to the Breast Imaging Reporting and Data System (BI-RADS). Batch corrected gene expression data was obtained from the TCGA Data Portal. The Kruskal-Wallis test was used to assess correlations between categorical image features and tumor-derived genomic features (such as gene pathway activity, copy number and mutation characteristics). Image-derived features were also correlated with estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu) status. Multiple hypothesis correction was done using Benjamini-Hochberg FDR. Associations at an FDR of 0.1 were selected for interpretation. Results: ER status was associated with rim enhancement and peritumoral edema. PR status was associated with internal enhancement. Several components of the PI3K/Akt pathway were associated with rim enhancement as well as heterogeneity. In addition, several components of cell cycle regulation and cell division were associated with imaging characteristics.TP53 and GATA3 mutations were associated with lesion size. MRI features associated with TP53 mutation status were rim enhancement and peritumoral edema. Rim enhancement was associated with activity of RB1, PIK3R1, MAP3K1, AKT1,PI3K, and PIK3CA. Margin status was associated with HIF1A/ARNT, Ras/ GTP/PI3K, KRAS, and GADD45A. Axillary lymphadenopathy was associated with RB1 and BCL2L1. Peritumoral edema was associated with Aurora A/GADD45A, BCL2L1, CCNE1, and FOXA1. Heterogeneous internal nonmass enhancement was associated with EGFR, PI3K, AKT1, HF/MET, and EGFR/Erbb4/neuregulin 1. Diffuse nonmass enhancement was associated with HGF/MET/MUC20/SHIP

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review

Science.gov (United States)

Weal, Mark; Morrison, Leanne; Yardley, Lucy

2018-01-01

Background Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. Objective The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. Methods Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. Results A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. Conclusions Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features’ suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included

Biometric Features in Person Recognition Systems

Directory of Open Access Journals (Sweden)

Edgaras Ivanovas

2011-03-01

Full Text Available Lately a lot of research effort is devoted for recognition of a human being using his biometric characteristics. Biometric recognition systems are used in various applications, e. g., identification for state border crossing or firearm, which allows only enrolled persons to use it. In this paper biometric characteristics and their properties are reviewed. Development of high accuracy system requires distinctive and permanent characteristics, whereas development of user friendly system requires collectable and acceptable characteristics. It is showed that properties of biometric characteristics do not influence research effort significantly. Properties of biometric characteristic features and their influence are discussed.Article in Lithuanian

Detection of relationships among multi-modal brain imaging meta-features via information flow.

Science.gov (United States)

Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

2018-01-15

Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is

Attention to internal face features in unfamiliar face matching.

Science.gov (United States)

Fletcher, Kingsley I; Butavicius, Marcus A; Lee, Michael D

2008-08-01

Accurate matching of unfamiliar faces is vital in security and forensic applications, yet previous research has suggested that humans often perform poorly when matching unfamiliar faces. Hairstyle and facial hair can strongly influence unfamiliar face matching but are potentially unreliable cues. This study investigated whether increased attention to the more stable internal face features of eyes, nose, and mouth was associated with more accurate face-matching performance. Forty-three first-year psychology students decided whether two simultaneously presented faces were of the same person or not. The faces were displayed for either 2 or 6 seconds, and had either similar or dissimilar hairstyles. The level of attention to internal features was measured by the proportion of fixation time spent on the internal face features and the sensitivity of discrimination to changes in external feature similarity. Increased attention to internal features was associated with increased discrimination in the 2-second display-time condition, but no significant relationship was found in the 6-second condition. Individual differences in eye-movements were highly stable across the experimental conditions.

Micro-Doppler Feature Extraction and Recognition Based on Netted Radar for Ballistic Targets

Directory of Open Access Journals (Sweden)

Feng Cun-qian

2015-12-01

Full Text Available This study examines the complexities of using netted radar to recognize and resolve ballistic midcourse targets. The application of micro-motion feature extraction to ballistic mid-course targets is analyzed, and the current status of application and research on micro-motion feature recognition is concluded for singlefunction radar networks such as low- and high-resolution imaging radar networks. Advantages and disadvantages of these networks are discussed with respect to target recognition. Hybrid-mode radar networks combine low- and high-resolution imaging radar and provide a specific reference frequency that is the basis for ballistic target recognition. Main research trends are discussed for hybrid-mode networks that apply micromotion feature extraction to ballistic mid-course targets.

Understanding Participatory Action Research: A Qualitative Research Methodology Option

Science.gov (United States)

MacDonald, Cathy

2012-01-01

Participatory Action Research (PAR) is a qualitative research methodology option that requires further understanding and consideration. PAR is considered democratic, equitable, liberating, and life-enhancing qualitative inquiry that remains distinct from other qualitative methodologies (Kach & Kralik, 2006). Using PAR, qualitative features of an…

European nuclear features. Interview: Commissioner Piebalgs (DG TREN)

International Nuclear Information System (INIS)

2005-01-01

Fifth issue of the European Nuclear Features. A joint publication of atw, Nuclear Espana, and Revue Generale Nucleaire. Contents: Frontier research in the EU Scientific Council of the European Research Council announced proactive safety management strategies; Pebble Bed Modular Reactor; New experiences applying methodologies for control room I and C modernization; Experts from Taiwan fulfil their training in Madrid; Ensa's activities in the Asian Commercial Nuclear Power Market; Clearance survey approach for scrap metals from NPP. (orig.)

Genetic algorithms for thyroid gland ultrasound image feature reduction

Czech Academy of Sciences Publication Activity Database

Tesař, Ludvík; Smutek, D.; Jiskra, J.

2005-01-01

Roč. 3612, č. - (2005), s. 841-844 ISSN 0302-9743. [International Conference ICNC 2005 /1./. Changsha, 27.08.2005-29.08.2005] R&D Projects: GA AV ČR 1ET101050403 Institutional research plan: CEZ:AV0Z10750506 Keywords : medical imaging * classification * Bayes classifier * Huzzolini feature * pattern recognition Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/prace/20050229.pdf

The Influence of Universities' Organizational Features on Professorial Intellectual Leadership

Science.gov (United States)

Uslu, Baris; Welch, Anthony

2018-01-01

This research examines the intellectual leadership behaviours of senior academics at professoriate level, and the influences of institutional support practices, climate and communication in universities as main organizational features on these behaviours. To explore relations among research variables, qualitative data were collected by interviews…

Feature selection from a facial image for distinction of sasang constitution.

Science.gov (United States)

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun; Kim, Keun Ho

2009-09-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

Effects of Feature Extraction and Classification Methods on Cyberbully Detection

OpenAIRE

ÖZEL, Selma Ayşe; SARAÇ, Esra

2016-01-01

Cyberbullying is defined as an aggressive, intentional action against a defenseless person by using the Internet, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. In this study we show the effects of feature extraction, feature selection, and classification methods that are used, on the performance of automatic detection of cyberbullying. To perform the exper...

JCE Feature Columns

Science.gov (United States)

Holmes, Jon L.

1999-05-01

The Features area of JCE Online is now readily accessible through a single click from our home page. In the Features area each column is linked to its own home page. These column home pages also have links to them from the online Journal Table of Contents pages or from any article published as part of that feature column. Using these links you can easily find abstracts of additional articles that are related by topic. Of course, JCE Online+ subscribers are then just one click away from the entire article. Finding related articles is easy because each feature column "site" contains links to the online abstracts of all the articles that have appeared in the column. In addition, you can find the mission statement for the column and the email link to the column editor that I mentioned above. At the discretion of its editor, a feature column site may contain additional resources. As an example, the Chemical Information Instructor column edited by Arleen Somerville will have a periodically updated bibliography of resources for teaching and using chemical information. Due to the increase in the number of these resources available on the WWW, it only makes sense to publish this information online so that you can get to these resources with a simple click of the mouse. We expect that there will soon be additional information and resources at several other feature column sites. Following in the footsteps of the Chemical Information Instructor, up-to-date bibliographies and links to related online resources can be made available. We hope to extend the online component of our feature columns with moderated online discussion forums. If you have a suggestion for an online resource you would like to see included, let the feature editor or JCE Online (jceonline@chem.wisc.edu) know about it. JCE Internet Features JCE Internet also has several feature columns: Chemical Education Resource Shelf, Conceptual Questions and Challenge Problems, Equipment Buyers Guide, Hal's Picks, Mathcad

LC-IMS-MS Feature Finder: detecting multidimensional liquid chromatography, ion mobility and mass spectrometry features in complex datasets.

Science.gov (United States)

Crowell, Kevin L; Slysz, Gordon W; Baker, Erin S; LaMarche, Brian L; Monroe, Matthew E; Ibrahim, Yehia M; Payne, Samuel H; Anderson, Gordon A; Smith, Richard D

2013-11-01

The addition of ion mobility spectrometry to liquid chromatography-mass spectrometry experiments requires new, or updated, software tools to facilitate data processing. We introduce a command line software application LC-IMS-MS Feature Finder that searches for molecular ion signatures in multidimensional liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data by clustering deisotoped peaks with similar monoisotopic mass, charge state, LC elution time and ion mobility drift time values. The software application includes an algorithm for detecting and quantifying co-eluting chemical species, including species that exist in multiple conformations that may have been separated in the IMS dimension. LC-IMS-MS Feature Finder is available as a command-line tool for download at http://omics.pnl.gov/software/LC-IMS-MS_Feature_Finder.php. The Microsoft.NET Framework 4.0 is required to run the software. All other dependencies are included with the software package. Usage of this software is limited to non-profit research to use (see README). rds@pnnl.gov. Supplementary data are available at Bioinformatics online.

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

Directory of Open Access Journals (Sweden)

Lijun Wang

2013-01-01

Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

Structural-phenomenological features of the internal picture of doctors’ illnesses

Directory of Open Access Journals (Sweden)

Lazarenko, Victor A.

2016-06-01

Full Text Available The vocational activities of doctors and their social status do not ensure their health. And, falling ill, doctors don’t identify themselves with ordinary patients as they have a deep knowledge of medicine. Thus, the internal picture of a doctor’s illness is both a research and a practical problem: the problem of the psychoprevention of doctors’ illnesses at all stages of their professionalization. The purpose of the research was to study the phenomenological features of the internal picture of doctors’ illnesses using the structural approach. The total number of participants was 132. The experimental group consisted of 66 sick doctors, differentiated according to their stage of professionalization: vocational training (students, professional adaptation (interns, full professionalization (doctors. The control group consisted of 66 people who did not have any medical education. All the control subjects were hospitalized with chronic diseases during the study period. The organization of the research was carried out with the use of clinical-psychological and diagnostic methods, the methods of descriptive statistics, and comparative, multidimensional, and structural analysis. The research revealed the following phenomenological features of the internal picture of doctors’ illnesses: the prevalence of some anxiety in the doctors and high awareness of their health; the doctors’ altruistic orientation; their willingness to work despite difficulties; and their ability to achieve high results in different activities. The structural features of the doctors’ image of their own diseases on the cognitive level were the following: qualitative heterogeneity during in-service activities; a high degree of image integration during in-service activities; and stereotyped perceptions of the disease. The emotional level revealed the emotional distance between doctors and their patients, and the behavioral level revealed doctors’ disregard for the

Childhood maltreatment and its link to borderline personality disorder features in children: A systematic review approach.

Science.gov (United States)

Ibrahim, Jeyda; Cosgrave, Nicola; Woolgar, Matthew

2018-01-01

Borderline personality disorder has repeatedly been associated with a history of maltreatment in childhood; however, research on maltreatment and its link to borderline features in children is limited. The aim of this review is to synthesise the existing data on the association between maltreatment and borderline features in childhood. In total, 10 studies were included in this systematic review. Studies indicated that children with borderline features were more likely to have a history of maltreatment, and that children who had been maltreated were more likely to present with borderline features. Other risk factors such as cognitive and executive functioning deficits, parental dysfunction and genetic vulnerability were also identified across studies. This review adds to the literature by highlighting maltreatment as a risk factor for borderline features in childhood. Longitudinal research is required to establish the link between childhood borderline features and adult borderline features. Implications for early identification, prevention and intervention services are discussed.

The contribution of local features to familiarity judgments in music.

Science.gov (United States)

Bigand, Emmanuel; Gérard, Yannick; Molin, Paul

2009-07-01

The contributions of local and global features to object identification depend upon the context. For example, while local features play an essential role in identification of words and objects, the global features are more influential in face recognition. In order to evaluate the respective strengths of local and global features for face recognition, researchers usually ask participants to recognize human faces (famous or learned) in normal and scrambled pictures. In this paper, we address a similar issue in music. We present the results of an experiment in which musically untrained participants were asked to differentiate famous from unknown musical excerpts that were presented in normal or scrambled ways. Manipulating the size of the temporal window on which the scrambling procedure was applied allowed us to evaluate the minimal length of time necessary for participants to make a familiarity judgment. Quite surprisingly, the minimum duration for differentiation of famous from unknown pieces is extremely short. This finding highlights the contribution of very local features to music memory.

Feature selection using genetic algorithms for fetal heart rate analysis

International Nuclear Information System (INIS)

Xu, Liang; Redman, Christopher W G; Georgieva, Antoniya; Payne, Stephen J

2014-01-01

The fetal heart rate (FHR) is monitored on a paper strip (cardiotocogram) during labour to assess fetal health. If necessary, clinicians can intervene and assist with a prompt delivery of the baby. Data-driven computerized FHR analysis could help clinicians in the decision-making process. However, selecting the best computerized FHR features that relate to labour outcome is a pressing research problem. The objective of this study is to apply genetic algorithms (GA) as a feature selection method to select the best feature subset from 64 FHR features and to integrate these best features to recognize unfavourable FHR patterns. The GA was trained on 404 cases and tested on 106 cases (both balanced datasets) using three classifiers, respectively. Regularization methods and backward selection were used to optimize the GA. Reasonable classification performance is shown on the testing set for the best feature subset (Cohen's kappa values of 0.45 to 0.49 using different classifiers). This is, to our knowledge, the first time that a feature selection method for FHR analysis has been developed on a database of this size. This study indicates that different FHR features, when integrated, can show good performance in predicting labour outcome. It also gives the importance of each feature, which will be a valuable reference point for further studies. (paper)

Research and research impact of a technical university

DEFF Research Database (Denmark)

Schwarz, Annette Winkel; Schwarz, S.; Tijssen, R. J. W.

1998-01-01

The research output of the Danish Technical University (DTU) has been studied as an aspect of the organization's research policy and visibility in its international context. Papers published in the three-year period (1992-94) were grouped according to 20 clusters of research areas. Using citation...... analysis techniques, the dynamics of citation frequencies, and a number of other features of the research system, like self-citation, research collaborations, relative impact on the international literature, etc., could be studied. The methods can be used to analyze institutional and national research...

Color Vision 2018: Introduction by the feature editors.

Science.gov (United States)

Buck, Steven L; Baraas, Rigmor; Kremers, Jan; Lindsey, Delwin T; Nascimento, Sérgio M C; Webster, Michael A; Werner, John S

2018-04-01

This feature issue of the Journal of the Optical Society of America A (JOSA A) reflects the basic and applied research interests of members of the color vision community. Most of the articles stem from presentations at the 24th Biennial Symposium of the International Colour Vision Society (ICVS).

Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Muhammad Naveed Iqbal Qureshi

2017-09-01

Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

Directory of Open Access Journals (Sweden)

Zhang Xinzheng

2017-10-01

Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

Efficient Feature-Driven Visualization of Large-Scale Scientific Data

Energy Technology Data Exchange (ETDEWEB)

Lu, Aidong

2012-12-12

Very large, complex scientific data acquired in many research areas creates critical challenges for scientists to understand, analyze, and organize their data. The objective of this project is to expand the feature extraction and analysis capabilities to develop powerful and accurate visualization tools that can assist domain scientists with their requirements in multiple phases of scientific discovery. We have recently developed several feature-driven visualization methods for extracting different data characteristics of volumetric datasets. Our results verify the hypothesis in the proposal and will be used to develop additional prototype systems.

Assessment of features for automatic CTG analysis based on expert annotation.

Science.gov (United States)

Chudácek, Vacláv; Spilka, Jirí; Lhotská, Lenka; Janku, Petr; Koucký, Michal; Huptych, Michal; Bursa, Miroslav

2011-01-01

Cardiotocography (CTG) is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO) since 1960's used routinely by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the ever-used features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and the features are assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. Annotation derived from the panel of experts instead of the commonly utilized pH values was used for evaluation of the features on a large data set (552 records). We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. Number of acceleration and deceleration, interval index, as well as Lempel-Ziv complexity and Higuchi's fractal dimension are among the top five features.

Obligatory encoding of task-irrelevant features depletes working memory resources

OpenAIRE

Marshall, Louise; Bays, Paul M.

2013-01-01

Selective attention is often considered the “gateway” to visual working memory (VWM). However, the extent to which we can voluntarily control which of an object's features enter memory remains subject to debate. Recent research has converged on the concept of VWM as a limited commodity distributed between elements of a visual scene. Consequently, as memory load increases, the fidelity with which each visual feature is stored decreases. Here we used changes in recall precision to probe whether...

Discourse Features of Written Mexican Spanish: Current Research in Contrastive Rhetoric and Its Implications.

Science.gov (United States)

Montano-Harmon, Maria Rosario

1991-01-01

Analyzes discourse features of compositions written in Spanish by secondary school students in Mexico, draws comparisons with those written in English by Anglo-American students in the United States, and discusses the implications of the results for teaching and evaluating composition skills in Spanish language programs. (29 references) (GLR)

Perceptual approaches to finding features in data

Science.gov (United States)

Rogowitz, Bernice E.

2013-03-01

Electronic imaging applications hinge on the ability to discover features in data. For example, doctors examine diagnostic images for tumors, broken bones and changes in metabolic activity. Financial analysts explore visualizations of market data to find correlations, outliers and interaction effects. Seismologists look for signatures in geological data to tell them where to drill or where an earthquake may begin. These data are very diverse, including images, numbers, graphs, 3-D graphics, and text, and are growing exponentially, largely through the rise in automatic data collection technologies such as sensors and digital imaging. This paper explores important trends in the art and science of finding features in data, such as the tension between bottom-up and top-down processing, the semantics of features, and the integration of human- and algorithm-based approaches. This story is told from the perspective of the IS and T/SPIE Conference on Human Vision and Electronic Imaging (HVEI), which has fostered research at the intersection between human perception and the evolution of new technologies.

Classification Influence of Features on Given Emotions and Its Application in Feature Selection

Science.gov (United States)

Xing, Yin; Chen, Chuang; Liu, Li-Long

2018-04-01

In order to solve the problem that there is a large amount of redundant data in high-dimensional speech emotion features, we analyze deeply the extracted speech emotion features and select better features. Firstly, a given emotion is classified by each feature. Secondly, the recognition rate is ranked in descending order. Then, the optimal threshold of features is determined by rate criterion. Finally, the better features are obtained. When applied in Berlin and Chinese emotional data set, the experimental results show that the feature selection method outperforms the other traditional methods.

Investigating the influence of product perception and geometric features

DEFF Research Database (Denmark)

Perez Mata, Marta; Ahmed-Kristensen, Saeema; Brockhoff, Per B.

2017-01-01

. Surveys were conducted with 71 participants to gather their perceptions of 11 vase concepts. Advanced statistical analyses, including mixed models, were applied to allow generalisation of the results beyond the data sample. Significant relations between the desire to own a product and how the product......Research in emotional design and Kansei Engineering has shown that aesthetics play a significant role in the appeal of a product. This paper contributes to establishing a methodology to identify the relationships between perceptions, aesthetic features, desire to own and background of consumers...... and was simple and tall). An automated mixed model analysis was conducted and revealed that general rules can be found between aesthetic features, perceptions and ownership, which can apply across gender and culture. The findings include design rules that link aesthetic features with perceptions...

Feature Selection from a Facial Image for Distinction of Sasang Constitution

Directory of Open Access Journals (Sweden)

Imhoi Koo

2009-01-01

Full Text Available Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here.

Feature Selection from a Facial Image for Distinction of Sasang Constitution

Science.gov (United States)

Koo, Imhoi; Kim, Jong Yeol; Kim, Myoung Geun

2009-01-01

Recently, oriental medicine has received attention for providing personalized medicine through consideration of the unique nature and constitution of individual patients. With the eventual goal of globalization, the current trend in oriental medicine research is the standardization by adopting western scientific methods, which could represent a scientific revolution. The purpose of this study is to establish methods for finding statistically significant features in a facial image with respect to distinguishing constitution and to show the meaning of those features. From facial photo images, facial elements are analyzed in terms of the distance, angle and the distance ratios, for which there are 1225, 61 250 and 749 700 features, respectively. Due to the very large number of facial features, it is quite difficult to determine truly meaningful features. We suggest a process for the efficient analysis of facial features including the removal of outliers, control for missing data to guarantee data confidence and calculation of statistical significance by applying ANOVA. We show the statistical properties of selected features according to different constitutions using the nine distances, 10 angles and 10 rates of distance features that are finally established. Additionally, the Sasang constitutional meaning of the selected features is shown here. PMID:19745013

Essential Features for a Scholarly Journal Content Management and Peer Review Software

Directory of Open Access Journals (Sweden)

Fatima Sheikh Shoaie

2010-03-01

Full Text Available   The present study investigates the software used in scientific journals for content management and peer review, in order to identify the essential features. These softwares are analyzed and presented in tabular format. A questionnaire was prepared and submitted to a panel composed of 15 referees, editor in chief, software designers and researchers. The essential features for a software managing the review process were divided into three groups with populations of 10-15, 5-10 and 0-5 respectively. The majority of peer review process software features, in view of panelists, fell into a group of features with a population of 10-15. Finally it should be said that the features represented by the first group must be taken into account when designing or purchasing a peer review software. The second tier features (with population of 5-10 are recommended given journal's status and capabilities. The third tier features were altogether discounted due to low population

Efficient feature for classification of eye movements using electrooculography signals

Directory of Open Access Journals (Sweden)

Phukpattaranont Pornchai

2016-01-01

Full Text Available Electrooculography (EOG signal is widely and successfully used to detect activities of human eye. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, an efficient feature extracted from two directional EOG signals: vertical and horizontal signals has been presented and evaluated. There are the maximum peak and valley amplitude values, the maximum peak and valley position values, and slope, which are derived from both vertical and horizontal signals. In the experiments, EOG signals obtained from five healthy subjects with ten directional eye movements were employed: up, down, right, left, up-right, up-left, down-right down-left clockwise and counterclockwise. The mean feature values and their standard deviations have been reported. The difference between the mean values of the proposed feature from different eye movements can be clearly seen. Using the scatter plot, the differences in features can be also clearly observed. Results show that classification accuracy can approach 100% with a simple distinction feature rule. The proposed features can be useful for various advanced human-computer interface applications in future researches.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

Directory of Open Access Journals (Sweden)

Gang Hu

2018-01-01

Full Text Available The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved.

University Research and Development Activities: The Federal Income Tax Consequences of Research Contracts, Research Subsidiaries and Joint Ventures.

Science.gov (United States)

Kertz, Consuelo Lauda; Hasson, James K., Jr.

1986-01-01

Features of the federal income tax law applying to income received from commercially funded university-based scientific research and development activities are discussed, including: industry-sponsored research contracts, separately incorporated entities, partnerships and joint ventures, subsidiaries and unrelated income consequences of…

An Effective Combined Feature For Web Based Image Retrieval

Directory of Open Access Journals (Sweden)

H.M.R.B Herath

2015-08-01

Full Text Available Abstract Technology advances as well as the emergence of large scale multimedia applications and the revolution of the World Wide Web has changed the world into a digital age. Anybody can use their mobile phone to take a photo at any time anywhere and upload that image to ever growing image databases. Development of effective techniques for visual and multimedia retrieval systems is one of the most challenging and important directions of the future research. This paper proposes an effective combined feature for web based image retrieval. Frequently used colour and texture features are explored in order to develop a combined feature for this purpose. Widely used three colour features Colour moments Colour coherence vector and Colour Correlogram and three texture features Grey Level Co-occurrence matrix Tamura features and Gabor filter were analyzed for their performance. Precision and Recall were used to evaluate the performance of each of these techniques. By comparing precision and recall values the methods that performed best were taken and combined to form a hybrid feature. The developed combined feature was evaluated by developing a web based CBIR system. A web crawler was used to first crawl through Web sites and images found in those sites are downloaded and the combined feature representation technique was used to extract image features. The test results indicated that this web system can be used to index web images with the combined feature representation schema and to find similar images. Random image retrievals using the web system shows that the combined feature can be used to retrieve images belonging to the general image domain. Accuracy of the retrieval can be noted high for natural images like outdoor scenes images of flowers etc. Also images which have a similar colour and texture distribution were retrieved as similar even though the images were belonging to deferent semantic categories. This can be ideal for an artist who wants

Iris-based medical analysis by geometric deformation features.

Science.gov (United States)

Ma, Lin; Zhang, D; Li, Naimin; Cai, Yan; Zuo, Wangmeng; Wang, Kuanguan

2013-01-01

Iris analysis studies the relationship between human health and changes in the anatomy of the iris. Apart from the fact that iris recognition focuses on modeling the overall structure of the iris, iris diagnosis emphasizes the detecting and analyzing of local variations in the characteristics of irises. This paper focuses on studying the geometrical structure changes in irises that are caused by gastrointestinal diseases, and on measuring the observable deformations in the geometrical structures of irises that are related to roundness, diameter and other geometric forms of the pupil and the collarette. Pupil and collarette based features are defined and extracted. A series of experiments are implemented on our experimental pathological iris database, including manual clustering of both normal and pathological iris images, manual classification by non-specialists, manual classification by individuals with a medical background, classification ability verification for the proposed features, and disease recognition by applying the proposed features. The results prove the effectiveness and clinical diagnostic significance of the proposed features and a reliable recognition performance for automatic disease diagnosis. Our research results offer a novel systematic perspective for iridology studies and promote the progress of both theoretical and practical work in iris diagnosis.

Hemorrhage detection in MRI brain images using images features

Science.gov (United States)

Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

FAST DISCRETE CURVELET TRANSFORM BASED ANISOTROPIC FEATURE EXTRACTION FOR IRIS RECOGNITION

Directory of Open Access Journals (Sweden)

Amol D. Rahulkar

2010-11-01

Full Text Available The feature extraction plays a very important role in iris recognition. Recent researches on multiscale analysis provide good opportunity to extract more accurate information for iris recognition. In this work, a new directional iris texture features based on 2-D Fast Discrete Curvelet Transform (FDCT is proposed. The proposed approach divides the normalized iris image into six sub-images and the curvelet transform is applied independently on each sub-image. The anisotropic feature vector for each sub-image is derived using the directional energies of the curvelet coefficients. These six feature vectors are combined to create the resultant feature vector. During recognition, the nearest neighbor classifier based on Euclidean distance has been used for authentication. The effectiveness of the proposed approach has been tested on two different databases namely UBIRIS and MMU1. Experimental results show the superiority of the proposed approach.

Obligatory encoding of task-irrelevant features depletes working memory resources.

Science.gov (United States)

Marshall, Louise; Bays, Paul M

2013-02-18

Selective attention is often considered the "gateway" to visual working memory (VWM). However, the extent to which we can voluntarily control which of an object's features enter memory remains subject to debate. Recent research has converged on the concept of VWM as a limited commodity distributed between elements of a visual scene. Consequently, as memory load increases, the fidelity with which each visual feature is stored decreases. Here we used changes in recall precision to probe whether task-irrelevant features were encoded into VWM when individuals were asked to store specific feature dimensions. Recall precision for both color and orientation was significantly enhanced when task-irrelevant features were removed, but knowledge of which features would be probed provided no advantage over having to memorize both features of all items. Next, we assessed the effect an interpolated orientation-or color-matching task had on the resolution with which orientations in a memory array were stored. We found that the presence of orientation information in the second array disrupted memory of the first array. The cost to recall precision was identical whether the interfering features had to be remembered, attended to, or could be ignored. Therefore, it appears that storing, or merely attending to, one feature of an object is sufficient to promote automatic encoding of all its features, depleting VWM resources. However, the precision cost was abolished when the match task preceded the memory array. So, while encoding is automatic, maintenance is voluntary, allowing resources to be reallocated to store new visual information.

Conditional Mutual Information Based Feature Selection for Classification Task

Czech Academy of Sciences Publication Activity Database

Novovičová, Jana; Somol, Petr; Haindl, Michal; Pudil, Pavel

2007-01-01

Roč. 45, č. 4756 (2007), s. 417-426 ISSN 0302-9743 R&D Projects: GA MŠk 1M0572; GA AV ČR IAA2075302 EU Projects: European Commission(XE) 507752 - MUSCLE Grant - others:GA MŠk(CZ) 2C06019 Institutional research plan: CEZ:AV0Z10750506 Keywords : Pattern classification * feature selection * conditional mutual information * text categorization Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.402, year: 2005

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

Science.gov (United States)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

Extending GIS Technology to Study Karst Features of Southeastern Minnesota

Science.gov (United States)

Gao, Y.; Tipping, R. G.; Alexander, E. C.; Alexander, S. C.

2001-12-01

This paper summarizes ongoing research on karst feature distribution of southeastern Minnesota. The main goals of this interdisciplinary research are: 1) to look for large-scale patterns in the rate and distribution of sinkhole development; 2) to conduct statistical tests of hypotheses about the formation of sinkholes; 3) to create management tools for land-use managers and planners; and 4) to deliver geomorphic and hydrogeologic criteria for making scientifically valid land-use policies and ethical decisions in karst areas of southeastern Minnesota. Existing county and sub-county karst feature datasets of southeastern Minnesota have been assembled into a large GIS-based database capable of analyzing the entire data set. The central database management system (DBMS) is a relational GIS-based system interacting with three modules: GIS, statistical and hydrogeologic modules. ArcInfo and ArcView were used to generate a series of 2D and 3D maps depicting karst feature distributions in southeastern Minnesota. IRIS ExplorerTM was used to produce satisfying 3D maps and animations using data exported from GIS-based database. Nearest-neighbor analysis has been used to test sinkhole distributions in different topographic and geologic settings. All current nearest-neighbor analyses testify that sinkholes in southeastern Minnesota are not evenly distributed in this area (i.e., they tend to be clustered). More detailed statistical methods such as cluster analysis, histograms, probability estimation, correlation and regression have been used to study the spatial distributions of some mapped karst features of southeastern Minnesota. A sinkhole probability map for Goodhue County has been constructed based on sinkhole distribution, bedrock geology, depth to bedrock, GIS buffer analysis and nearest-neighbor analysis. A series of karst features for Winona County including sinkholes, springs, seeps, stream sinks and outcrop has been mapped and entered into the Karst Feature Database

Synoptic evaluation of scale-dependent metrics for hydrographic line feature geometry

Science.gov (United States)

Stanislawski, Larry V.; Buttenfield, Barbara P.; Raposo, Paulo; Cameron, Madeline; Falgout, Jeff T.

2015-01-01

Methods of acquisition and feature simplification for vector feature data impact cartographic representations and scientific investigations of these data, and are therefore important considerations for geographic information science (Haunert and Sester 2008). After initial collection, linear features may be simplified to reduce excessive detail or to furnish a reduced-scale version of the features through cartographic generalization (Regnauld and McMaster 2008, Stanislawski et al. 2014). A variety of algorithms exist to simplify linear cartographic features, and all of the methods affect the positional accuracy of the features (Shahriari and Tao 2002, Regnauld and McMaster 2008, Stanislawski et al. 2012). In general, simplification operations are controlled by one or more tolerance parameters that limit the amount of positional change the operation can make to features. Using a single tolerance value can have varying levels of positional change on features; depending on local shape, texture, or geometric characteristics of the original features (McMaster and Shea 1992, Shahriari and Tao 2002, Buttenfield et al. 2010). Consequently, numerous researchers have advocated calibration of simplification parameters to control quantifiable properties of resulting changes to the features (Li and Openshaw 1990, Raposo 2013, Tobler 1988, Veregin 2000, and Buttenfield, 1986, 1989).This research identifies relations between local topographic conditions and geometric characteristics of linear features that are available in the National Hydrography Dataset (NHD). The NHD is a comprehensive vector dataset of surface 18 th ICA Workshop on Generalisation and Multiple Representation, Rio de Janiero, Brazil 2015 2 water features within the United States that is maintained by the U.S. Geological Survey (USGS). In this paper, geometric characteristics of cartographic representations for natural stream and river features are summarized for subbasin watersheds within entire regions of the

Identifying features of pocket parks that may be related to health promoting use

DEFF Research Database (Denmark)

Peschardt, Karin Kragsig; Stigsdotter, Ulrika K.; Schipperijn, Jasper

2016-01-01

. The results show that ‘green features’ do not seem to be of crucial importance for ‘socialising’ whereas, as expected, features promoting gathering should be prioritised. For ‘rest and restitution’, the main results show that ‘green ground cover’ and ‘enclosed green niches’ are important, while ‘disturbing......Urban green spaces have been shown to promote health and well-being and recent research indicates that the two primary potentially health promoting uses of pocket parks are ‘rest and restitution’ and ‘socialising’. The aim of this study is to identify features in pocket parks that may support...... features’ (playground, view outside park) should be avoided. The results add knowledge about the features which support the health promoting use of pocket parks to the existing body of research....

Iris features-based heart disease diagnosis by computer vision

Science.gov (United States)

Nguchu, Benedictor A.; Li, Li

2017-07-01

The study takes advantage of several new breakthroughs in computer vision technology to develop a new mid-irisbiomedical platform that processes iris image for early detection of heart-disease. Guaranteeing early detection of heart disease provides a possibility of having non-surgical treatment as suggested by biomedical researchers and associated institutions. However, our observation discovered that, a clinical practicable solution which could be both sensible and specific for early detection is still lacking. Due to this, the rate of majority vulnerable to death is highly increasing. The delayed diagnostic procedures, inefficiency, and complications of available methods are the other reasons for this catastrophe. Therefore, this research proposes the novel IFB (Iris Features Based) method for diagnosis of premature, and early stage heart disease. The method incorporates computer vision and iridology to obtain a robust, non-contact, nonradioactive, and cost-effective diagnostic tool. The method analyzes abnormal inherent weakness in tissues, change in color and patterns, of a specific region of iris that responds to impulses of heart organ as per Bernard Jensen-iris Chart. The changes in iris infer the presence of degenerative abnormalities in heart organ. These changes are precisely detected and analyzed by IFB method that includes, tensor-based-gradient(TBG), multi orientations gabor filters(GF), textural oriented features(TOF), and speed-up robust features(SURF). Kernel and Multi class oriented support vector machines classifiers are used for classifying normal and pathological iris features. Experimental results demonstrated that the proposed method, not only has better diagnostic performance, but also provides an insight for early detection of other diseases.

Cloud field classification based on textural features

Science.gov (United States)

Sengupta, Sailes Kumar

1989-01-01

An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes

Mead features fermented by Saccharomyces cerevisiae (lalvin k1 ...

African Journals Online (AJOL)

Eduardo Morales

Full Length Research Paper. Mead features fermented by Saccharomyces cerevisiae. (lalvin k1-1116). Eduardo Marin MORALES1*, Valmir Eduardo ALCARDE2 and Dejanira de Franceschi de. ANGELIS1. 1Department of Biochemistry and Microbiology, Institute of Biosciences, UNESP - Univ Estadual Paulista, Av. 24-A,.

Psychological features of attention in archery

Directory of Open Access Journals (Sweden)

Ekaterina Yu. Korobeynikova

2017-06-01

Full Text Available The issue of attention is one of the leading in sports psychology. Quite often, athletes’ failures in competitions are ultimately connected with the attention processes, i.e. distraction, switching or loss of concentration. Simultaneously, each particular kind of sport is distinguished by a specific competitive situation and accordingly presents a number of requirements to athletes, including attention features. Archery is no exception. Thus, in shooting sports, concentration and stability of attention are often deemed as the most significant features of attention. The paper is devoted to the study of the attention dynamic properties in archers. Attention features of athletes were assessed depending on the sports major, gender, age, experience and level of competence. 65 archers from different regions of Russia took part in the study, including 34 males and 31 females, the average age being 16.29 ± 1.74. Experience ranges from 1 year to 8 years, average experience is 4.46 ± 1.93. The research results showed that archers are characterized by high indicators of stability of attention, and also high efficiency of solving attention problems. The professional success of archery was associated with the ability to distribute attention when necessary. At the same time, there were no significant differences in the features of attention for recurved and compound archers, which indicates the uniformity of tasks related to attention in the sporting practice of archers. Summing up, it is necessary to include skills in the distribution of attention in the program of psychological training of archers.

Unsupervised Feature Subset Selection

DEFF Research Database (Denmark)

Søndberg-Madsen, Nicolaj; Thomsen, C.; Pena, Jose

2003-01-01

This paper studies filter and hybrid filter-wrapper feature subset selection for unsupervised learning (data clustering). We constrain the search for the best feature subset by scoring the dependence of every feature on the rest of the features, conjecturing that these scores discriminate some ir...... irrelevant features. We report experimental results on artificial and real data for unsupervised learning of naive Bayes models. Both the filter and hybrid approaches perform satisfactorily....

Users’ attention behaviors and features in internet forum

Directory of Open Access Journals (Sweden)

Yong-Zhong Sha

2015-11-01

Full Text Available Purpose: Attention resource is scarce. Organizing community activities in online forums faces the challenge of attracting users’ limited attention. Understanding how users of online forums allocate, maintain, and change their attentional focus and what features of online forms influence their attention behaviors is critical for effective information design. This paper seeks understanding of users’ attention behaviors and features when they participate in discussions in online forums. Design/methodology/approach: A conceptual model was established to explore the indicator system of attention’s measurement. The related attention data were collected from Alexa Access Statistics Tool and Katie community. Then this paper computed the correlation coefficient and regression relationship between the indicators of visual attention and cognitive attention. Thereafter this paper analyzed and discussed users’ attention behaviors and features in Internet forum. Findings: Relevant bivariate correlation analysis and regression analysis discovers that Internet forum's attention is mainly as visual attention in users’ early involvement. Attention resources can be transformed. In a deep participation, users’ cognitive attention is more significant. Meanwhile cognitive attention behaviors’ further development will lead to the phenomenon that cognitive attention input is prone to increase faster in the early duration. That means in-depth discussion and interaction are more likely to appear in the early stages of participation. Research limitations/implications: There are some limitations about this study. The indicators are not comprehensive enough because factors affecting the distribution of attention resources in Internet forums are complex. We didn’t distinguish different types of Internet forums when we collected the relevant data. Future research will focus more on how to obtain comprehensive attention data. Originality/value: T his paper

Neural tuning to low-level features of speech throughout the perisylvian cortex

NARCIS (Netherlands)

Berezutskaya, Y.; Freudenburg, Z.V.; Güçlü, U.; Gerven, M.A.J. van; Ramsey, N.F.

2017-01-01

Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus towards anterior superior

Neural tuning to low-level features of speech throughout the perisylvian cortex

NARCIS (Netherlands)

Berezutskaya, Julia; Freudenburg, Zachary V.; Güçlü, Umut; van Gerven, Marcel A.J.; Ramsey, Nick F.

2017-01-01

Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus toward anterior superior

When do letter features migrate? A boundary condition for feature-integration theory.

Science.gov (United States)

Butler, B E; Mewhort, D J; Browse, R A

1991-01-01

Feature-integration theory postulates that a lapse of attention will allow letter features to change position and to recombine as illusory conjunctions (Treisman & Paterson, 1984). To study such errors, we used a set of uppercase letters known to yield illusory conjunctions in each of three tasks. The first, a bar-probe task, showed whole-character mislocations but not errors based on feature migration and recombination. The second, a two-alternative forced-choice detection task, allowed subjects to focus on the presence or absence of subletter features and showed illusory conjunctions based on feature migration and recombination. The third was also a two-alternative forced-choice detection task, but we manipulated the subjects' knowledge of the shape of the stimuli: In the case-certain condition, the stimuli were always in uppercase, but in the case-uncertain condition, the stimuli could appear in either upper- or lowercase. Subjects in the case-certain condition produced illusory conjunctions based on feature recombination, whereas subjects in the case-uncertain condition did not. The results suggest that when subjects can view the stimuli as feature groups, letter features regroup as illusory conjunctions; when subjects encode the stimuli as letters, whole items may be mislocated, but subletter features are not. Thus, illusory conjunctions reflect the subject's processing strategy, rather than the architecture of the visual system.

Featureous: an Integrated Approach to Location, Analysis and Modularization of Features in Java Applications

DEFF Research Database (Denmark)

Olszak, Andrzej

, it is essential that features are properly modularized within the structural organization of software systems. Nevertheless, in many object-oriented applications, features are not represented explicitly. Consequently, features typically end up scattered and tangled over multiple source code units......, such as architectural layers, packages and classes. This lack of modularization is known to make application features difficult to locate, to comprehend and to modify in isolation from one another. To overcome these problems, this thesis proposes Featureous, a novel approach to location, analysis and modularization...... quantitative and qualitative results suggest that Featureous succeeds at efficiently locating features in unfamiliar codebases, at aiding feature-oriented comprehension and modification, and at improving modularization of features using Java packages....

MindDigger: Feature Identification and Opinion Association for Chinese Movie Reviews

Science.gov (United States)

Zhao, Lili; Li, Chunping

In this paper, we present a prototype system called MindDigger, which can be used to analyze the opinions in Chinese movie reviews. Different from previous research that employed techniques on product reviews, we focus on Chinese movie reviews, in which opinions are expressed in subtle and varied ways. The system designed in this work aims to extract the opinion expressions and assign them to the corresponding features. The core tasks include feature and opinion extraction, and feature-opinion association. To deal with Chinese effectively, several novel approaches based on syntactic analysis are proposed in this paper. Running results show the performance is satisfactory.

Innovations in individual feature history management - The significance of feature-based temporal model

Science.gov (United States)

Choi, J.; Seong, J.C.; Kim, B.; Usery, E.L.

2008-01-01

A feature relies on three dimensions (space, theme, and time) for its representation. Even though spatiotemporal models have been proposed, they have principally focused on the spatial changes of a feature. In this paper, a feature-based temporal model is proposed to represent the changes of both space and theme independently. The proposed model modifies the ISO's temporal schema and adds new explicit temporal relationship structure that stores temporal topological relationship with the ISO's temporal primitives of a feature in order to keep track feature history. The explicit temporal relationship can enhance query performance on feature history by removing topological comparison during query process. Further, a prototype system has been developed to test a proposed feature-based temporal model by querying land parcel history in Athens, Georgia. The result of temporal query on individual feature history shows the efficiency of the explicit temporal relationship structure. ?? Springer Science+Business Media, LLC 2007.

An expert botanical feature extraction technique based on phenetic features for identifying plant species.

Directory of Open Access Journals (Sweden)

Hoshang Kolivand

Full Text Available In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.

An expert botanical feature extraction technique based on phenetic features for identifying plant species

Science.gov (United States)

Fern, Bong Mei; Rahim, Mohd Shafry Mohd; Sulong, Ghazali; Baker, Thar; Tully, David

2018-01-01

In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost. PMID:29420568

Dependency Parsing with Transformed Feature

Directory of Open Access Journals (Sweden)

Fuxiang Wu

2017-01-01

Full Text Available Dependency parsing is an important subtask of natural language processing. In this paper, we propose an embedding feature transforming method for graph-based parsing, transform-based parsing, which directly utilizes the inner similarity of the features to extract information from all feature strings including the un-indexed strings and alleviate the feature sparse problem. The model transforms the extracted features to transformed features via applying a feature weight matrix, which consists of similarities between the feature strings. Since the matrix is usually rank-deficient because of similar feature strings, it would influence the strength of constraints. However, it is proven that the duplicate transformed features do not degrade the optimization algorithm: the margin infused relaxed algorithm. Moreover, this problem can be alleviated by reducing the number of the nearest transformed features of a feature. In addition, to further improve the parsing accuracy, a fusion parser is introduced to integrate transformed and original features. Our experiments verify that both transform-based and fusion parser improve the parsing accuracy compared to the corresponding feature-based parser.

Using a Feature Film to Promote Scientific Enquiry

Science.gov (United States)

Hadzigeorgiou, Yannis; Kodakos, Tassos; Garganourakis, Vassilios

2010-01-01

This article reports on an action research project undertaken with the primary aim of investigating the extent to which a feature film, whose plot included Tesla's demonstrations on the wireless transmission of electrical energy, can promote scientific enquiry. The class that participated in this project was an 11th grade class in a rural area of…

Information Commons Features Cutting-Edge Conservation and Technology

Science.gov (United States)

Gilroy, Marilyn

2011-01-01

This article features Richard J. Klarchek Information Commons (IC) at Loyola University Chicago, an all-glass library building on the shore of Chicago's Lake Michigan that is not only a state-of-the-art digital research library and study space--it also runs on cutting-edge energy technology. The building has attracted attention and visitors from…

Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

Science.gov (United States)

Chan, Yi-Tung; Wang, Shuenn-Jyi; Tsai, Chung-Hsien

2017-09-01

Public safety is a matter of national security and people's livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method's performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.

Memory and Forgetfulness: NIH Research

Science.gov (United States)

... of this page please turn Javascript on. Feature: Memory & Forgetfulness NIH Research Past Issues / Summer 2013 Table ... agency for research on Alzheimer's disease and related memory research. An analysis funded by the NIA finds ...

A statistical-textural-features based approach for classification of solid drugs using surface microscopic images.

Science.gov (United States)

Tahir, Fahima; Fahiem, Muhammad Abuzar

2014-01-01

The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evaluate the performance of the proposed method. Multiple textural features are extracted from the surface of the defective and nondefective tablets. These textural features are gray level cooccurrence matrix, run length matrix, histogram, autoregressive model and HAAR wavelet. Total textural features extracted from images are 281. We performed an analysis on all those 281, top 15, and top 2 features. Top 15 features are extracted using three different feature reduction techniques: chi-square, gain ratio and relief-F. In this research we have used three different classifiers: support vector machine, K-nearest neighbors and naïve Bayes to calculate the accuracies against proposed method using two experiments, that is, leave-one-out cross-validation technique and train test models. We tested each classifier against all selected features and then performed the comparison of their results. The experimental work resulted in that in most of the cases SVM performed better than the other two classifiers.

Effects of Feature Extraction and Classification Methods on Cyberbully Detection

Directory of Open Access Journals (Sweden)

Esra SARAÇ

2016-12-01

Full Text Available Cyberbullying is defined as an aggressive, intentional action against a defenseless person by using the Internet, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. In this study we show the effects of feature extraction, feature selection, and classification methods that are used, on the performance of automatic detection of cyberbullying. To perform the experiments FormSpring.me dataset is used and the effects of preprocessing methods; several classifiers like C4.5, Naïve Bayes, kNN, and SVM; and information gain and chi square feature selection methods are investigated. Experimental results indicate that the best classification results are obtained when alphabetic tokenization, no stemming, and no stopwords removal are applied. Using feature selection also improves cyberbully detection performance. When classifiers are compared, C4.5 performs the best for the used dataset.

The feature-weighted receptive field: an interpretable encoding model for complex feature spaces.

Science.gov (United States)

St-Yves, Ghislain; Naselaris, Thomas

2017-06-20

We introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The fwRF is organized around the notion of a feature map-a transformation of visual stimuli into visual features that preserves the topology of visual space (but not necessarily the native resolution of the stimulus). The key assumption of the fwRF model is that activity in each voxel encodes variation in a spatially localized region across multiple feature maps. This region is fixed for all feature maps; however, the contribution of each feature map to voxel activity is weighted. Thus, the model has two separable sets of parameters: "where" parameters that characterize the location and extent of pooling over visual features, and "what" parameters that characterize tuning to visual features. The "where" parameters are analogous to classical receptive fields, while "what" parameters are analogous to classical tuning functions. By treating these as separable parameters, the fwRF model complexity is independent of the resolution of the underlying feature maps. This makes it possible to estimate models with thousands of high-resolution feature maps from relatively small amounts of data. Once a fwRF model has been estimated from data, spatial pooling and feature tuning can be read-off directly with no (or very little) additional post-processing or in-silico experimentation. We describe an optimization algorithm for estimating fwRF models from data acquired during standard visual neuroimaging experiments. We then demonstrate the model's application to two distinct sets of features: Gabor wavelets and features supplied by a deep convolutional neural network. We show that when Gabor feature maps are used, the fwRF model recovers receptive fields and spatial frequency tuning functions consistent with known organizational principles of the visual cortex. We also show that a fwRF model can be used to regress entire deep

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

Science.gov (United States)

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

Learning about the internal structure of categories through classification and feature inference.

Science.gov (United States)

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Enhancing the Performance of LibSVM Classifier by Kernel F-Score Feature Selection

Science.gov (United States)

Sarojini, Balakrishnan; Ramaraj, Narayanasamy; Nickolas, Savarimuthu

Medical Data mining is the search for relationships and patterns within the medical datasets that could provide useful knowledge for effective clinical decisions. The inclusion of irrelevant, redundant and noisy features in the process model results in poor predictive accuracy. Much research work in data mining has gone into improving the predictive accuracy of the classifiers by applying the techniques of feature selection. Feature selection in medical data mining is appreciable as the diagnosis of the disease could be done in this patient-care activity with minimum number of significant features. The objective of this work is to show that selecting the more significant features would improve the performance of the classifier. We empirically evaluate the classification effectiveness of LibSVM classifier on the reduced feature subset of diabetes dataset. The evaluations suggest that the feature subset selected improves the predictive accuracy of the classifier and reduce false negatives and false positives.

Discrimination Features of Chromatic Figures in Various Background Characteristics

Directory of Open Access Journals (Sweden)

Y A Chudina

2013-12-01

Full Text Available Visual recognition features of images with different figure-ground segregation have been considered in the article. The research was carried out within the framework of Sokolov and Izmaylov’s spherical model and was based on the construction of color objects discrimination models depending on the changes of background characteristics. The research has revealed the specific influence of the background on figure discrimination. The derived models reflect the mechanisms of the all-in-one perception of the visual space.

Factors that affect micro-tooling features created by direct printing approach

Science.gov (United States)

Kumbhani, Mayur N.

Current market required faster pace production of smaller, better, and improved products in shorter amount of time. Traditional high-rate manufacturing process such as hot embossing, injection molding, compression molding, etc. use tooling to replicate feature on a products. Miniaturization of many product in the field of biomedical, electronics, optical, and microfluidic is occurring on a daily bases. There is a constant need to produce cheaper, and faster tooling, which can be utilize by existing manufacturing processes. Traditionally, in order to manufacture micron size tooling features processes such as micro-machining, Electrical Discharge Machining (EDM), etc. are utilized. Due to a higher difficulty to produce smaller size features, and longer production cycle time, various additive manufacturing approaches are proposed, e.g. selective laser sintering (SLS), inkjet printing (3DP), fused deposition modeling (FDM), etc. were proposed. Most of these approaches can produce net shaped products from different materials such as metal, ceramic, or polymers. Several attempts were made to produce tooling features using additive manufacturing approaches. Most of these produced tooling were not cost effective, and the life cycle of these tooling was reported short. In this research, a method to produce tooling features using direct printing approach, where highly filled feedstock was dispensed on a substrate. This research evaluated different natural binders, such as guar gum, xanthan gum, and sodium carboxymethyl cellulose (NaCMC) and their combinations were evaluated. The best binder combination was then use to evaluate effect of different metal (316L stainless steel (3 mum), 316 stainless steel (45 mum), and 304 stainless steel (45 mum)) particle size on feature quality. Finally, the effect of direct printing process variables such as dispensing tip internal diameter (500 mum, and 333 mum) at different printing speeds were evaluated.

[Physiological features of skin ageing in human].

Science.gov (United States)

Tikhonova, I V; Tankanag, A V; Chemeris, N K

2013-01-01

The issue deals with the actual problem of gerontology, notably physiological features of human skin ageing. In the present review the authors have considered the kinds of ageing, central factors, affected on the ageing process (ultraviolet radiation and oxidation stress), as well as the research guidelines of the ageing changes in the skin structure and fuctions: study of mechanical properties, microcirculation, pH and skin thickness. The special attention has been payed to the methods of assessment of skin blood flow, and to results of investigations of age features of peripheral microhemodynamics. The laser Doppler flowmetry technique - one of the modern, noninvasive and extensively used methods for the assessmant of skin blood flow microcirculation system has been expanded in the review. The main results of the study of the ageing changes of skin blood perfusion using this method has been also presented.

Language Features and Culture Features on Short Message

Institute of Scientific and Technical Information of China (English)

王佳

2013-01-01

Mobile phone is regarded as“the fifth media”after newspaper,radio,TV and the Internet.The mobile phone short message further highlights the importance of written signs in communication.“The thumb revolution”is eagerly anticipating one kind of trend by the hand replace of mouth,sound substitute for the quiet around us. My paper will analyze the language features and the culture features of mobile phone short messages which are written in Chinese and English.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

Science.gov (United States)

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Clustering-based Feature Learning on Variable Stars

Science.gov (United States)

Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

2016-04-01

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

International Nuclear Information System (INIS)

Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

2016-01-01

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline

CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

Energy Technology Data Exchange (ETDEWEB)

Mackenzie, Cristóbal; Pichara, Karim [Computer Science Department, Pontificia Universidad Católica de Chile, Santiago (Chile); Protopapas, Pavlos [Institute for Applied Computational Science, Harvard University, Cambridge, MA (United States)

2016-04-01

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

Effective automated feature construction and selection for classification of biological sequences.

Directory of Open Access Journals (Sweden)

Uday Kamath

Full Text Available Many open problems in bioinformatics involve elucidating underlying functional signals in biological sequences. DNA sequences, in particular, are characterized by rich architectures in which functional signals are increasingly found to combine local and distal interactions at the nucleotide level. Problems of interest include detection of regulatory regions, splice sites, exons, hypersensitive sites, and more. These problems naturally lend themselves to formulation as classification problems in machine learning. When classification is based on features extracted from the sequences under investigation, success is critically dependent on the chosen set of features.We present an algorithmic framework (EFFECT for automated detection of functional signals in biological sequences. We focus here on classification problems involving DNA sequences which state-of-the-art work in machine learning shows to be challenging and involve complex combinations of local and distal features. EFFECT uses a two-stage process to first construct a set of candidate sequence-based features and then select a most effective subset for the classification task at hand. Both stages make heavy use of evolutionary algorithms to efficiently guide the search towards informative features capable of discriminating between sequences that contain a particular functional signal and those that do not.To demonstrate its generality, EFFECT is applied to three separate problems of importance in DNA research: the recognition of hypersensitive sites, splice sites, and ALU sites. Comparisons with state-of-the-art algorithms show that the framework is both general and powerful. In addition, a detailed analysis of the constructed features shows that they contain valuable biological information about DNA architecture, allowing biologists and other researchers to directly inspect the features and potentially use the insights obtained to assist wet-laboratory studies on retainment or modification

Enhanced HMAX model with feedforward feature learning for multiclass categorization

Directory of Open Access Journals (Sweden)

Yinlin eLi

2015-10-01

Full Text Available In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 milliseconds of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: 1 To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; 2 To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; 3 Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

Enhanced HMAX model with feedforward feature learning for multiclass categorization.

Science.gov (United States)

Li, Yinlin; Wu, Wei; Zhang, Bo; Li, Fengfu

2015-01-01

In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

Cost-Sensitive Feature Selection of Numeric Data with Measurement Errors

Directory of Open Access Journals (Sweden)

Hong Zhao

2013-01-01

Full Text Available Feature selection is an essential process in data mining applications since it reduces a model’s complexity. However, feature selection with various types of costs is still a new research topic. In this paper, we study the cost-sensitive feature selection problem of numeric data with measurement errors. The major contributions of this paper are fourfold. First, a new data model is built to address test costs and misclassification costs as well as error boundaries. It is distinguished from the existing models mainly on the error boundaries. Second, a covering-based rough set model with normal distribution measurement errors is constructed. With this model, coverings are constructed from data rather than assigned by users. Third, a new cost-sensitive feature selection problem is defined on this model. It is more realistic than the existing feature selection problems. Fourth, both backtracking and heuristic algorithms are proposed to deal with the new problem. Experimental results show the efficiency of the pruning techniques for the backtracking algorithm and the effectiveness of the heuristic algorithm. This study is a step toward realistic applications of the cost-sensitive learning.

Fast Branch & Bound algorithms for optimal feature selection

Czech Academy of Sciences Publication Activity Database

Somol, Petr; Pudil, Pavel; Kittler, J.

2004-01-01

Roč. 26, č. 7 (2004), s. 900-912 ISSN 0162-8828 R&D Projects: GA ČR GA402/02/1271; GA ČR GA402/03/1310; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : subset search * feature selection * search tree Subject RIV: BD - Theory of Information Impact factor: 4.352, year: 2004

RETHINKING RESEARCH ETHICS FOR MEDIATED SETTINGS

NARCIS (Netherlands)

Beaulieu, Anne; Estalella, Adolfo

2012-01-01

An important feature of e-research is the increased mediation of research practices, which changes not only the objects and tools of research, but also the relation between researcher and object, between researchers, and between researchers and their constituencies and stakeholders. This article

Robust Features Of Surface Electromyography Signal

Science.gov (United States)

Sabri, M. I.; Miskon, M. F.; Yaacob, M. R.

2013-12-01

Nowadays, application of robotics in human life has been explored widely. Robotics exoskeleton system are one of drastically areas in recent robotic research that shows mimic impact in human life. These system have been developed significantly to be used for human power augmentation, robotics rehabilitation, human power assist, and haptic interaction in virtual reality. This paper focus on solving challenges in problem using neural signals and extracting human intent. Commonly, surface electromyography signal (sEMG) are used in order to control human intent for application exoskeleton robot. But the problem lies on difficulty of pattern recognition of the sEMG features due to high noises which are electrode and cable motion artifact, electrode noise, dermic noise, alternating current power line interface, and other noise came from electronic instrument. The main objective in this paper is to study the best features of electromyography in term of time domain (statistical analysis) and frequency domain (Fast Fourier Transform).The secondary objectives is to map the relationship between torque and best features of muscle unit activation potential (MaxPS and RMS) of biceps brachii. This project scope use primary data of 2 male sample subject which using same dominant hand (right handed), age between 20-27 years old, muscle diameter 32cm to 35cm and using single channel muscle (biceps brachii muscle). The experiment conduct 2 times repeated task of contraction and relaxation of biceps brachii when lifting different load from no load to 3kg with ascending 1kg The result shows that Fast Fourier Transform maximum power spectrum (MaxPS) has less error than mean value of reading compare to root mean square (RMS) value. Thus, Fast Fourier Transform maximum power spectrum (MaxPS) show the linear relationship against torque experience by elbow joint to lift different load. As the conclusion, the best features is MaxPS because it has the lowest error than other features and show

Robust Features Of Surface Electromyography Signal

International Nuclear Information System (INIS)

Sabri, M I; Miskon, M F; Yaacob, M R

2013-01-01

Nowadays, application of robotics in human life has been explored widely. Robotics exoskeleton system are one of drastically areas in recent robotic research that shows mimic impact in human life. These system have been developed significantly to be used for human power augmentation, robotics rehabilitation, human power assist, and haptic interaction in virtual reality. This paper focus on solving challenges in problem using neural signals and extracting human intent. Commonly, surface electromyography signal (sEMG) are used in order to control human intent for application exoskeleton robot. But the problem lies on difficulty of pattern recognition of the sEMG features due to high noises which are electrode and cable motion artifact, electrode noise, dermic noise, alternating current power line interface, and other noise came from electronic instrument. The main objective in this paper is to study the best features of electromyography in term of time domain (statistical analysis) and frequency domain (Fast Fourier Transform).The secondary objectives is to map the relationship between torque and best features of muscle unit activation potential (MaxPS and RMS) of biceps brachii. This project scope use primary data of 2 male sample subject which using same dominant hand (right handed), age between 20–27 years old, muscle diameter 32cm to 35cm and using single channel muscle (biceps brachii muscle). The experiment conduct 2 times repeated task of contraction and relaxation of biceps brachii when lifting different load from no load to 3kg with ascending 1kg The result shows that Fast Fourier Transform maximum power spectrum (MaxPS) has less error than mean value of reading compare to root mean square (RMS) value. Thus, Fast Fourier Transform maximum power spectrum (MaxPS) show the linear relationship against torque experience by elbow joint to lift different load. As the conclusion, the best features is MaxPS because it has the lowest error than other features and

Specific features of occupational medicine in nuclear research and industry

International Nuclear Information System (INIS)

Giraud, J.M.; Quesne, B.

2003-01-01

Measures to prevent the exposure of personnel to ionising radiation were taken as soon as the first nuclear laboratories were set up. This branch of occupational preventive medicine has since kept pace with advances in research and in the industrial applications of nuclear energy. (authors)

Reliability in content analysis: The case of semantic feature norms classification.

Science.gov (United States)

Bolognesi, Marianna; Pilgram, Roosmaryn; van den Heerik, Romy

2017-12-01

Semantic feature norms (e.g., STIMULUS: car → RESPONSE: ) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses-that is, by using taxonomies of feature types and having independent coders perform the annotation task. However, the ways in which such content analyses are typically performed and reported are not consistent across the literature. This constitutes a serious methodological problem that might undermine the theoretical claims based on such annotations. In this study, we first offer a review of some of the released datasets of annotated semantic feature norms and the related taxonomies used for content analysis. We then provide theoretical and methodological insights in relation to the content analysis methodology. Finally, we apply content analysis to a new dataset of semantic features and show how the method should be applied in order to deliver reliable annotations and replicable coding schemes. We tackle the following issues: (1) taxonomy structure, (2) the description of categories, (3) coder training, and (4) sustainability of the coding scheme-that is, comparison of the annotations provided by trained versus novice coders. The outcomes of the project are threefold: We provide methodological guidelines for semantic feature classification; we provide a revised and adapted taxonomy that can (arguably) be applied to both concrete and abstract concepts; and we provide a dataset of annotated semantic feature norms.

Towards automated statewide land cover mapping in Wisconsin using satellite remote sensing and GIS techniques

International Nuclear Information System (INIS)

Cosentino, B.L.; Lillesand, T.M.

1991-01-01

Attention is given to an initial research project being performed by the University of Wisconsin-Madison, Environmental Remote Sensing Center in conjunction with seven local, state, and federal agencies to implement automated statewide land cover mapping using satellite remote sensing and geographical information system (GIS) techniques. The basis, progress, and future research needs for this mapping program are presented. The research efforts are directed toward strategies that integrate satellite remote sensing and GIS techniques in the generation of land cover data for multiple users of land cover information. The project objectives are to investigate methodologies that integrate satellite data with other imagery and spatial data resident in emerging GISs in the state for particular program needs, and to develop techniques that can improve automated land cover mapping efficiency and accuracy. 10 refs

Product information representation for feature conversion and implementation of group technology automated coding

Science.gov (United States)

Medland, A. J.; Zhu, Guowang; Gao, Jian; Sun, Jian

1996-03-01

Feature conversion, also called feature transformation and feature mapping, is defined as the process of converting features from one view of an object to another view of the object. In a relatively simple implementation, for each application the design features are automatically converted into features specific for that application. All modifications have to be made via the design features. This is the approach that has attracted most attention until now. In the ideal situation, however, conversions directly from application views to the design view, and to other applications views, are also possible. In this paper, some difficulties faced in feature conversion are discussed. A new representation scheme of feature-based parts models has been proposed for the purpose of one-way feature conversion. The parts models consist of five different levels of abstraction, extending from an assembly level and its attributes, single parts and their attributes, single features and their attributes, one containing the geometric reference element and finally one for detailed geometry. One implementation of feature conversion for rotational components within GT (Group Technology) has already been undertaken using an automated coding procedure operating on a design-feature database. This database has been generated by a feature-based design system, and the GT coding scheme used in this paper is a specific scheme created for a textile machine manufacturing plant. Such feature conversion techniques presented here are only in their early stages of development and further research is underway.

Feature level fusion of hand and face biometrics

Science.gov (United States)

Ross, Arun A.; Govindarajan, Rohin

2005-03-01

Multibiometric systems utilize the evidence presented by multiple biometric sources (e.g., face and fingerprint, multiple fingers of a user, multiple matchers, etc.) in order to determine or verify the identity of an individual. Information from multiple sources can be consolidated in several distinct levels, including the feature extraction level, match score level and decision level. While fusion at the match score and decision levels have been extensively studied in the literature, fusion at the feature level is a relatively understudied problem. In this paper we discuss fusion at the feature level in 3 different scenarios: (i) fusion of PCA and LDA coefficients of face; (ii) fusion of LDA coefficients corresponding to the R,G,B channels of a face image; (iii) fusion of face and hand modalities. Preliminary results are encouraging and help in highlighting the pros and cons of performing fusion at this level. The primary motivation of this work is to demonstrate the viability of such a fusion and to underscore the importance of pursuing further research in this direction.

STUDY ON SHADOW EFFECTS OF VARIOUS FEATURES ON CLOSE RANGE THERMAL IMAGES

Directory of Open Access Journals (Sweden)

C. L. Liao

2012-07-01

Full Text Available Thermal infrared data become more popular in remote sensing investigation, for it could be acquired both in day and night. The change of temperature has special characteristic in natural environment, so the thermal infrared images could be used in monitoring volcanic landform, the urban development, and disaster prevention. Heat shadow is formed by reflecting radiating capacity which followed the objects. Because of poor spatial resolution of thermal infrared images in satellite sensor, shadow effects were usually ignored. This research focus on discussing the shadow effects of various features, which include metals and nonmetallic materials. An area-based thermal sensor, FLIR-T360 was selected to acquire thermal images. Various features with different emissivity were chosen as reflective surface to obtain thermal shadow in normal atmospheric temperature. Experiments found that the shadow effects depend on the distance between sensors and features, depression angle, object temperature and emissivity of reflective surface. The causes of shadow effects have been altered in the experiment for analyzing the variance in thermal infrared images. The result shows that there were quite different impacts by shadow effects between metals and nonmetallic materials. The further research would be produced a math model to describe the shadow effects of different features in the future work.

Extracting Information from Conventional AE Features for Fatigue Onset Damage Detection in Carbon Fiber Composites

DEFF Research Database (Denmark)

Unnthorsson, Runar; Pontoppidan, Niels Henrik Bohl; Jonsson, Magnus Thor

2005-01-01

We have analyzed simple data fusion and preprocessing methods on Acoustic Emission measurements of prosthetic feet made of carbon fiber reinforced composites. This paper presents the initial research steps; aiming at reducing the time spent on the fatigue test. With a simple single feature...... approaches can readily be investigated using the improved features, possibly improving the performance using multiple feature classifiers, e.g., Voting systems; Support Vector Machines and Gaussian Mixtures....

LINGUISTIC FEATURES ANALYSIS OF THE ENGLISH ELECTRONIC COMMERCE WEBSITES

Directory of Open Access Journals (Sweden)

Siti Nurani

2014-06-01

Full Text Available This research aims at identifying linguistic features used in the English electronic commerce websites used in correlation with the field, tenor and mode of discourse as parts of Systemic Functional Linguistics (SFL approach. Findings have shown that in the field of discourse, the linguistic features are largely appeared in the experiential domain analysis which shows that all terms of registers function as technical terms, of which the two major forms of nouns and verbs were the most frequent categories among other kinds of technical terms. The goal orientation is considered to be as a long term and the social activity is exchange. In the tenor of discourse, the linguistic features are highly appeared in the social distance analysis which shows that the social distance between participants is considered minimal. The agentive role is said to be equal and the social role is considered as non-hierarchic. In the mode of discourse, the linguistic features are excessively occurred in the language role analysis which exists equally of both constitutive and ancillary. The channel is in graphic mode. The medium is in written with a visual contact as its device.

Feature selection for splice site prediction: A new method using EDA-based feature ranking

Directory of Open Access Journals (Sweden)

Rouzé Pierre

2004-05-01

Full Text Available Abstract Background The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. Results In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing. Conclusion We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.

Coding of visual object features and feature conjunctions in the human brain.

Science.gov (United States)

Martinovic, Jasna; Gruber, Thomas; Müller, Matthias M

2008-01-01

Object recognition is achieved through neural mechanisms reliant on the activity of distributed coordinated neural assemblies. In the initial steps of this process, an object's features are thought to be coded very rapidly in distinct neural assemblies. These features play different functional roles in the recognition process--while colour facilitates recognition, additional contours and edges delay it. Here, we selectively varied the amount and role of object features in an entry-level categorization paradigm and related them to the electrical activity of the human brain. We found that early synchronizations (approx. 100 ms) increased quantitatively when more image features had to be coded, without reflecting their qualitative contribution to the recognition process. Later activity (approx. 200-400 ms) was modulated by the representational role of object features. These findings demonstrate that although early synchronizations may be sufficient for relatively crude discrimination of objects in visual scenes, they cannot support entry-level categorization. This was subserved by later processes of object model selection, which utilized the representational value of object features such as colour or edges to select the appropriate model and achieve identification.

Focus Group Interview in Family Practice Research: Implementing a qualitative research method

OpenAIRE

Wood, Marjorie L.

1992-01-01

Focus group interviews, described as a qualitative research method with good potential in family medicine, are traced from their origins in market research to their growing role in sociology and medicine. Features of this method are described, including design, conduct, and analysis. Both proven and potential areas for primary care research using focus groups are outlined.

Data Visualization and Feature Selection Methods in Gel-based Proteomics

DEFF Research Database (Denmark)

Silva, Tomé Santos; Richard, Nadege; Dias, Jorge P.

2014-01-01

-based proteomics, summarizing the current state of research within this field. Particular focus is given on discussing the usefulness of available multivariate analysis tools both for data visualization and feature selection purposes. Visual examples are given using a real gel-based proteomic dataset as basis....

Process of technical performance: essential features and their economic view

OpenAIRE

Zhylinska, O.

2010-01-01

Essential features of technical performance have been defined in the context of similarity of its components such as research and development, training of technical/engineering personnel and providing technical services. Also peculiarities of economic view have been examined in terms of information model of managing.

Research on Nonlinear Feature of Electrical Resistance of Acupuncture Points

Directory of Open Access Journals (Sweden)

Jianzi Wei

2012-01-01

Full Text Available A highly sensitive volt-ampere characteristics detecting system was applied to measure the volt-ampere curves of nine acupuncture points, LU9, HT7, LI4, PC6, ST36, SP6, KI3, LR3, and SP3, and corresponding nonacupuncture points bilaterally from 42 healthy volunteers. Electric currents intensity was increased from 0 μA to 20 μA and then returned to 0 μA again. The results showed that the volt-ampere curves of acupuncture points had nonlinear property and magnetic hysteresis-like feature. On all acupuncture point spots, the volt-ampere areas of the increasing phase were significantly larger than that of the decreasing phase (P<0.01. The volt-ampere areas of ten acupuncture point spots were significantly smaller than those of the corresponding nonacupuncture point spots when intensity was increase (P<0.05~P<0.001. And when intensity was decrease, eleven acupuncture point spots showed the same property as above (P<0.05~P<0.001, while two acupuncture point spots showed opposite phenomenon in which the areas of two acupuncture point spots were larger than those of the corresponding nonacupuncture point spots (P<0.05~P<0.01. These results show that the phenomenon of low skin resistance does not exist to all acupuncture points.

Desired features of smartphone applications promoting physical activity.

Science.gov (United States)

Rabin, Carolyn; Bock, Beth

2011-12-01

Approximately one-third of adults in the United States are physically inactive. This is a significant public health concern as physical activity (PA) can influence the risk of cardiovascular disease, diabetes, and certain forms of cancer. To minimize these health risks, effective PA interventions must be developed and disseminated to the vast number of individuals who remain sedentary. Smartphone technology presents an exciting opportunity for delivering PA interventions remotely. Although a number of PA applications are currently available for smartphones, these "apps" are not based on established theories of health behavior change and most do not include evidence-based features (e.g., reinforcement and goal setting). Our aim was to collect formative data to develop a smartphone PA app that is empirically and theoretically-based and incorporates user preferences. We recruited 15 sedentary adults to test three currently available PA smartphone apps and provide qualitative and quantitative feedback. Findings indicate that users have a number of specific preferences with regard to PA app features, including that apps provide automatic tracking of PA (e.g., steps taken and calories burned), track progress toward PA goals, and integrate a music feature. Participants also preferred that PA apps be flexible enough to be used with several types of PA, and have well-documented features and user-friendly interfaces (e.g., a one-click main page). When queried by the researcher, most participants endorsed including goal-setting and problem-solving features. These findings provide a blue print for developing a smartphone PA app that incorporates evidence-based components and user preferences.

Viewshed and sense of place as conservation features: A case study and research agenda for South Africa's national parks

Directory of Open Access Journals (Sweden)

Jaco Barendse

2016-08-01

Full Text Available Sense of place (SoP refers to the meanings and values that people attach to places. The concept can be used to frame how people engage or form a connection with the natural environment. At a sensory level, SoP is influenced by people’s visual experiences, which in turn can be linked to the concept of viewsheds. Viewsheds can be transformed, either abruptly (e.g. by infrastructure development such as wind turbines or more gradually (e.g. by non-native trees invading a landscape. In this study, we focus on the Garden Route National Park to explore the potential importance of viewsheds as a conservation feature, specifically in the context of non-native (especially invasive tree species. Using mixed information sources, we explore the potential role of invasive trees on experiences of visitors to this protected area and speculate on how viewsheds may shape SoP associations and how such associations may inform protected area management. Our investigation shows that people’s experiences regarding natural and modified viewsheds are varied and intricate. Both SoP and viewsheds have the potential to inform conservation action, and these concepts should form an integral part of objective hierarchies and management plans for national parks. However, while legislation and park management plans make provision for the use of these concepts, associated research in South Africa is virtually non-existent. We conclude by proposing a conceptual model and research agenda to promote the use of viewsheds and SoP in the management of national parks in South Africa. Conservation implications: Viewshed and sense of place can be used as boundary concepts to (1 facilitate interdisciplinary research between social and natural scientists, (2 help understand the connectedness and feedbacks between people and nature and (3 promote communication between science, management and stakeholders regarding desired conditions of landscapes in and around parks.

Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

Directory of Open Access Journals (Sweden)

Yi An

2013-01-01

Full Text Available Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from [0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

An object-oriented feature-based design system face-based detection of feature interactions

International Nuclear Information System (INIS)

Ariffin Abdul Razak

1999-01-01

This paper presents an object-oriented, feature-based design system which supports the integration of design and manufacture by ensuring that part descriptions fully account for any feature interactions. Manufacturing information is extracted from the feature descriptions in the form of volumes and Tool Access Directions, TADs. When features interact, both volumes and TADs are updated. This methodology has been demonstrated by developing a prototype system in which ACIS attributes are used to record feature information within the data structure of the solid model. The system implemented in the C++ programming language and embedded in a menu-driven X-windows user interface to the ACIS 3D Toolkit. (author)

3D face analysis by using Mesh-LBP feature

Science.gov (United States)

Wang, Haoyu; Yang, Fumeng; Zhang, Yuming; Wu, Congzhong

2017-11-01

Objective: Face Recognition is one of the widely application of image processing. Corresponding two-dimensional limitations, such as the pose and illumination changes, to a certain extent restricted its accurate rate and further development. How to overcome the pose and illumination changes and the effects of self-occlusion is the research hotspot and difficulty, also attracting more and more domestic and foreign experts and scholars to study it. 3D face recognition fusing shape and texture descriptors has become a very promising research direction. Method: Our paper presents a 3D point cloud based on mesh local binary pattern grid (Mesh-LBP), then feature extraction for 3D face recognition by fusing shape and texture descriptors. 3D Mesh-LBP not only retains the integrity of the 3D geometry, is also reduces the need for recognition process of normalization steps, because the triangle Mesh-LBP descriptor is calculated on 3D grid. On the other hand, in view of multi-modal consistency in face recognition advantage, construction of LBP can fusing shape and texture information on Triangular Mesh. In this paper, some of the operators used to extract Mesh-LBP, Such as the normal vectors of the triangle each face and vertex, the gaussian curvature, the mean curvature, laplace operator and so on. Conclusion: First, Kinect devices obtain 3D point cloud face, after the pretreatment and normalization, then transform it into triangular grid, grid local binary pattern feature extraction from face key significant parts of face. For each local face, calculate its Mesh-LBP feature with Gaussian curvature, mean curvature laplace operator and so on. Experiments on the our research database, change the method is robust and high recognition accuracy.

Neural Tuning to Low-Level Features of Speech throughout the Perisylvian Cortex.

Science.gov (United States)

Berezutskaya, Julia; Freudenburg, Zachary V; Güçlü, Umut; van Gerven, Marcel A J; Ramsey, Nick F

2017-08-16

Despite a large body of research, we continue to lack a detailed account of how auditory processing of continuous speech unfolds in the human brain. Previous research showed the propagation of low-level acoustic features of speech from posterior superior temporal gyrus toward anterior superior temporal gyrus in the human brain (Hullett et al., 2016). In this study, we investigate what happens to these neural representations past the superior temporal gyrus and how they engage higher-level language processing areas such as inferior frontal gyrus. We used low-level sound features to model neural responses to speech outside of the primary auditory cortex. Two complementary imaging techniques were used with human participants (both males and females): electrocorticography (ECoG) and fMRI. Both imaging techniques showed tuning of the perisylvian cortex to low-level speech features. With ECoG, we found evidence of propagation of the temporal features of speech sounds along the ventral pathway of language processing in the brain toward inferior frontal gyrus. Increasingly coarse temporal features of speech spreading from posterior superior temporal cortex toward inferior frontal gyrus were associated with linguistic features such as voice onset time, duration of the formant transitions, and phoneme, syllable, and word boundaries. The present findings provide the groundwork for a comprehensive bottom-up account of speech comprehension in the human brain. SIGNIFICANCE STATEMENT We know that, during natural speech comprehension, a broad network of perisylvian cortical regions is involved in sound and language processing. Here, we investigated the tuning to low-level sound features within these regions using neural responses to a short feature film. We also looked at whether the tuning organization along these brain regions showed any parallel to the hierarchy of language structures in continuous speech. Our results show that low-level speech features propagate throughout the

Comparisons of feature extraction algorithm based on unmanned aerial vehicle image

Directory of Open Access Journals (Sweden)

Xi Wenfei

2017-07-01

Full Text Available Feature point extraction technology has become a research hotspot in the photogrammetry and computer vision. The commonly used point feature extraction operators are SIFT operator, Forstner operator, Harris operator and Moravec operator, etc. With the high spatial resolution characteristics, UAV image is different from the traditional aviation image. Based on these characteristics of the unmanned aerial vehicle (UAV, this paper uses several operators referred above to extract feature points from the building images, grassland images, shrubbery images, and vegetable greenhouses images. Through the practical case analysis, the performance, advantages, disadvantages and adaptability of each algorithm are compared and analyzed by considering their speed and accuracy. Finally, the suggestions of how to adapt different algorithms in diverse environment are proposed.

Introduction to the Special Feature on rebuilding fisheries and threatened communities

Directory of Open Access Journals (Sweden)

Rosemary E. Ommer

2014-09-01

Full Text Available In this introductory essay to the Special Feature on rebuilding fisheries and threatened communities, we review the contributions of the researchers whose work is contained in this Special Feature. The essays are reviewed using the lens of the three questions that were posed by the Special Feature editors: Why is rebuilding so challenging? What is the relationship between fishery collapse/degradation and short- and long-term issues for food security, livelihoods, employment, and industrial and community resilience? How can we avoid situations in which the communities and people who may have contributed least to collapses/degradation end up paying the most for rebuilding and, indeed, may no longer be in a position where they can benefit from the results of their necessary sacrifices?

Overall Design Features and Key Technology Development for KJRR

Energy Technology Data Exchange (ETDEWEB)

Park, C.; Lee, B. C.; Ryu, J. S.; Kim, Y. K. [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2015-10-15

The KJRR (Ki-Jang Research Reactor) project was launched on Apr., 2012; 1) to make up the advanced technology related to RRs, 2) to provide the self-sufficiency in terms of medical and industrial radioisotope (RI) supply, and 3) to enlarge the NTD silicon doping services for growing the power device industry. The major facilities to be built through the KJRR project are, • 15 MW Research Reactor and Reactor building • Radioisotopes Production Facility (RIPF) and related R and D Facility • Fission Mo Production Facility (FMPF) with LEU Target • Radio-waste Treatment Facility (RTF) • Neutron Irradiation Facility such as PTS and HTS. This paper describes the overall design features of the KJRR and the key technology development for RRs during the project. The overall design features of the KJRR and RR technology under development have been overviewd. The design of the KJRR will comply with the Korean Nuclear Law, regulatory requirements and guidelines as well as international standards and guidelines. The KJRR is expected to be put into operation in the middle of 2019.

Fractured reservoir discrete feature network technologies. Final report, March 7, 1996 to September 30, 1998

Energy Technology Data Exchange (ETDEWEB)

Dershowitz, William S.; Einstein, Herbert H.; LaPoint, Paul R.; Eiben, Thorsten; Wadleigh, Eugene; Ivanova, Violeta

1998-12-01

This report summarizes research conducted for the Fractured Reservoir Discrete Feature Network Technologies Project. The five areas studied are development of hierarchical fracture models; fractured reservoir compartmentalization, block size, and tributary volume analysis; development and demonstration of fractured reservoir discrete feature data analysis tools; development of tools for data integration and reservoir simulation through application of discrete feature network technologies for tertiary oil production; quantitative evaluation of the economic value of this analysis approach.

Organization of co-occurring Axis II features in borderline personality disorder.

Science.gov (United States)

Critchfield, Kenneth L; Clarkin, John F; Levy, Kenneth N; Kernberg, Otto F

2008-06-01

Considerable heterogeneity exists in the comorbid Axis II features that frequently accompany borderline personality disorder (BPD). These features have potential to be meaningfully organized, relate to specific BPD presentation, and have implications for treatment process and outcome. The present study explored patterns of Axis II comorbidity in order to identify subtypes of BPD. A well-defined sample of 90 patients diagnosed with BPD was recruited as part of an RCT study. Participants were administered the International Personality Disorder Examination (Loranger, 1999) to diagnose BPD and assess comorbid Axis II features. Other measures were also administered to assess aspects of current work and relationship functioning, symptomatology, and self-concept. Q-factoring was used to develop subtypes based on commonly occurring Axis II profiles, identifying three: Cluster A (elevated paranoid and schizotypal features), Cluster B (elevated narcissistic and histrionic features), and Cluster C (elevated avoidant and obsessive-compulsive features). An additional factor analysis revealed two dimensions underlying the comorbid features identifiable as: extraversion versus introversion and antagonism versus constraint. Validity of these two maps of comorbidity was explored in terms of the BPD criteria themselves, as well as on work and relationship functioning, identity diffusion, views of self and others, positive and negative affect, behavioural dyscontrol, and symptomatic distress. Clinically meaningful subtypes can be identified for BPD based on co-occurring Axis II features. Further research is needed to replicate and further establish base-rates of these subtypes as well as their differential implications for treatment.

Epithermal neutron beam for BNCT research at the Washington State University TRIGA research reactor

International Nuclear Information System (INIS)

Nigg, D.W.; Venhuizen, J.R.; Wheeler, F.J.; Wemple, C.A.; Tripard, G.E.; Gavin, P.R.

2000-01-01

A new epithermal-neutron beam facility for BNCT (Boron Neutron Capture Therapy) research and boronated agent screening in animal models is in the final stages of construction at Washington State University (WSU). A key distinguishing feature of the design is the incorporation of a new, high-efficiency, neutron moderating and filtering material, Fluental, developed by the Technical Research Centre of Finland. An additional key feature is the provision for adjustable filter-moderator thickness to systematically explore the radiobiological consequences of increasing the fast-neutron contamination above the nominal value associated with the baseline system. (author)

FEATURES OF CRISIS MANAGEMENT IN ENTERPRISES

Directory of Open Access Journals (Sweden)

K. D. Busygin

2014-01-01

Full Text Available The article considers the value of preventive management in modern conditions. The global fi nancial and economic crisis of 2008-2010. sharpened interest in the problems of crisis management. This interest is manifested at the level of individual businesses, and at the level of the economy as a whole. At the same time revealed a signifi cant drawback: the development of crisis management theory lags behind practice. Non-compliance of the existing theory to modern requirements leads to the fact that the known approaches are not based on theoretical positions and empirical evidence and best practices, and, consequently, do not diff er systematically, because of this requires further research in this direction. The analysis shows that crisis management is a complex control system, which has its own specifi c features. Feature development solutions in crisis situations caused by the fact that they can only wear improving change with the obligatory account the limiting parameters of sustainable livelihoods enterprise (structure funds, personnel, activity profi le, the main products, and others.

The Features of the Architectonic of Financial System

Directory of Open Access Journals (Sweden)

Bondarenko Olena S.

2017-10-01

Full Text Available The article is aimed at substantiating the features of function of a contemporary financial system of the State together with the need to develop its architectonic, taking into consideration the functions and objectives of socio-economic development. The features of function of the current financial system of Ukraine have been disclosed. The main factors of influence have been defined and the need to develop new approaches to the management of the components of financial system has been proven. The essence and feasibility of building the architectonic of financial system have been substantiated, the main directions of practical implementation have been characterized. Prospects for further research are developing a mechanism for building the architectonic of financial system and creating an efficient management instrumentarium for managing its components.

Texture Feature Extraction and Classification for Iris Diagnosis

Science.gov (United States)

Ma, Lin; Li, Naimin

Appling computer aided techniques in iris image processing, and combining occidental iridology with the traditional Chinese medicine is a challenging research area in digital image processing and artificial intelligence. This paper proposes an iridology model that consists the iris image pre-processing, texture feature analysis and disease classification. To the pre-processing, a 2-step iris localization approach is proposed; a 2-D Gabor filter based texture analysis and a texture fractal dimension estimation method are proposed for pathological feature extraction; and at last support vector machines are constructed to recognize 2 typical diseases such as the alimentary canal disease and the nerve system disease. Experimental results show that the proposed iridology diagnosis model is quite effective and promising for medical diagnosis and health surveillance for both hospital and public use.

How Large-Scale Research Facilities Connect to Global Research

DEFF Research Database (Denmark)

Lauto, Giancarlo; Valentin, Finn

2013-01-01

Policies for large-scale research facilities (LSRFs) often highlight their spillovers to industrial innovation and their contribution to the external connectivity of the regional innovation system hosting them. Arguably, the particular institutional features of LSRFs are conducive for collaborative...... research. However, based on data on publications produced in 2006–2009 at the Neutron Science Directorate of Oak Ridge National Laboratory in Tennessee (United States), we find that internationalization of its collaborative research is restrained by coordination costs similar to those characterizing other...

Feature-Based Retinal Image Registration Using D-Saddle Feature

Directory of Open Access Journals (Sweden)

Roziana Ramli

2017-01-01

Full Text Available Retinal image registration is important to assist diagnosis and monitor retinal diseases, such as diabetic retinopathy and glaucoma. However, registering retinal images for various registration applications requires the detection and distribution of feature points on the low-quality region that consists of vessels of varying contrast and sizes. A recent feature detector known as Saddle detects feature points on vessels that are poorly distributed and densely positioned on strong contrast vessels. Therefore, we propose a multiresolution difference of Gaussian pyramid with Saddle detector (D-Saddle to detect feature points on the low-quality region that consists of vessels with varying contrast and sizes. D-Saddle is tested on Fundus Image Registration (FIRE Dataset that consists of 134 retinal image pairs. Experimental results show that D-Saddle successfully registered 43% of retinal image pairs with average registration accuracy of 2.329 pixels while a lower success rate is observed in other four state-of-the-art retinal image registration methods GDB-ICP (28%, Harris-PIIFD (4%, H-M (16%, and Saddle (16%. Furthermore, the registration accuracy of D-Saddle has the weakest correlation (Spearman with the intensity uniformity metric among all methods. Finally, the paired t-test shows that D-Saddle significantly improved the overall registration accuracy of the original Saddle.

Using probabilistic model as feature descriptor on a smartphone device for autonomous navigation of unmanned ground vehicles

Science.gov (United States)

Desai, Alok; Lee, Dah-Jye

2013-12-01

There has been significant research on the development of feature descriptors in the past few years. Most of them do not emphasize real-time applications. This paper presents the development of an affine invariant feature descriptor for low resource applications such as UAV and UGV that are equipped with an embedded system with a small microprocessor, a field programmable gate array (FPGA), or a smart phone device. UAV and UGV have proven suitable for many promising applications such as unknown environment exploration, search and rescue operations. These applications required on board image processing for obstacle detection, avoidance and navigation. All these real-time vision applications require a camera to grab images and match features using a feature descriptor. A good feature descriptor will uniquely describe a feature point thus allowing it to be correctly identified and matched with its corresponding feature point in another image. A few feature description algorithms are available for a resource limited system. They either require too much of the device's resource or too much simplification on the algorithm, which results in reduction in performance. This research is aimed at meeting the needs of these systems without sacrificing accuracy. This paper introduces a new feature descriptor called PRObabilistic model (PRO) for UGV navigation applications. It is a compact and efficient binary descriptor that is hardware-friendly and easy for implementation.

Screening for Plant Features

NARCIS (Netherlands)

Heijden, van der G.W.A.M.; Polder, G.

2015-01-01

In this chapter, an overview of different plant features is given, from (sub)cellular to canopy level. A myriad of methods is available to measure these features using image analysis, and often, multiple methods can be used to measure the same feature. Several criteria are listed for choosing a

Teaching English Pronunciation of Suprasegmental Features on Students of English Education

Directory of Open Access Journals (Sweden)

Ahmad Yousef Bani

2018-01-01

Full Text Available The objective of this research is to know the technique and activity in teaching English pronunciation on suprasegmental features (intonation and stress. This research uses qualitative approach with descriptive method. The subject of this research is 6 students from English education department. Technique of collecting data by doing observation, interview and documentation. The results showed In teaching English pronunciation for suprasegmental features is very concerned about the how to teach students, give materials and do exercises. There are some materials that are taught to improve students' ability in stress words of English sentences. Students learnt combination of words adjectives and nouns are generally stressed is in the first, students are taught about the prefix, learnt about words with suffixes and students were also given exercise with compound words. Furthermore, in teaching intonation, students are also given understanding and practicing the reading text, analyzing and pronouncing the English word in accordance with the correct intonation. The impact, students understand how to use rising and falling intonation.

On feature augmentation for semantic argument classification of the Quran English translation using support vector machine

Science.gov (United States)

Khaira Batubara, Dina; Arif Bijaksana, Moch; Adiwijaya

2018-03-01

Research on the semantic argument classification requires semantically labeled data in large numbers, called corpus. Because building a corpus is costly and time-consuming, recently many studies have used existing corpus as the training data to conduct semantic argument classification research on new domain. But previous studies have proven that there is a significant decrease in performance when classifying semantic arguments on different domain between the training and the testing data. The main problem is when there is a new argument that found in the testing data but it is not found in the training data. This research carries on semantic argument classification on a new domain that is Quran English Translation by utilizing Propbank corpus as the training data. To recognize the new argument in the training data, this research proposes four new features for extending the argument features in the training data. By using SVM Linear, the experiment has proven that augmenting the proposed features to the baseline system with some combinations option improve the performance of semantic argument classification on Quran data using Propbank Corpus as training data.

A General Purpose Feature Extractor for Light Detection and Ranging Data

Directory of Open Access Journals (Sweden)

Edwin B. Olson

2010-11-01

Full Text Available Feature extraction is a central step of processing Light Detection and Ranging (LIDAR data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.

A general purpose feature extractor for light detection and ranging data.

Science.gov (United States)

Li, Yangming; Olson, Edwin B

2010-01-01

Feature extraction is a central step of processing Light Detection and Ranging (LIDAR) data. Existing detectors tend to exploit characteristics of specific environments: corners and lines from indoor (rectilinear) environments, and trees from outdoor environments. While these detectors work well in their intended environments, their performance in different environments can be poor. We describe a general purpose feature detector for both 2D and 3D LIDAR data that is applicable to virtually any environment. Our method adapts classic feature detection methods from the image processing literature, specifically the multi-scale Kanade-Tomasi corner detector. The resulting method is capable of identifying highly stable and repeatable features at a variety of spatial scales without knowledge of environment, and produces principled uncertainty estimates and corner descriptors at same time. We present results on both software simulation and standard datasets, including the 2D Victoria Park and Intel Research Center datasets, and the 3D MIT DARPA Urban Challenge dataset.

Features of successful bids for funding of applied health research: a cohort study.

Science.gov (United States)

Turner, Sheila; Davidson, Peter; Stanton, Louise; Cawdeary, Victoria

2014-09-22

The literature suggests that research funding decisions may be influenced by criteria such as gender or institution of the principal investigator (PI). The aim of this study was to investigate the association between characteristics of funding applications and success when considered by a research funding board. We selected a retrospective cohort of 296 outline applications for primary research (mainly pragmatic clinical trials) submitted to the commissioning board of the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme between January 1st 2006 and December 31st 2009. We selected proposals submitted to the commissioned NIHR HTA work stream as they addressed issues which the programme already deemed to be important, hence the priority of the research question was not considered as one of the selection criteria for success or failure. Main outcome measures were success or failure at short-listing and in obtaining research funding. The characteristics of applications associated with success at shortlisting and funding were multi-disciplinarity of the team (OR 19.94 [5.13, 77.50], P research applications most strongly associated with success were related to the range of expertise in the team and the completion of a pilot or feasibility study.

Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset

Science.gov (United States)

Liu, Qiaoyuan; Wang, Yuru; Yin, Minghao; Ren, Jinchang; Li, Ruizhi

2017-11-01

Although various visual tracking algorithms have been proposed in the last 2-3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion, etc. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy causing low efficiency and ambiguity causing poor performance. An effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, the "curse of dimensionality" has been avoided while the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.

Feature diagnosticity and task context shape activity in human scene-selective cortex.

Science.gov (United States)

Lowe, Matthew X; Gallivan, Jason P; Ferber, Susanne; Cant, Jonathan S

2016-01-15

Scenes are constructed from multiple visual features, yet previous research investigating scene processing has often focused on the contributions of single features in isolation. In the real world, features rarely exist independently of one another and likely converge to inform scene identity in unique ways. Here, we utilize fMRI and pattern classification techniques to examine the interactions between task context (i.e., attend to diagnostic global scene features; texture or layout) and high-level scene attributes (content and spatial boundary) to test the novel hypothesis that scene-selective cortex represents multiple visual features, the importance of which varies according to their diagnostic relevance across scene categories and task demands. Our results show for the first time that scene representations are driven by interactions between multiple visual features and high-level scene attributes. Specifically, univariate analysis of scene-selective cortex revealed that task context and feature diagnosticity shape activity differentially across scene categories. Examination using multivariate decoding methods revealed results consistent with univariate findings, but also evidence for an interaction between high-level scene attributes and diagnostic visual features within scene categories. Critically, these findings suggest visual feature representations are not distributed uniformly across scene categories but are shaped by task context and feature diagnosticity. Thus, we propose that scene-selective cortex constructs a flexible representation of the environment by integrating multiple diagnostically relevant visual features, the nature of which varies according to the particular scene being perceived and the goals of the observer. Copyright © 2015 Elsevier Inc. All rights reserved.

Recognition during recall failure: Semantic feature matching as a mechanism for recognition of semantic cues when recall fails.

Science.gov (United States)

Cleary, Anne M; Ryals, Anthony J; Wagner, Samantha R

2016-01-01

Research suggests that a feature-matching process underlies cue familiarity-detection when cued recall with graphemic cues fails. When a test cue (e.g., potchbork) overlaps in graphemic features with multiple unrecalled studied items (e.g., patchwork, pitchfork, pocketbook, pullcork), higher cue familiarity ratings are given during recall failure of all of the targets than when the cue overlaps in graphemic features with only one studied target and that target fails to be recalled (e.g., patchwork). The present study used semantic feature production norms (McRae et al., Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005) to examine whether the same holds true when the cues are semantic in nature (e.g., jaguar is used to cue cheetah). Indeed, test cues (e.g., cedar) that overlapped in semantic features (e.g., a_tree, has_bark, etc.) with four unretrieved studied items (e.g., birch, oak, pine, willow) received higher cue familiarity ratings during recall failure than test cues that overlapped in semantic features with only two (also unretrieved) studied items (e.g., birch, oak), which in turn received higher familiarity ratings during recall failure than cues that did not overlap in semantic features with any studied items. These findings suggest that the feature-matching theory of recognition during recall failure can accommodate recognition of semantic cues during recall failure, providing a potential mechanism for conceptually-based forms of cue recognition during target retrieval failure. They also provide converging evidence for the existence of the semantic features envisaged in feature-based models of semantic knowledge representation and for those more concretely specified by the production norms of McRae et al. (Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005).

Essential Features for a Scholarly Journal Content Management and Peer Review Software

OpenAIRE

Fatima Sheikh Shoaie; Mehdi Husseini

2010-01-01

  The present study investigates the software used in scientific journals for content management and peer review, in order to identify the essential features. These softwares are analyzed and presented in tabular format. A questionnaire was prepared and submitted to a panel composed of 15 referees, editor in chief, software designers and researchers. The essential features for a software managing the review process were divided into three groups with populations of 10-15, 5-10 and 0-5 respect...

A database of semantic features for chosen concepts (Attested in 8- to 10-year-old Czech pupils

Directory of Open Access Journals (Sweden)

Konečná Kristýna

2017-06-01

Full Text Available In this paper, a database of semantic features is presented. 104 nominal concepts from 13 semantic categories were described by young Czech school children. They were asked to respond to the question “what is it, what does it mean?” by listing different kinds of properties for concepts in writing. Their responses were broken down into semantic features and the database was prepared using a set of pre-established rules. The method of database design, with an emphasis on the way features were recorded, is described in detail within this article. The data were statistically analysed and interpreted and the results along with database usage methodologies are discussed. The goal of this research is to produce a complex database to be used in future research relating to semantic features and therefore it has been published online for use by the wider academic community. At present, databases have been published based on data gathered from adult English and Czech speakers; however participation in this study was limited specifically to young Czech-speaking children. Thus, this database is characteristically unique as it provides important insight into this specific age and language group’s conceptual knowledge. The research is inspired primarily by research papers concerning semantic feature production obtained from adult English speakers (McRae, de Sa, and Seidenberg, 1997; McRae, Cree, Seidenberg, and McNorgan, 2005; Vinson and Vigliocco, 2008.

Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Directory of Open Access Journals (Sweden)

Liogienė Tatjana

2016-07-01

Full Text Available The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS and Sequential Floating Forward Selection (SFFS techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

Speeded induction under uncertainty: the influence of multiple categories and feature conjunctions.

Science.gov (United States)

Newell, Ben R; Paton, Helen; Hayes, Brett K; Griffiths, Oren

2010-12-01

When people are uncertain about the category membership of an item (e.g., Is it a dog or a dingo?), research shows that they tend to rely only on the dominant or most likely category when making inductions (e.g., How likely is it to befriend me?). An exception has been reported using speeded induction judgments where participants appeared to use information from multiple categories to make inductions (Verde, Murphy, & Ross, 2005). In two speeded induction studies, we found that participants tended to rely on the frequency with which features co-occurred when making feature predictions, independently of category membership. This pattern held whether categories were considered implicitly (Experiment 1) or explicitly (Experiment 2) prior to feature induction. The results converge with other recent work suggesting that people often rely on feature conjunction information, rather than category boundaries, when making inductions under uncertainty.

Music preferences based on audio features, and its relation to personality

OpenAIRE

Dunn, Greg

2009-01-01

Recent studies have summarized reported music preferences by genre into four broadly defined categories, which relate to various personality characteristics. Other research has indicated that genre classification is ambiguous and inconsistent. This ambiguity suggests that research relating personality to music preferences based on genre could benefit from a more objective definition of music. This problem is addressed by investigating how music preferences linked to objective audio features r...

Polish Qualitative Sociology: The General Features and Development

OpenAIRE

Konecki, Krzysztof Tomasz; Kacperczyk, Anna; Marciniak, Łukasz

2005-01-01

Forum Qualitative Sozialforschung / Forum: Qualitative Social Research,2005, 6(3) The article explores the development of Polish qualitative sociology in Poland by presenting its main intellectual routes and some of the general features of Polish sociology. Romanticism and inductionmethod are crucial elements for the development of this discipline in Poland and contribute to its. unigueness. The role of Florian Znaniecki in creating the Polish qualitative sociology is also underlined.

RETAIL BANKING BUSINESS: CURRENT STATE ANDSPECIFIC FEATURES

Directory of Open Access Journals (Sweden)

Гузель Рефкадовна Фаизова

2013-04-01

Full Text Available The role and importance of the retail banking business in the banking sector continueto grow. The current state of the retail banking business is considered and specific features of this area in the face of growing demand for banking products and services by the public and interest from lending institutions are identified by the article.Purpose: Research of current state of retail banking business and detection specific features of this area.Methodology: In the process of analysis and researchof the question the methods of economical and statistical analysis, methods of comparison and generalizationwereused.Results: The conclusion is that interest in the retail banking business continues to grow.There were revealed the role and the importance of standardized service processes and standardized products and services delivering as one of the main line of development in the segment of retail business.DOI: http://dx.doi.org/10.12731/2218-7405-2013-3-2

Dissociation between Features and Feature Relations in Infant Memory: Effects of Memory Load.

Science.gov (United States)

Bhatt, Ramesh S.; Rovee-Collier, Carolyn

1997-01-01

Four experiments examined effects of the number of features and feature relations on learning and long-term memory in 3-month olds. Findings suggested that memory load size selectively constrained infants' long-term memory for relational information, suggesting that in infants, features and relations are psychologically distinct and that memory…

Personalized features for attention detection in children with Attention Deficit Hyperactivity Disorder.

Science.gov (United States)

Fahimi, Fatemeh; Guan, Cuntai; Wooi Boon Goh; Kai Keng Ang; Choon Guan Lim; Tih Shih Lee

2017-07-01

Measuring attention from electroencephalogram (EEG) has found applications in the treatment of Attention Deficit Hyperactivity Disorder (ADHD). It is of great interest to understand what features in EEG are most representative of attention. Intensive research has been done in the past and it has been proven that frequency band powers and their ratios are effective features in detecting attention. However, there are still unanswered questions, like, what features in EEG are most discriminative between attentive and non-attentive states? Are these features common among all subjects or are they subject-specific and must be optimized for each subject? Using Mutual Information (MI) to perform subject-specific feature selection on a large data set including 120 ADHD children, we found that besides theta beta ratio (TBR) which is commonly used in attention detection and neurofeedback, the relative beta power and theta/(alpha+beta) (TBAR) are also equally significant and informative for attention detection. Interestingly, we found that the relative theta power (which is also commonly used) may not have sufficient discriminative information itself (it is informative only for 3.26% of ADHD children). We have also demonstrated that although these features (relative beta power, TBR and TBAR) are the most important measures to detect attention on average, different subjects have different set of most discriminative features.

Building-integrated photovoltaics (BIPV); GISS Gebaeude-Integrierte Solarstrom-Systeme (Building Integrated Photovoltaic BIPV)

Energy Technology Data Exchange (ETDEWEB)

Hof, R. [Geilinger Fassaden AG, Winterthur (Switzerland); Mikesch, W. [Colt Solar Technology AG, Baar (Switzerland); Miloni, R. [Lichtplanung und Architektur, Muelligen (Switzerland); Kaelin, T. [Jaakko Poeyry Infra, Zuerich (Switzerland); Nordmann, T. [TNC Consulting AG, Erlenbach (Switzerland); Meier, Ch. [Energiebuero Die Solarplaner, Zuerich (Switzerland); Locher, R. [Schweizerische Zentrale Fenster und Fassaden (SZFF), Dietikon (Switzerland)

2005-07-01

This extensive report for the Swiss Federal Office of Energy (SFOE) by the Swiss Central Association for Window and Facade Construction (SZFF) takes a look at the research project it launched to develop a basis for estimating existing potentials between facade builders and solar specialists and for the reduction of the technical impediments and mental barriers involved. The goals of the project are listed and the results expected are noted. Part-projects included are described and the results obtained so far are examined. These include information acquisition and analysis, surveys developed in co-operation with the University of Applied Sciences in Horw, Switzerland, a market survey and the development of a handbook and argumentation-aid available on the Internet and as a CD-ROM.

Eating disorder features and quality of life: Does gender matter?

Science.gov (United States)

Wagner, Allison F; Stefano, Emily C; Cicero, David C; Latner, Janet D; Mond, Jonathan M

2016-10-01

This study examined whether gender moderates the associations between eating disorder features and quality-of-life impairment and whether eating disorder features can explain gender differences in quality of life in a sample of undergraduate students. The SF-12 Physical and Mental Component Summary Scales were used to measure health-related quality of life (HRQoL), and the Eating Disorders Examination Questionnaire (EDE-Q) was used to quantify eating disorder behaviors and cognitions. These self-report forms were completed by undergraduate men and women (n = 709). Gender was a significant predictor of mental HRQoL, such that women in this sample reported poorer mental HRQoL than men. Eating disorder cognitions were the strongest predictor of undergraduate students' mental and physical HRQoL, while binge eating negatively predicted their physical HRQoL only. Gender was not found to moderate the associations between eating disorder features and HRQoL, and eating disorder cognitions were found to mediate the association between gender and mental HRQoL such that a proportion of the difference between undergraduate men and women's mental HRQoL was attributable to eating disorder cognitions. This study provided further evidence of the significant impact of eating disorder features, particularly eating disorder cognitions, on HRQoL. The finding that gender did not moderate the relationships between eating disorder features and HRQoL indicates the importance of investigating these features in both men and women in future research.

Feature inference with uncertain categorization: Re-assessing Anderson's rational model.

Science.gov (United States)

Konovalova, Elizaveta; Le Mens, Gaël

2017-09-18

A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson's model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson's model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson's model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.

Tracing the breeding farm of domesticated pig using feature selection (

Directory of Open Access Journals (Sweden)

Taehyung Kwon

2017-11-01

Full Text Available Objective Increasing food safety demands in the animal product market have created a need for a system to trace the food distribution process, from the manufacturer to the retailer, and genetic traceability is an effective method to trace the origin of animal products. In this study, we successfully achieved the farm tracing of 6,018 multi-breed pigs, using single nucleotide polymorphism (SNP markers strictly selected through least absolute shrinkage and selection operator (LASSO feature selection. Methods We performed farm tracing of domesticated pig (Sus scrofa from SNP markers and selected the most relevant features for accurate prediction. Considering multi-breed composition of our data, we performed feature selection using LASSO penalization on 4,002 SNPs that are shared between breeds, which also includes 179 SNPs with small between-breed difference. The 100 highest-scored features were extracted from iterative simulations and then evaluated using machine-leaning based classifiers. Results We selected 1,341 SNPs from over 45,000 SNPs through iterative LASSO feature selection, to minimize between-breed differences. We subsequently selected 100 highest-scored SNPs from iterative scoring, and observed high statistical measures in classification of breeding farms by cross-validation only using these SNPs. Conclusion The study represents a successful application of LASSO feature selection on multi-breed pig SNP data to trace the farm information, which provides a valuable method and possibility for further researches on genetic traceability.

What are the visual features underlying rapid object recognition?

Directory of Open Access Journals (Sweden)

Sébastien M Crouzet

2011-11-01

Full Text Available Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically-plausible computational models of (bottom-up pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task.

Depth estimation of features in video frames with improved feature matching technique using Kinect sensor

Science.gov (United States)

Sharma, Kajal; Moon, Inkyu; Kim, Sung Gaun

2012-10-01

Estimating depth has long been a major issue in the field of computer vision and robotics. The Kinect sensor's active sensing strategy provides high-frame-rate depth maps and can recognize user gestures and human pose. This paper presents a technique to estimate the depth of features extracted from video frames, along with an improved feature-matching method. In this paper, we used the Kinect camera developed by Microsoft, which captured color and depth images for further processing. Feature detection and selection is an important task for robot navigation. Many feature-matching techniques have been proposed earlier, and this paper proposes an improved feature matching between successive video frames with the use of neural network methodology in order to reduce the computation time of feature matching. The features extracted are invariant to image scale and rotation, and different experiments were conducted to evaluate the performance of feature matching between successive video frames. The extracted features are assigned distance based on the Kinect technology that can be used by the robot in order to determine the path of navigation, along with obstacle detection applications.

A ROC-based feature selection method for computer-aided detection and diagnosis

Science.gov (United States)

Wang, Songyuan; Zhang, Guopeng; Liao, Qimei; Zhang, Junying; Jiao, Chun; Lu, Hongbing

2014-03-01

Image-based computer-aided detection and diagnosis (CAD) has been a very active research topic aiming to assist physicians to detect lesions and distinguish them from benign to malignant. However, the datasets fed into a classifier usually suffer from small number of samples, as well as significantly less samples available in one class (have a disease) than the other, resulting in the classifier's suboptimal performance. How to identifying the most characterizing features of the observed data for lesion detection is critical to improve the sensitivity and minimize false positives of a CAD system. In this study, we propose a novel feature selection method mR-FAST that combines the minimal-redundancymaximal relevance (mRMR) framework with a selection metric FAST (feature assessment by sliding thresholds) based on the area under a ROC curve (AUC) generated on optimal simple linear discriminants. With three feature datasets extracted from CAD systems for colon polyps and bladder cancer, we show that the space of candidate features selected by mR-FAST is more characterizing for lesion detection with higher AUC, enabling to find a compact subset of superior features at low cost.

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors.

Science.gov (United States)

Li, Frédéric; Shirahama, Kimiaki; Nisar, Muhammad Adeel; Köping, Lukas; Grzegorzek, Marcin

2018-02-24

Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches-in particular deep-learning based-have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.

Doubly sparse factor models for unifying feature transformation and feature selection

International Nuclear Information System (INIS)

Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato; Matsumoto, Narihisa; Sugase-Miyamoto, Yasuko

2010-01-01

A number of unsupervised learning methods for high-dimensional data are largely divided into two groups based on their procedures, i.e., (1) feature selection, which discards irrelevant dimensions of the data, and (2) feature transformation, which constructs new variables by transforming and mixing over all dimensions. We propose a method that both selects and transforms features in a common Bayesian inference procedure. Our method imposes a doubly automatic relevance determination (ARD) prior on the factor loading matrix. We propose a variational Bayesian inference for our model and demonstrate the performance of our method on both synthetic and real data.

Doubly sparse factor models for unifying feature transformation and feature selection

Energy Technology Data Exchange (ETDEWEB)

Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato [ERATO, Okanoya Emotional Information Project, Japan Science Technology Agency, Saitama (Japan); Matsumoto, Narihisa; Sugase-Miyamoto, Yasuko, E-mail: okada@k.u-tokyo.ac.j [Human Technology Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki (Japan)

2010-06-01

A number of unsupervised learning methods for high-dimensional data are largely divided into two groups based on their procedures, i.e., (1) feature selection, which discards irrelevant dimensions of the data, and (2) feature transformation, which constructs new variables by transforming and mixing over all dimensions. We propose a method that both selects and transforms features in a common Bayesian inference procedure. Our method imposes a doubly automatic relevance determination (ARD) prior on the factor loading matrix. We propose a variational Bayesian inference for our model and demonstrate the performance of our method on both synthetic and real data.

Feature Import Vector Machine: A General Classifier with Flexible Feature Selection.

Science.gov (United States)

Ghosh, Samiran; Wang, Yazhen

2015-02-01

The support vector machine (SVM) and other reproducing kernel Hilbert space (RKHS) based classifier systems are drawing much attention recently due to its robustness and generalization capability. General theme here is to construct classifiers based on the training data in a high dimensional space by using all available dimensions. The SVM achieves huge data compression by selecting only few observations which lie close to the boundary of the classifier function. However when the number of observations are not very large (small n ) but the number of dimensions/features are large (large p ), then it is not necessary that all available features are of equal importance in the classification context. Possible selection of an useful fraction of the available features may result in huge data compression. In this paper we propose an algorithmic approach by means of which such an optimal set of features could be selected. In short, we reverse the traditional sequential observation selection strategy of SVM to that of sequential feature selection. To achieve this we have modified the solution proposed by Zhu and Hastie (2005) in the context of import vector machine (IVM), to select an optimal sub-dimensional model to build the final classifier with sufficient accuracy.

Psychological Features of Foreign Language Acquisition in Different Age Groups

Directory of Open Access Journals (Sweden)

N V Kudinova

2011-06-01

Full Text Available The influence of age factor on the foreign language learning is examined in the article from the practical point of view. The specific age features and their influence on the foreign language acquisition at different stages of age are highlighted and analyzed on the basis of psychological research.

Clinical features of pedophilia and implications for treatment.

Science.gov (United States)

Cohen, Lisa J; Galynker, Igor I

2002-09-01

The authors discuss the diagnostic criteria for pedophilia and review the literature on its clinical features, including data on prevalence, gender, age of onset, number of victims, frequency and type of acts, violence, impulsivity, and insight. Findings concerning the characteristics of victims (e.g., sex, age, relationship to the pedophile) and research on pedophilic subtypes-exclusive versus nonexclusive; incestuous versus nonincestuous; heterosexual, homosexual, or bisexual-are reviewed. Studies have shown that pedophiles may share many psychiatric features beyond deviant sexual desire, including high rates of comorbid axis I disorders (affective disorders, substance use disorders, impulse control disorders, other paraphilias) as well as severe axis II psychopathology (especially antisocial and Cluster C personality disorders). The authors present several possible etiological models for pedophilia and conclude that further research is needed concerning the etiological role of a childhood history of sexual abuse as well as the underlying neurobiology of deviant sexual arousal and decreased erotic differentiation. Finally, findings concerning pharmacological and cognitive-behavioral treatments for pedophilia are briefly reviewed. Recidivism, drop-out, and noncompliance are significant problems in the treatment of pedophilia. The authors review predictors of treatment outcome and conclude that pedophilia is extremely difficult to treat and that effective treatment needs to be intensive, long-term, and comprehensive, possibly with lifetime follow-up.

Attentional Selection of Feature Conjunctions Is Accomplished by Parallel and Independent Selection of Single Features.

Science.gov (United States)

Andersen, Søren K; Müller, Matthias M; Hillyard, Steven A

2015-07-08

Experiments that study feature-based attention have often examined situations in which selection is based on a single feature (e.g., the color red). However, in more complex situations relevant stimuli may not be set apart from other stimuli by a single defining property but by a specific combination of features. Here, we examined sustained attentional selection of stimuli defined by conjunctions of color and orientation. Human observers attended to one out of four concurrently presented superimposed fields of randomly moving horizontal or vertical bars of red or blue color to detect brief intervals of coherent motion. Selective stimulus processing in early visual cortex was assessed by recordings of steady-state visual evoked potentials (SSVEPs) elicited by each of the flickering fields of stimuli. We directly contrasted attentional selection of single features and feature conjunctions and found that SSVEP amplitudes on conditions in which selection was based on a single feature only (color or orientation) exactly predicted the magnitude of attentional enhancement of SSVEPs when attending to a conjunction of both features. Furthermore, enhanced SSVEP amplitudes elicited by attended stimuli were accompanied by equivalent reductions of SSVEP amplitudes elicited by unattended stimuli in all cases. We conclude that attentional selection of a feature-conjunction stimulus is accomplished by the parallel and independent facilitation of its constituent feature dimensions in early visual cortex. The ability to perceive the world is limited by the brain's processing capacity. Attention affords adaptive behavior by selectively prioritizing processing of relevant stimuli based on their features (location, color, orientation, etc.). We found that attentional mechanisms for selection of different features belonging to the same object operate independently and in parallel: concurrent attentional selection of two stimulus features is simply the sum of attending to each of those

How Do Ownership Features Affect Corporate Governance Disclosure ? – The Case Of Banking System

Directory of Open Access Journals (Sweden)

Cristina Stefanescu

2013-04-01

Full Text Available The purpose of our empirical study is to assess the relationship between ownership’features and the level of disclosure in case of banking institutions listed on London Stock Exchange,basing on the general statement that disclosure and quality of corporate governance system are twoclosely related concepts-the higher the level of transparency, the better the quality corporategovernance practices.The research methodology used for achieving our goal is based on econometric analysis usingstatistical tools-correlations for identifying the relationships and regressions for assessing them-allof these beingperformed using SPSS software. In this respect, we developed a disclosure index,considered structure and concentration as features for assessing ownership.The results of the performed analysis reveal significant positive influences of all features testedon thelevel of disclosure, thus confirming our assumptions that the higher the quality of ownership, thehigher the level of disclosure.Irrespective of prior studies, which were focused on various corporate governance features, our papercomes to add value in this respect by testing only ownership. Moreover, because the banking systemwas little explored on this topic before, we had another chance to enrich the research literature withthis empirical study.

Less is More: How manipulative features affect children's learning from picture books.

Science.gov (United States)

Tare, Medha; Chiong, Cynthia; Ganea, Patricia; Deloache, Judy

2010-09-01

Picture books are ubiquitous in young children's lives and are assumed to support children's acquisition of information about the world. Given their importance, relatively little research has directly examined children's learning from picture books. We report two studies examining children's acquisition of labels and facts from picture books that vary on two dimensions: iconicity of the pictures and presence of manipulative features (or "pop-ups"). In Study 1, 20-month-old children generalized novel labels less well when taught from a book with manipulative features than from standard picture books without such elements. In Study 2, 30- and 36-month-old children learned fewer facts when taught from a manipulative picture book with drawings than from a standard picture book with realistic images and no manipulative features. The results of the two studies indicate that children's learning from picture books is facilitated by realistic illustrations, but impeded by manipulative features.

An Accurate Integral Method for Vibration Signal Based on Feature Information Extraction

Directory of Open Access Journals (Sweden)

Yong Zhu

2015-01-01

Full Text Available After summarizing the advantages and disadvantages of current integral methods, a novel vibration signal integral method based on feature information extraction was proposed. This method took full advantage of the self-adaptive filter characteristic and waveform correction feature of ensemble empirical mode decomposition in dealing with nonlinear and nonstationary signals. This research merged the superiorities of kurtosis, mean square error, energy, and singular value decomposition on signal feature extraction. The values of the four indexes aforementioned were combined into a feature vector. Then, the connotative characteristic components in vibration signal were accurately extracted by Euclidean distance search, and the desired integral signals were precisely reconstructed. With this method, the interference problem of invalid signal such as trend item and noise which plague traditional methods is commendably solved. The great cumulative error from the traditional time-domain integral is effectively overcome. Moreover, the large low-frequency error from the traditional frequency-domain integral is successfully avoided. Comparing with the traditional integral methods, this method is outstanding at removing noise and retaining useful feature information and shows higher accuracy and superiority.

Deep features for efficient multi-biometric recognition with face and ear images

Science.gov (United States)

Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

2017-07-01

Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

Statistical Analysis of Research Data | Center for Cancer Research

Science.gov (United States)

Recent advances in cancer biology have resulted in the need for increased statistical analysis of research data. The Statistical Analysis of Research Data (SARD) course will be held on April 5-6, 2018 from 9 a.m.-5 p.m. at the National Institutes of Health's Natcher Conference Center, Balcony C on the Bethesda Campus. SARD is designed to provide an overview on the general principles of statistical analysis of research data.  The first day will feature univariate data analysis, including descriptive statistics, probability distributions, one- and two-sample inferential statistics.

Wavelet-Based Feature Extraction in Fault Diagnosis for Biquad High-Pass Filter Circuit

OpenAIRE

Yuehai Wang; Yongzheng Yan; Qinyong Wang

2016-01-01

Fault diagnosis for analog circuit has become a prominent factor in improving the reliability of integrated circuit due to its irreplaceability in modern integrated circuits. In fact fault diagnosis based on intelligent algorithms has become a popular research topic as efficient feature extraction and selection are a critical and intricate task in analog fault diagnosis. Further, it is extremely important to propose some general guidelines for the optimal feature extraction and selection. In ...

Examining the design features of a communication-rich, problem-centred mathematics professional development

Science.gov (United States)

de Araujo, Zandra; Orrill, Chandra Hawley; Jacobson, Erik

2018-04-01

While there is considerable scholarship describing principles for effective professional development, there have been few attempts to examine these principles in practice. In this paper, we identify and examine the particular design features of a mathematics professional development experience provided for middle grades teachers over 14 weeks. The professional development was grounded in a set of mathematical tasks that each had one right answer, but multiple solution paths. The facilitator engaged participants in problem solving and encouraged participants to work collaboratively to explore different solution paths. Through analysis of this collaborative learning environment, we identified five design features for supporting teacher learning of important mathematics and pedagogy in a problem-solving setting. We discuss these design features in depth and illustrate them by presenting an elaborated example from the professional development. This study extends the existing guidance for the design of professional development by examining and operationalizing the relationships among research-based features of effective professional development and the enacted features of a particular design.

Guilt by Association: The 13 micron Dust Feature in Circumstellar Shells and Related Spectral Features

Science.gov (United States)

Sloan, G. C.; Kraemer, K. E.; Goebel, J. H.; Price, S. D.

A study of spectra from the SWS on ISO of optically thin oxygen-rich dust shells shows that the strength of the 13 micron dust emission feature is correlated with the CO2 bands (13--17 microns) and dust emission features at 19.8 and 28.1 microns. SRb variables tend to show stronger 13 micron features than Mira variables, suggesting that the presence of the 13 micron and related features depends on pulsation mode and mass-loss rate. The absence of any correlation to dust emission features at 16.8 and 32 microns makes spinel an unlikely carrier. The most plausible carrier of the 13 micron feature remains crystalline alumina, and we suggest that the related dust features may be crystalline silicates. When dust forms in regions of low density, it may condense into crystalline grain structures.

Digit recognition for Arabic/Jawi and Roman using features from triangle geometry

Science.gov (United States)

Azmi, Mohd Sanusi; Omar, Khairuddin; Nasrudin, Mohamad Faidzul; Idrus, Bahari; Wan Mohd Ghazali, Khadijah

2013-04-01

A novel method is proposed to recognize the Arab/Jawi and Roman digits. This new method is based on features from the triangle geometry, normalized into nine features. The features are used for zoning which results in five and 25 zones. The algorithm is validated by using three standard datasets which are publicly available and used by researchers in this field. The first dataset is HODA that contains 60,000 images for training and 20,000 images for testing. The second dataset is IFHCDB. This dataset has 52,380 isolated characters and 17,740 digits. Only the 17,740 images of digits are used for this research. For the roman digit, MNIST are chosen. MNIST dataset has 60,000 images for training and 10,000 images for testing. Supervised (SML) and Unsupervised Machine Learning (UML) are used to test the nine features. The SML used are Neural Network (NN) and Support Vector Machine (SVM). Whereas the UML uses Euclidean Distance Method with data mining algorithms; namely Mean Average Precision (eMAP) and Frequency Based (eFB). Results for SML testing for HODA dataset are 98.07% accuracy for SVM, and 96.73% for NN. For IFHCDB and MNIST the accuracy are 91.75% and 93.095% respectively. For the UML tests, HODA dataset is 93.91%, IFHCDB 85.94% and MNIST 86.61%. The train and test images are selected using both random and the original dataset's distribution. The results show that the accuracy of proposed algorithm is over 90% for each SML trained datasets where the highest result is the one that uses 25 zones features.

Feature displacement interpolation

DEFF Research Database (Denmark)

Nielsen, Mads; Andresen, Per Rønsholt

1998-01-01

Given a sparse set of feature matches, we want to compute an interpolated dense displacement map. The application may be stereo disparity computation, flow computation, or non-rigid medical registration. Also estimation of missing image data, may be phrased in this framework. Since the features...... often are very sparse, the interpolation model becomes crucial. We show that a maximum likelihood estimation based on the covariance properties (Kriging) show properties more expedient than methods such as Gaussian interpolation or Tikhonov regularizations, also including scale......-selection. The computational complexities are identical. We apply the maximum likelihood interpolation to growth analysis of the mandibular bone. Here, the features used are the crest-lines of the object surface....

Research Note:

DEFF Research Database (Denmark)

Behuria, Pritish; Buur, Lars; Gray, Hazel

2017-01-01

its core conceptual and methodological features. This Research Note starts by setting out our understanding of political settlements and provides an overview of existing political settlements literature on African countries. The note then explores how the key concept of ‘holding power’ has been...

The Role of Book Features in Young Children's Transfer of Information from Picture Books to Real-World Contexts.

Science.gov (United States)

Strouse, Gabrielle A; Nyhout, Angela; Ganea, Patricia A

2018-01-01

Picture books are an important source of new language, concepts, and lessons for young children. A large body of research has documented the nature of parent-child interactions during shared book reading. A new body of research has begun to investigate the features of picture books that support children's learning and transfer of that information to the real world. In this paper, we discuss how children's symbolic development, analogical reasoning, and reasoning about fantasy may constrain their ability to take away content information from picture books. We then review the nascent body of findings that has focused on the impact of picture book features on children's learning and transfer of words and letters, science concepts, problem solutions, and morals from picture books. In each domain of learning we discuss how children's development may interact with book features to impact their learning. We conclude that children's ability to learn and transfer content from picture books can be disrupted by some book features and research should directly examine the interaction between children's developing abilities and book characteristics on children's learning.

The Role of Book Features in Young Children's Transfer of Information from Picture Books to Real-World Contexts

Science.gov (United States)

Strouse, Gabrielle A.; Nyhout, Angela; Ganea, Patricia A.

2018-01-01

Picture books are an important source of new language, concepts, and lessons for young children. A large body of research has documented the nature of parent-child interactions during shared book reading. A new body of research has begun to investigate the features of picture books that support children's learning and transfer of that information to the real world. In this paper, we discuss how children's symbolic development, analogical reasoning, and reasoning about fantasy may constrain their ability to take away content information from picture books. We then review the nascent body of findings that has focused on the impact of picture book features on children's learning and transfer of words and letters, science concepts, problem solutions, and morals from picture books. In each domain of learning we discuss how children's development may interact with book features to impact their learning. We conclude that children's ability to learn and transfer content from picture books can be disrupted by some book features and research should directly examine the interaction between children's developing abilities and book characteristics on children's learning. PMID:29467690

The Role of Book Features in Young Children's Transfer of Information from Picture Books to Real-World Contexts

Directory of Open Access Journals (Sweden)

Gabrielle A. Strouse

2018-02-01

Full Text Available Picture books are an important source of new language, concepts, and lessons for young children. A large body of research has documented the nature of parent-child interactions during shared book reading. A new body of research has begun to investigate the features of picture books that support children's learning and transfer of that information to the real world. In this paper, we discuss how children's symbolic development, analogical reasoning, and reasoning about fantasy may constrain their ability to take away content information from picture books. We then review the nascent body of findings that has focused on the impact of picture book features on children's learning and transfer of words and letters, science concepts, problem solutions, and morals from picture books. In each domain of learning we discuss how children's development may interact with book features to impact their learning. We conclude that children's ability to learn and transfer content from picture books can be disrupted by some book features and research should directly examine the interaction between children's developing abilities and book characteristics on children's learning.

Mid-Infrared Emission Features in the ISM: Feature-to-Features Flux Ratios

Science.gov (United States)

Lu, N. Y.

1998-01-01

Using a limited, but representative sample of sources in the ISM of our Galaxy with published spectra from the Infrared Space Observatory, we analyze flux ratios between the major mid-IR emission features (EFs) centered around 6.2, 7.7, 8.6 and 11.3 mu, respectively.

FEATUREOUS: AN INTEGRATED ENVIRONMENT FOR FEATURE-CENTRIC ANALYSIS AND MODIFICATION OF OBJECT-ORIENTED SOFTWARE

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2011-01-01

The decentralized nature of collaborations between objects in object-oriented software makes it difficult to understand the implementations of user-observable program features and their respective interdependencies. As feature-centric program understanding and modification are essential during...... software maintenance and evolution, this situation needs to change. In this paper, we present Featureous, an integrated development environment built on top of the NetBeans IDE that facilitates feature-centric analysis of object-oriented software. Our integrated development environment encompasses...... a lightweight feature location mechanism, a number of reusable analytical views, and necessary APIs for supporting future extensions. The base of the integrated development environment is a conceptual framework comprising of three complementary dimensions of comprehension: perspective, abstraction...

A Robust Shape Reconstruction Method for Facial Feature Point Detection

Directory of Open Access Journals (Sweden)

Shuqiu Tan

2017-01-01

Full Text Available Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods.

STYLISTIC FEATURES OF ADVERTISING TEXTS OF INFORMATIVE AND COMPARATIVE TYPES

Directory of Open Access Journals (Sweden)

Poddubskaya, O.N.

2016-06-01

Full Text Available The relevance of this article is related to the fact that nowadays advertising has a very strong impact both on the consumer market, political and cultural life of society, and on the language and its development as a system. Advertising has given rise to the development of a special set of stylistic features of a text, formed under the influence of reviving advertising traditions in the Russian language and under the active impact of energetic and pushy European advertising. The purpose of this study is to explore stylistic features of informative and comparative advertising texts. The object of research is Russian-language advertising in printed media and on television. In the end of the article we made conclusions about groups of language means used for different stylistic devices in informative and comparative advertising texts. Analysis of stylistic features of modern informative and comparative advertising texts can be of great interest to specialists in the field of theoretical studies of modern advertising.

Integration of pore features into the evaluation of fingerprint evidence.

Science.gov (United States)

Anthonioz, Alexandre; Champod, Christophe

2014-01-01

Fingerprint practitioners rely on level 3 features to make decisions in relation to the source of an unknown friction ridge skin impression. This research proposes to assess the strength of evidence associated with pores when shown in (dis)agreement between a mark and a reference print. Based upon an algorithm designed to automatically detect pores, a metric is defined in order to compare different impressions. From this metric, the weight of the findings is quantified using a likelihood ratio. The results obtained on four configurations and 54 donors show the significant contribution of the pore features and translate into statistical terms what latent fingerprint examiners have developed holistically through experience. The system provides LRs that are indicative of the true state under both the prosecution and the defense propositions. Not only such a system brings transparency regarding the weight to assign to such features, but also forces a discussion in relation to the risks of such a model to mislead. © 2013 American Academy of Forensic Sciences.

English Computer Discourse: Some Characteristic Features

Directory of Open Access Journals (Sweden)

Tatjana Rusko

2013-12-01

Full Text Available The problem of virtual discourse is coming into focus of linguistic research. This interest results from the rapid spread of information technology, modern Internet culture incipience, a symbol of information revolution, new opportunities and threats that accompany computer civilization. The emergence of the communicative environment as a particular sphere of language actualization, necessitates new language means of communication or transformation and reframing the already existing ones. Obviously, it’s time to talk about the formation of a new discourse in the new communicative space – computer (electronic, virtual discourse, which subsequently may considerably affect the speech behavior of society. The present article makes an attempt to identify some linguistic and communicative features of virtual discourse. Computer discourse, being a sub-language of hybrid character, combines elements of oral and written discourse with its own specific features. It should be noted that in the context of information culture the problem of communication interaction is among the most topical issues in science and education. There is hardly any doubt that the study and advancement of virtual communication culture is one of higher education distinctive mission components.

Vehicle license plate recognition based on geometry restraints and multi-feature decision

Science.gov (United States)

Wu, Jianwei; Wang, Zongyue

2005-10-01

Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

Application and interview features used to assess applicant qualifications for residency training.

Science.gov (United States)

Butts, Allison R; Smith, Kelly M

2015-02-01

To determine what factors residency program directors (RPDs) consider and what methods they use to assess applicants. Respondents ranked the importance of 27 applicant features within domains: academics/credentials, application features/program fit, involvement, professional experience, research/ teaching experience, and postgraduate year 1 (PGY-1) residency experience. Rank was assigned in an ordinal fashion (1 = most important feature). The domains were characterized by their importance (mean % ± SD) in selecting candidates for interviews. Participants characterized their screening process according to 8 application and 6 interview features and the corresponding applicant dimensions evaluated. RPDs rated the importance of 14 methods applicants used to communicate with the program and 3 methods by which references were obtained. A Likert scale was used for rating (4 = crucial features). The approaches the program used to evaluate 12 application features or interpersonal interactions were reported. The most important application domain was application features/program fit (26.28 ± 19.11). The highest ranked application feature was program fit (2.04 ± 1.17). The applicant's cover letter, recommendation letters, curriculum vitae, and interview meal were commonly used to assess communication and interpersonal skills, knowledge base, and experience. The most important communication venue was the on-site interview (3.95 ± 0.23). Recommendations solicited by RPDs (3.42 ± 0.69) were most important. Programs formally evaluated the interview (89%) and recommendation letters (84%). Understanding the importance that RPDs place on application and interview features, as well as the process used to assess communication skills and interpersonal interactions, should allow residency candidates to become more competitive residency prospects.

Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

Science.gov (United States)

Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

2014-01-01

Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

Introduction to Special Feature on Catastrophic Thresholds, Perspectives, Definitions, and Applications

Directory of Open Access Journals (Sweden)

Robert A. Washington-Allen

2010-09-01

Full Text Available The contributions to this special feature focus on several conceptual and operational applications for understanding non-linear behavior of complex systems with various ecological criteria at unique levels of organization. The organizing theme of the feature emphasizes alternative stable states or regimes and intervening thresholds that possess great relevance to ecology and natural resource management. The authors within this special feature address the conceptual models of catastrophe theory, self-organization, cross-scale interactions and time-scale calculus; develop operational definitions and procedures for understanding the occurrence of dynamic regimes or multiple stable states and thresholds; suggest diagnostics tools for detection of states and thresholds and contribute to the development of scaling laws; and finally, demonstrate applications that promote both greater ecological understanding and management prescriptions for insect and disease outbreaks, resource island formation, and characterization of ecological resilience. This Special Feature concludes with a synthesis of the commonalities and disparities of concepts and interpretations among the contributed papers to identify issues and approaches that merit further research emphasis.

Research Article Special Issue

African Journals Online (AJOL)

pc

2017-11-24

Nov 24, 2017 ... Non-finite forms of the verb attract more and more attention of modern researchers. The target of research is morphological and syntactical features of adjectives in ... Now, in the world of globalization and integration, there is close ... translation theory, comparative linguistics, and typology can be used while ...

Improving Naive Bayes with Online Feature Selection for Quick Adaptation to Evolving Feature Usefulness

Energy Technology Data Exchange (ETDEWEB)

Pon, R K; Cardenas, A F; Buttler, D J

2007-09-19

The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance [1], particularly over the initial set of news articles being classified. The initial set of document is critical for a user when considering which particular news recommendation system to adopt. To address these problems, we introduce an improved version of the naive Bayes classifier with online feature selection. We use correlation to determine the utility of each feature and take advantage of the conditional independence assumption used by naive Bayes for online feature selection and classification. The augmented naive Bayes classifier performs 28% better than the traditional naive Bayes classifier in recommending news articles from the Yahoo! RSS feeds.

THEORETICAL QUESTIONS OF INVESTMENT RISK RESEARCH, ITS MAIN FEATURES AND CLASSIFICATION

Directory of Open Access Journals (Sweden)

I. A. Kadyrbaev

2016-01-01

Full Text Available The article examines framework methodology of investment risk. The subject of the study are the basic theoretical positions directly related to the economic category of "investment risk". The purpose of this writing is the improvement of the methodology of the study of investment risk in the investment activity. This objective is to define the tasks, which consist in the formulation of the concept of "investment risk", the development of investment risk classification to provide investors with effective protection against such risks. The implementation  of the tasks will create conditions for the growth of investments in Russia. Currently, effective investment strategy for such an increase, is among the priority directions of development of the Russian economy.The article deals with logically interrelated study of basic economic categories, influencing directly on the investment risk. Author provided classification of investments in accordance with the level of risk. Examine the matter of the financial-economic category of the concept of "investment risk" and the classification of investment risks. Specified main features of the investment risk, which allowed to reveal specifics, which consists in the redistribution of capital in various types of assets in order to maximize profits or to obtain significant social effect.

Access, Participation, and Supports: The Defining Features of High-Quality Inclusion

Science.gov (United States)

Buysse, Virginia

2011-01-01

This article describes current knowledge about early childhood inclusion, summarizing research and the DEC/NAEYC joint position statement on inclusion. The article also describes effective or promising educational practices that promote access, participation, and supports--the defining features of high-quality inclusion. Future efforts to improve…

Feature recognition and detection for ancient architecture based on machine vision

Science.gov (United States)

Zou, Zheng; Wang, Niannian; Zhao, Peng; Zhao, Xuefeng

2018-03-01

Ancient architecture has a very high historical and artistic value. The ancient buildings have a wide variety of textures and decorative paintings, which contain a lot of historical meaning. Therefore, the research and statistics work of these different compositional and decorative features play an important role in the subsequent research. However, until recently, the statistics of those components are mainly by artificial method, which consumes a lot of labor and time, inefficiently. At present, as the strong support of big data and GPU accelerated training, machine vision with deep learning as the core has been rapidly developed and widely used in many fields. This paper proposes an idea to recognize and detect the textures, decorations and other features of ancient building based on machine vision. First, classify a large number of surface textures images of ancient building components manually as a set of samples. Then, using the convolution neural network to train the samples in order to get a classification detector. Finally verify its precision.

Exploring the Relevance of Qualitative Research Synthesis to Higher Education Research and Practice

Science.gov (United States)

Major, Claire; Savin-Baden, Maggi

2010-01-01

This paper proposes the importance of qualitative research synthesis to the field of higher education. It examines seven key texts that undertake synthesis in this field and compares essential features and elements across studies. The authors indicate strengths of the approaches and highlight ways forward for using qualitative research synthesis…

Premotor and non-motor features of Parkinson’s disease

Science.gov (United States)

Goldman, Jennifer G.; Postuma, Ron

2014-01-01

Purpose of review This review highlights recent advances in premotor and non-motor features in Parkinson’s disease, focusing on these issues in the context of prodromal and early stage Parkinson’s disease. Recent findings While Parkinson’s disease patients experience a wide range of non-motor symptoms throughout the disease course, studies demonstrate that non-motor features are not solely a late manifestation. Indeed, disturbances of smell, sleep, mood, and gastrointestinal function may herald Parkinson’s disease or related synucleinopathies and precede these neurodegenerative conditions by 5 or more years. In addition, other non-motor symptoms such as cognitive impairment are now recognized in incident or de novo Parkinson’s disease cohorts. Many of these non-motor features reflect disturbances in non-dopaminergic systems and early involvement of peripheral and central nervous systems including olfactory, enteric, and brainstem neurons as in Braak’s proposed pathological staging of Parkinson’s disease. Current research focuses on identifying potential biomarkers that may detect persons at risk for Parkinson’s disease and permit early intervention with neuroprotective or disease-modifying therapeutics. Summary Recent studies provide new insights on the frequency, pathophysiology, and importance of non-motor features in Parkinson’s disease as well as the recognition that these non-motor symptoms occur in premotor, early, and later phases of Parkinson’s disease. PMID:24978368

Dialogic and Hortatory Features in the Writing of Chinese Candidates for the IELTS Test

Science.gov (United States)

Mayor, Barbara M.

2006-01-01

Research conducted in the context of the IELTS Research Program indicates that there are recurrent features in the writing under test conditions of candidates from Chinese language backgrounds, particularly in terms of interpersonal tenor. These include a high level of interpersonal reference, combined with a heavily dialogic and hortatory style.…

Case study of information product for strategy research, planning research, and policy research

International Nuclear Information System (INIS)

Yuan Yujun; Zou Lin; Liu Qun; Wang Yongping

2010-01-01

Soft science research is significant and can directly support the decision-making and development. The strategy research, planning research, and policy research each play an important role in soft science research. As the National Strategy of Informatization being implemented and advanced, some progress are made and some special information tools are produced in the process of strengthening the development research with information technologies. At first, the article introduced some cases of information products application, such as the domestic and overseas information products for energy strategy research and planning research and policy research, the governmental management information system for planning and investment, examination and approval and permission system for the planning of the land for construction, China agriculture decision support system and so on, and also gave a brief analysis on the theories and methods, main functions and application status. And then, with a analysis on the features of the works of development planning of China National Nuclear Corporation (CNNC) development, this article gave some suggestions on how to strengthen the development of information system for the development planning of the CNNC. (authors)

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

Directory of Open Access Journals (Sweden)

Frédéric Li

2018-02-01

Full Text Available Getting a good feature representation of data is paramount for Human Activity Recognition (HAR using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM to obtain features characterising both short- and long-term time dependencies in the data.

Middle-aged women's preferred theory-based features in mobile physical activity applications.

Science.gov (United States)

Ehlers, Diane K; Huberty, Jennifer L

2014-09-01

The purpose of this study was to describe which theory-based behavioral and technological features middle-aged women prefer to be included in a mobile application designed to help them adopt and maintain regular physical activity (PA). Women aged 30 to 64 years (N = 120) completed an online survey measuring their demographics and mobile PA application preferences. The survey was developed upon behavioral principles of Social Cognitive Theory, recent mobile app research, and technology adoption principles of the Unified Theory of Acceptance and Use of Technology. Frequencies were calculated and content analyses conducted to identify which features women most preferred. Behavioral features that help women self-regulate their PA (PA tracking, goal-setting, progress monitoring) were most preferred. Technological features that enhance perceived effort expectancy and playfulness were most preferred. Many women reported the desire to interact and compete with others through the application. Theory-based PA self-regulation features and theory-based design features that improve perceived effort expectancy and playfulness may be most beneficial in a mobile PA application for middle-aged women. Opportunities to interact with other people and the employment of social, game-like activities may also be attractive. Interdisciplinary engagement of experts in PA behavior change, technology adoption, and software development is needed.

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.

Science.gov (United States)

Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2017-02-27

In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called 'shadow features' are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

The Features and Translation Strategies of English Advertising Slogan

Institute of Scientific and Technical Information of China (English)

杨赬

2014-01-01

It is all shared by us that advertising has its cultural and social specialty as well as its own linguistic features. By means of advertising, society and the people can indeed obtain some useful information. To some degree, it is without doubt that language in advertising has a profound impact on the public and people as well as their behaviors. By using special means of expressions, sentences and structures technique, advertising can be given more persuasive and attractive influence. The most effective way that we can write some English advertising slogan in order to meet and satisfy foreigners ’tastes and draw their attention to the particu⁃lar Chinese products is to research the major features of English advertisements and seize the principles and strategies for their translation.

HANARO cooling features: design and experience

International Nuclear Information System (INIS)

Park, Cheol; Chae, Hee-Taek; Han, Gee-Yang; Jun, Byung-Jin; Ahn, Guk-Hoon

1999-01-01

In order to achieve the safe core cooling during normal operation and upset conditions, HANARO adopted an upward forced convection cooling system with dual containment arrangements instead of the forced downward flow system popularly used in the majority of forced convection cooling research reactors. This kind of upward flow system was selected by comparing the relative merits of upward and downward flow systems from various points of view such as safety, performance, maintenance. However, several operational matters which were not regarded as serious at design come out during operation. In this paper are presented the design and operational experiences on the unique cooling features of HANARO. (author)

Feature confirmation in object perception: Feature integration theory 26 years on from the Treisman Bartlett lecture.

Science.gov (United States)

Humphreys, Glyn W

2016-10-01

The Treisman Bartlett lecture, reported in the Quarterly Journal of Experimental Psychology in 1988, provided a major overview of the feature integration theory of attention. This has continued to be a dominant account of human visual attention to this day. The current paper provides a summary of the work reported in the lecture and an update on critical aspects of the theory as applied to visual object perception. The paper highlights the emergence of findings that pose significant challenges to the theory and which suggest that revisions are required that allow for (a) several rather than a single form of feature integration, (b) some forms of feature integration to operate preattentively, (c) stored knowledge about single objects and interactions between objects to modulate perceptual integration, (d) the application of feature-based inhibition to object files where visual features are specified, which generates feature-based spreading suppression and scene segmentation, and (e) a role for attention in feature confirmation rather than feature integration in visual selection. A feature confirmation account of attention in object perception is outlined.

Action research: Scandinavian Experiences

DEFF Research Database (Denmark)

Rasmussen, Lauge Baungaard

2004-01-01

The article focus on paradigms, methods and ethics of action research in the Scandinavian countries. The special features of the action research paradigm is identified. A historical overview follows of some main action research projects in Norway, Sweden and Denmark. The tendency towards upsclae...... action research projects from organisational or small community projects yo large-scale, regional based network apporaches are also outlined and discussed. Finally, a synthesised approach of the classical, socio-technical action research approach and the large-scale network and holistic approaches...

Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems

Directory of Open Access Journals (Sweden)

Yujing Sun

2016-12-01

Full Text Available The module temperature is the most important parameter influencing the output power of solar photovoltaic (PV systems, aside from solar irradiance. In this paper, we focus on the interdisciplinary research that combines the correlation analysis, mutual information (MI and heat transfer theory, which aims to figure out the correlative relations between different meteorological impact factors (MIFs and PV module temperature from both quality and quantitative aspects. The identification and confirmation of primary MIFs of PV module temperature are investigated as the first step of this research from the perspective of physical meaning and mathematical analysis about electrical performance and thermal characteristic of PV modules based on PV effect and heat transfer theory. Furthermore, the quantitative description of the MIFs influence on PV module temperature is mathematically formulated as several indexes using correlation-based feature selection (CFS and MI theory to explore the specific impact degrees under four different typical weather statuses named general weather classes (GWCs. Case studies for the proposed methods were conducted using actual measurement data of a 500 kW grid-connected solar PV plant in China. The results not only verified the knowledge about the main MIFs of PV module temperatures, more importantly, but also provide the specific ratio of quantitative impact degrees of these three MIFs respectively through CFS and MI based measures under four different GWCs.

Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

Directory of Open Access Journals (Sweden)

Chunlan Yang

2017-04-01

Full Text Available In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28 each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%, and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78 indicates

Classification of visual and linguistic tasks using eye-movement features.

Science.gov (United States)

Coco, Moreno I; Keller, Frank

2014-03-07

The role of the task has received special attention in visual-cognition research because it can provide causal explanations of goal-directed eye-movement responses. The dependency between visual attention and task suggests that eye movements can be used to classify the task being performed. A recent study by Greene, Liu, and Wolfe (2012), however, fails to achieve accurate classification of visual tasks based on eye-movement features. In the present study, we hypothesize that tasks can be successfully classified when they differ with respect to the involvement of other cognitive domains, such as language processing. We extract the eye-movement features used by Greene et al. as well as additional features from the data of three different tasks: visual search, object naming, and scene description. First, we demonstrated that eye-movement responses make it possible to characterize the goals of these tasks. Then, we trained three different types of classifiers and predicted the task participants performed with an accuracy well above chance (a maximum of 88% for visual search). An analysis of the relative importance of features for classification accuracy reveals that just one feature, i.e., initiation time, is sufficient for above-chance performance (a maximum of 79% accuracy in object naming). Crucially, this feature is independent of task duration, which differs systematically across the three tasks we investigated. Overall, the best task classification performance was obtained with a set of seven features that included both spatial information (e.g., entropy of attention allocation) and temporal components (e.g., total fixation on objects) of the eye-movement record. This result confirms the task-dependent allocation of visual attention and extends previous work by showing that task classification is possible when tasks differ in the cognitive processes involved (purely visual tasks such as search vs. communicative tasks such as scene description).

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses

Science.gov (United States)

Krömker, Dörthe; Meguerditchian, Ari N; Tamblyn, Robyn

2016-01-01

Background Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids. Objective The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making. Methods Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making. Results Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however

Features of Computer-Based Decision Aids: Systematic Review, Thematic Synthesis, and Meta-Analyses.

Science.gov (United States)

Syrowatka, Ania; Krömker, Dörthe; Meguerditchian, Ari N; Tamblyn, Robyn

2016-01-26

Patient information and education, such as decision aids, are gradually moving toward online, computer-based environments. Considerable research has been conducted to guide content and presentation of decision aids. However, given the relatively new shift to computer-based support, little attention has been given to how multimedia and interactivity can improve upon paper-based decision aids. The first objective of this review was to summarize published literature into a proposed classification of features that have been integrated into computer-based decision aids. Building on this classification, the second objective was to assess whether integration of specific features was associated with higher-quality decision making. Relevant studies were located by searching MEDLINE, Embase, CINAHL, and CENTRAL databases. The review identified studies that evaluated computer-based decision aids for adults faced with preference-sensitive medical decisions and reported quality of decision-making outcomes. A thematic synthesis was conducted to develop the classification of features. Subsequently, meta-analyses were conducted based on standardized mean differences (SMD) from randomized controlled trials (RCTs) that reported knowledge or decisional conflict. Further subgroup analyses compared pooled SMDs for decision aids that incorporated a specific feature to other computer-based decision aids that did not incorporate the feature, to assess whether specific features improved quality of decision making. Of 3541 unique publications, 58 studies met the target criteria and were included in the thematic synthesis. The synthesis identified six features: content control, tailoring, patient narratives, explicit values clarification, feedback, and social support. A subset of 26 RCTs from the thematic synthesis was used to conduct the meta-analyses. As expected, computer-based decision aids performed better than usual care or alternative aids; however, some features performed better than

Ankle fractures have features of an osteoporotic fracture.

Science.gov (United States)

Lee, K M; Chung, C Y; Kwon, S S; Won, S H; Lee, S Y; Chung, M K; Park, M S

2013-11-01

We report the bone attenuation of ankle joint measured on computed tomography (CT) and the cause of injury in patients with ankle fractures. The results showed age- and gender-dependent low bone attenuation and low-energy trauma in elderly females, which suggest the osteoporotic features of ankle fractures. This study was performed to investigate the osteoporotic features of ankle fracture in terms of bone attenuation and cause of injury. One hundred ninety-four patients (mean age 51.0 years, standard deviation 15.8 years; 98 males and 96 females) with ankle fracture were included. All patients underwent CT examination, and causes of injury (high/low-energy trauma) were recorded. Mean bone attenuations of the talus, medial malleolus, lateral malleolus, and distal tibial metaphysis were measured on CT images. Patients were divided into younger age (fractures than the younger age group. With increasing age, bone attenuations tended to decrease and the difference of bone attenuation between the genders tended to increase in the talus, medial malleolus, lateral malleolus, and distal tibial metaphysis. Ankle fracture had features of osteoporotic fracture that is characterized by age- and gender-dependent low bone attenuation. Ankle fracture should not be excluded from the clinical and research interest as well as from the benefit of osteoporosis management.

Familiarity and Within-Person Facial Variability: The Importance of the Internal and External Features.

Science.gov (United States)

Kramer, Robin S S; Manesi, Zoi; Towler, Alice; Reynolds, Michael G; Burton, A Mike

2018-01-01

As faces become familiar, we come to rely more on their internal features for recognition and matching tasks. Here, we assess whether this same pattern is also observed for a card sorting task. Participants sorted photos showing either the full face, only the internal features, or only the external features into multiple piles, one pile per identity. In Experiments 1 and 2, we showed the standard advantage for familiar faces-sorting was more accurate and showed very few errors in comparison with unfamiliar faces. However, for both familiar and unfamiliar faces, sorting was less accurate for external features and equivalent for internal and full faces. In Experiment 3, we asked whether external features can ever be used to make an accurate sort. Using familiar faces and instructions on the number of identities present, we nevertheless found worse performance for the external in comparison with the internal features, suggesting that less identity information was available in the former. Taken together, we show that full faces and internal features are similarly informative with regard to identity. In comparison, external features contain less identity information and produce worse card sorting performance. This research extends current thinking on the shift in focus, both in attention and importance, toward the internal features and away from the external features as familiarity with a face increases.

Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.

Science.gov (United States)

Melendez, Jaime; Sánchez, Clara I; van Ginneken, Bram; Karssemeijer, Nico

2014-08-01

Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed. The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together. The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002). Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature

The Ocean Carbon States Database: a proof-of-concept application of cluster analysis in the ocean carbon cycle

Science.gov (United States)

Latto, Rebecca; Romanou, Anastasia

2018-03-01

In this paper, we present a database of the basic regimes of the carbon cycle in the ocean, the ocean carbon states, as obtained using a data mining/pattern recognition technique in observation-based as well as model data. The goal of this study is to establish a new data analysis methodology, test it and assess its utility in providing more insights into the regional and temporal variability of the marine carbon cycle. This is important as advanced data mining techniques are becoming widely used in climate and Earth sciences and in particular in studies of the global carbon cycle, where the interaction of physical and biogeochemical drivers confounds our ability to accurately describe, understand, and predict CO2 concentrations and their changes in the major planetary carbon reservoirs. In this proof-of-concept study, we focus on using well-understood data that are based on observations, as well as model results from the NASA Goddard Institute for Space Studies (GISS) climate model. Our analysis shows that ocean carbon states are associated with the subtropical-subpolar gyre during the colder months of the year and the tropics during the warmer season in the North Atlantic basin. Conversely, in the Southern Ocean, the ocean carbon states can be associated with the subtropical and Antarctic convergence zones in the warmer season and the coastal Antarctic divergence zone in the colder season. With respect to model evaluation, we find that the GISS model reproduces the cold and warm season regimes more skillfully in the North Atlantic than in the Southern Ocean and matches the observed seasonality better than the spatial distribution of the regimes. Finally, the ocean carbon states provide useful information in the model error attribution. Model air-sea CO2 flux biases in the North Atlantic stem from wind speed and salinity biases in the subpolar region and nutrient and wind speed biases in the subtropics and tropics. Nutrient biases are shown to be most important in

Research evaluation

DEFF Research Database (Denmark)

Pedersen, David Budtz

2014-01-01

decisions that have marked the period since the first edition was researched and published. In addition, to help make ESTE more global and interdisciplinary in scope and reach, the second edition will engage consultants from ethics centers around the world, and will feature the revised title Ethics, Science...

Research Summaries

Science.gov (United States)

Brock, Stephen E., Ed.

2010-01-01

This column features summaries of research articles from 3 recent crisis management publications. The first, "School Shootings and Counselor Leadership: Four Lessons from the Field" summarized by Kristi Fenning, was conducted as the result of the increased demand for trained crisis personnel on school campuses. Survey participants were…

Safety features of the MAPLE-X10 reactor design

International Nuclear Information System (INIS)

Lee, A.G.; Bishop, W.E.; Heeds, W.

1990-09-01

The MAPLE-X10 reactor is a D 2 0-reflected, H 2 0-cooled and -moderated pool-type reactor under construction at the Chalk River Nuclear Laboratories. This 10-MW reactor will produce key medical and industrial radio-isotopes such as 99 Mo, 125 I, and 192 Ir. As the prototype for the MAPLE research reactor concept, the reactor incorporates diverse safety features both inherent in the design and in the added engineered systems. The safety requirements are analogous to those of the Canadian CANDU power reactor since standards for the licensing of new research reactors have not been developed yet by the licensing authority in Canada

Safety features of the MAPLE-X10 reactor design

International Nuclear Information System (INIS)

Lee, A.G.; Bishop, W.E.; Heeds, W.

1990-01-01

This paper reports on the MAPLE-X10 reactor D 2 O-reflected, H 2 O-cooled and -moderated pool- type reactor, under construction at the Chalk River Nuclear Laboratories. This 10-MW will produce key medical and industrial radioisotopes such as 99 Mo, 125 I, and 192 Ir. The prototype for the MAPLE research reactor concept, the reactor incorporates diverse safety features both inherent in the design and in the added engineered systems. The safety requirements are analogous to those of the Canadian CANDU power reactor as standards for the licensing of new research reactors have not been developed by the licensing authority in Canada

DYNAMIC FEATURE SELECTION FOR WEB USER IDENTIFICATION ON LINGUISTIC AND STYLISTIC FEATURES OF ONLINE TEXTS

Directory of Open Access Journals (Sweden)

A. A. Vorobeva

2017-01-01

Full Text Available The paper deals with identification and authentication of web users participating in the Internet information processes (based on features of online texts.In digital forensics web user identification based on various linguistic features can be used to discover identity of individuals, criminals or terrorists using the Internet to commit cybercrimes. Internet could be used as a tool in different types of cybercrimes (fraud and identity theft, harassment and anonymous threats, terrorist or extremist statements, distribution of illegal content and information warfare. Linguistic identification of web users is a kind of biometric identification, it can be used to narrow down the suspects, identify a criminal and prosecute him. Feature set includes various linguistic and stylistic features extracted from online texts. We propose dynamic feature selection for each web user identification task. Selection is based on calculating Manhattan distance to k-nearest neighbors (Relief-f algorithm. This approach improves the identification accuracy and minimizes the number of features. Experiments were carried out on several datasets with different level of class imbalance. Experiment results showed that features relevance varies in different set of web users (probable authors of some text; features selection for each set of web users improves identification accuracy by 4% at the average that is approximately 1% higher than with the use of static set of features. The proposed approach is most effective for a small number of training samples (messages per user.

A novel framework for feature extraction in multi-sensor action potential sorting.

Science.gov (United States)

Wu, Shun-Chi; Swindlehurst, A Lee; Nenadic, Zoran

2015-09-30

Extracellular recordings of multi-unit neural activity have become indispensable in neuroscience research. The analysis of the recordings begins with the detection of the action potentials (APs), followed by a classification step where each AP is associated with a given neural source. A feature extraction step is required prior to classification in order to reduce the dimensionality of the data and the impact of noise, allowing source clustering algorithms to work more efficiently. In this paper, we propose a novel framework for multi-sensor AP feature extraction based on the so-called Matched Subspace Detector (MSD), which is shown to be a natural generalization of standard single-sensor algorithms. Clustering using both simulated data and real AP recordings taken in the locust antennal lobe demonstrates that the proposed approach yields features that are discriminatory and lead to promising results. Unlike existing methods, the proposed algorithm finds joint spatio-temporal feature vectors that match the dominant subspace observed in the two-dimensional data without needs for a forward propagation model and AP templates. The proposed MSD approach provides more discriminatory features for unsupervised AP sorting applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Constructing New Biorthogonal Wavelet Type which Matched for Extracting the Iris Image Features

International Nuclear Information System (INIS)

Isnanto, R Rizal; Suhardjo; Susanto, Adhi

2013-01-01

Some former research have been made for obtaining a new type of wavelet. In case of iris recognition using orthogonal or biorthogonal wavelets, it had been obtained that Haar filter is most suitable to recognize the iris image. However, designing the new wavelet should be done to find a most matched wavelet to extract the iris image features, for which we can easily apply it for identification, recognition, or authentication purposes. In this research, a new biorthogonal wavelet was designed based on Haar filter properties and Haar's orthogonality conditions. As result, it can be obtained a new biorthogonal 5/7 filter type wavelet which has a better than other types of wavelets, including Haar, to extract the iris image features based on its mean-squared error (MSE) and Euclidean distance parameters.

Feature Size Effect on Formability of Multilayer Metal Composite Sheets under Microscale Laser Flexible Forming

Directory of Open Access Journals (Sweden)

Huixia Liu

2017-07-01

Full Text Available Multilayer metal composite sheets possess superior properties to monolithic metal sheets, and formability is different from monolithic metal sheets. In this research, the feature size effect on formability of multilayer metal composite sheets under microscale laser flexible forming was studied by experiment. Two-layer copper/nickel composite sheets were selected as experimental materials. Five types of micro molds with different diameters were utilized. The formability of materials was evaluated by forming depth, thickness thinning, surface quality, and micro-hardness distribution. The research results showed that the formability of two-layer copper/nickel composite sheets was strongly influenced by feature size. With feature size increasing, the effect of layer stacking sequence on forming depth, thickness thinning ratio, and surface roughness became increasingly larger. However, the normalized forming depth, thickness thinning ratio, surface roughness, and micro-hardness of the formed components under the same layer stacking sequence first increased and then decreased with increasing feature size. The deformation behavior of copper/nickel composite sheets was determined by the external layer. The deformation extent was larger when the copper layer was set as the external layer.

Predicting age groups of Twitter users based on language and metadata features.

Directory of Open Access Journals (Sweden)