Cryogenic terahertz spectrum of (+)-methamphetamine hydrochloride and assignment using solid-state density functional theory.

PubMed

Hakey, Patrick M; Allis, Damian G; Ouellette, Wayne; Korter, Timothy M

2009-04-30

The cryogenic terahertz spectrum of (+)-methamphetamine hydrochloride from 10.0 to 100.0 cm(-1) is presented, as is the complete structural analysis and vibrational assignment of the compound using solid-state density functional theory. This cryogenic investigation reveals multiple spectral features that were not previously reported in room-temperature terahertz studies of the title compound. Modeling of the compound employed eight density functionals utilizing both solid-state and isolated-molecule methods. The results clearly indicate the necessity of solid-state simulations for the accurate assignment of solid-state THz spectra. Assignment of the observed spectral features to specific atomic motions is based on the BP density functional, which provided the best-fit solid-state simulation of the experimental spectrum. The seven experimental spectral features are the result of thirteen infrared-active vibrational modes predicted at a BP/DNP level of theory with more than 90% of the total spectral intensity associated with external crystal vibrations.

SWIFT Center State Education Agency Blueprint for Equity-Based Inclusive Reform: Implementing, Sustaining, and Scaling up SWIFT in States

ERIC Educational Resources Information Center

Beitz, Linda M.; Mitchiner, Melinda; Sailor, Wayne; Nelson, Loui Lord

2016-01-01

This blueprint provides an overview of the technical assistance (TA) practices and collaborative learning structures that support state education agencies (SEAs) in whole system implementation of Schoolwide Integrated Framework for Transformation (SWIFT) Domains and Features, and in the development of state-level infrastructure hospitable to…

General properties of quantum optical systems in a strong field limit

NASA Technical Reports Server (NTRS)

Chumakov, S. M.; Klimov, Andrei B.

1994-01-01

We investigate the dynamics of an arbitrary atomic system (n-level atoms or many n-level atoms) interacting with a resonant quantized mode of an em field. If the initial field state is a coherent state with a large photon number then the system dynamics possesses some general features, independently of the particular structure of the atomic system. Namely, trapping states, factorization of the wave function, collapses and revivals of the atomic energy oscillations are discussed.

Synaptic State Matching: A Dynamical Architecture for Predictive Internal Representation and Feature Detection

PubMed Central

Tavazoie, Saeed

2013-01-01

Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework. PMID:23991161

EEG-based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

PubMed Central

Wade, Joshua W.; Key, Alexandra P.; Warren, Zachary E.; Sarkar, Nilanjan

2017-01-01

objective To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods Twenty adolescents with ASD participated in a six-session virtual reality driving simulator based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist’s rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins (Δf = 2 Hz) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention. PMID:28422647

Revealing the Coulomb interaction strength in a cuprate superconductor

DOE PAGES

Yang, S. -L.; Sobota, J. A.; He, Y.; ...

2017-12-08

Here, we study optimally doped Bi 2 Sr 2 Ca 0.92 Y 0.08 Cu 2 O 8 + δ (Bi2212) using angle-resolved two-photon photoemission spectroscopy. Three spectral features are resolved near 1.5, 2.7, and 3.6 eV above the Fermi level. By tuning the photon energy, we determine that the 2.7-eV feature arises predominantly from unoccupied states. The 1.5- and 3.6-eV features reflect unoccupied states whose spectral intensities are strongly modulated by the corresponding occupied states. These unoccupied states are thus consistent with the prediction from a cluster perturbation theory based on the single-band Hubbard model. Through this comparison, amore » Coulomb interaction strength U of 2.7 eV is extracted. Our study complements equilibrium photoemission spectroscopy and provides a direct spectroscopic measurement of the unoccupied states in cuprates. The determined Coulomb U indicates that the charge-transfer gap of optimally doped Bi2212 is 1.1 eV.« less

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

NASA Astrophysics Data System (ADS)

Lee, Jongpil; Nam, Juhan

2017-08-01

Music auto-tagging is often handled in a similar manner to image classification by regarding the 2D audio spectrogram as image data. However, music auto-tagging is distinguished from image classification in that the tags are highly diverse and have different levels of abstractions. Considering this issue, we propose a convolutional neural networks (CNN)-based architecture that embraces multi-level and multi-scaled features. The architecture is trained in three steps. First, we conduct supervised feature learning to capture local audio features using a set of CNNs with different input sizes. Second, we extract audio features from each layer of the pre-trained convolutional networks separately and aggregate them altogether given a long audio clip. Finally, we put them into fully-connected networks and make final predictions of the tags. Our experiments show that using the combination of multi-level and multi-scale features is highly effective in music auto-tagging and the proposed method outperforms previous state-of-the-arts on the MagnaTagATune dataset and the Million Song Dataset. We further show that the proposed architecture is useful in transfer learning.

Discriminative Dictionary Learning With Two-Level Low Rank and Group Sparse Decomposition for Image Classification.

PubMed

Wen, Zaidao; Hou, Zaidao; Jiao, Licheng

2017-11-01

Discriminative dictionary learning (DDL) framework has been widely used in image classification which aims to learn some class-specific feature vectors as well as a representative dictionary according to a set of labeled training samples. However, interclass similarities and intraclass variances among input samples and learned features will generally weaken the representability of dictionary and the discrimination of feature vectors so as to degrade the classification performance. Therefore, how to explicitly represent them becomes an important issue. In this paper, we present a novel DDL framework with two-level low rank and group sparse decomposition model. In the first level, we learn a class-shared and several class-specific dictionaries, where a low rank and a group sparse regularization are, respectively, imposed on the corresponding feature matrices. In the second level, the class-specific feature matrix will be further decomposed into a low rank and a sparse matrix so that intraclass variances can be separated to concentrate the corresponding feature vectors. Extensive experimental results demonstrate the effectiveness of our model. Compared with the other state-of-the-arts on several popular image databases, our model can achieve a competitive or better performance in terms of the classification accuracy.

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions

PubMed Central

Li, Zuojin; Li, Shengbo Eben; Li, Renjie; Cheng, Bo; Shi, Jinliang

2017-01-01

This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn) features from fixed sliding windows on real-time steering wheel angles time series. After that, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue. PMID:28257094

LACIE large area acreage estimation. [United States of America

NASA Technical Reports Server (NTRS)

Chhikara, R. S.; Feiveson, A. H. (Principal Investigator)

1979-01-01

A sample wheat acreage for a large area is obtained by multiplying its small grains acreage estimate as computed by the classification and mensuration subsystem by the best available ratio of wheat to small grains acreages obtained from historical data. In the United States, as in other countries with detailed historical data, an additional level of aggregation was required because sample allocation was made at the substratum level. The essential features of the estimation procedure for LACIE countries are included along with procedures for estimating wheat acreage in the United States.

Six-State Quantum Key Distribution Using Photons with Orbital Angular Momentum

NASA Astrophysics Data System (ADS)

Li, Jun-Lin; Wang, Chuan

2010-11-01

A new implementation of high-dimensional quantum key distribution (QKD) protocol is discussed. Using three mutual unbiased bases, we present a d-level six-state QKD protocol that exploits the orbital angular momentum with the spatial mode of the light beam. The protocol shows that the feature of a high capacity since keys are encoded using photon modes in d-level Hilbert space. The devices for state preparation and measurement are also discussed. This protocol has high security and the alignment of shared reference frames is not needed between sender and receiver.

Macro-Level Profile of Hawai'i's Schools Participating in the Significant Bilingual Instructional Features Study.

ERIC Educational Resources Information Center

Gavieres, Milagros; And Others

This document contains six chapters: (1) The introduction discusses the State of Hawaii, its residents, and major industries. (2) "Education in Hawaii" presents the state's education plan, Department of Education rules and regulations, and student services. (3) "Bilingual Education Program Data" describes Hawaii's bilingual…

Features of the Information and Communication Technology Application by the Subjects of Special Education

ERIC Educational Resources Information Center

Arhipova, Svetlana Vladimirovna; Sergeeva, Olesya Sergeevna

2015-01-01

The main purpose of this study is to reveal the features of information and communication technologies application by the subjects of education in the conditions of special (correctional) school type VIII, and to identify the level of computer literacy of special education. The study was conducted on the basis of the State Budgetary Special…

Nature of the valence band states in Bi2(Ca, Sr, La)3Cu2O8

NASA Astrophysics Data System (ADS)

Wells, B. O.; Lindberg, P. A. P.; Shen, Z.-X.; Dessau, D. S.; Spicer, W. E.; Lindau, I.; Mitzi, D. B.; Kapitulnik, A.

1990-01-01

We have used photoemission spectroscopy to examine the symmetry of the occupied states of the valence band for the La doped superconductor Bi2(Ca, Sr, La)3Cu2O8. While the oxygen states near the bottom of the 7 eV wide valence band exhibit predominantly O 2pz symmetry, the states at the top of the valence band extending to the Fermi level are found to have primarily O 2px and O 2py character. We have also examined anomalous intensity enhancements in the valence band feature for photon energies near 18 eV. These enhancements, which occur at photon energies ranging from 15.8 to 18.0 eV for the different valence band features, are not consistent with either simple final state effects or direct O2s transitions to unoccupied O2p states.

Valence-band states in Bi2(Ca,Sr,La)3Cu2O8

NASA Astrophysics Data System (ADS)

Wells, B. O.; Lindberg, P. A. P.; Shen, Z.-X.; Dessau, D. S.; Spicer, W. E.; Lindau, I.; Mitzi, D. B.; Kapitulnik, A.

1989-09-01

We have used photoemission spectroscopy to examine the symmetry of the occupied states of the valence band for the La-doped superconductor Bi2(Ca,Sr,La)3Cu2O8. While the oxygen states near the bottom of the 7-eV wide valence band exhibit predominantly O 2pz symmetry, the states at the top of the valence band extending to the Fermi level are found to have primarily O 2px and O 2py character. We have also examined anomalous intensity enhancements in the valence-band features for photon energies near 18 eV. These enhancements, which occur at photon energies ranging from 15.8 to 18.0 eV for the different valence-band features, are not consistent with either simple final-state effects or direct O 2s transitions to unoccupied O 2p states.

Reasons for and challenges of recent increases in teen birth rates: a study of family planning service policies and demographic changes at the state level.

PubMed

Yang, Zhou; Gaydos, Laura M

2010-06-01

After declining for over a decade, the birth rate in the United States for adolescents aged 15-19 years increased by 3% in 2006 and 1% again in 2007. We examined demographic and policy reasons for this trend at state level. With data merged from multiple sources, descriptive analysis was used to detect state-level trends in birth rate and policy changes from 2000 to 2006, and variations in the distribution of teen birth rates, sex education, and family planning service policies, and demographic features across each state in 2006. Regression analysis was then conducted to estimate the effect of several reproductive health policies and demographic features on teen birth rates at the state level. Instrument variable was used to correct possible bias in the regression analysis. Medicaid family planning waivers were found to reduce teen birth rates across all ages and races. Abstinence-only education programs were found to cause an increase in teen birth rates among white and black teens. The increasing Hispanic population is another driving force for high teen birth rates. Both demographic factors and policy changes contributed to the increase in teen birth rates between 2000 and 2006. Future policy and behavioral interventions should focus on promoting and increasing access to contraceptive use. Family planning policies should be crafted to address the special needs of teens from different cultural backgrounds, especially Hispanics. Copyright 2010 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

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

Pratt, S. T.; Chemistry

The photoelectron spectrum following resonant two-photon ionization of 1,4-diazabicyclo[2.2.2]octane (DABCO) was recorded with the laser tuned to the origin band of the S{sub 2}{l_arrow}S{sub 0} transition. The spectrum is consistent with the rapid radiationless transition from the S2 state into high vibrational levels of the S1 state, as proposed by Smith et al. Features in the double-resonance spectrum of Smith et al. that were previously assigned to photoionization of S(-) 3s(-){sup 1}A'{sub 2} vibrational levels populated by a radiationless transition from the S2 state are reinterpreted, with the conclusion that the S(-) state has yet to be observed.

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

USGS Publications Warehouse

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 conterminous United States and compared to topographic metrics. A concurrent processing workflow is implemented using a Linux high-performance computing cluster to simultaneously process multiple subbasins, and thereby complete the work in a fraction of the time required for a single-process environment. In addition, similar metrics are generated for several levels of simplification of the hydrographic features to quantify the effects of simplification over the various landscape conditions. Objectives of this exploratory investigation are to quantify geometric characteristics of linear hydrographic features over the various terrain conditions within the conterminous United States and thereby illuminate relations between stream geomorphological conditions and cartographic representation. The synoptic view of these characteristics over regional watersheds that is afforded through concurrent processing, in conjunction with terrain conditions, may reveal patterns for classifying cartographic stream features into stream geomorphological classes. Furthermore, the synoptic measurement of the amount of change in geometric characteristics caused by the several levels of simplification can enable estimation of tolerance values that appropriately control simplification-induced geometric change of the cartographic features within the various geomorphological classes in the country. Hence, these empirically derived rules or relations could help generate multiscale-representations of features through automated generalization that adequately maintain surface drainage variations and patterns reflective of the natural stream geomorphological conditions across the country.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity.

PubMed

Ravishankar, Hariharan; Madhavan, Radhika; Mullick, Rakesh; Shetty, Teena; Marinelli, Luca; Joel, Suresh E

2016-08-01

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

6 CFR 37.15 - Physical security features for the driver's license or identification card.

Code of Federal Regulations, 2012 CFR

2012-01-01

.... (3) Level 3. Inspection by forensic specialists. (d) Document security and integrity. States must... independent laboratory experienced with adversarial analysis of identification documents concerning one or...

6 CFR 37.15 - Physical security features for the driver's license or identification card.

Code of Federal Regulations, 2014 CFR

2014-01-01

.... (3) Level 3. Inspection by forensic specialists. (d) Document security and integrity. States must... independent laboratory experienced with adversarial analysis of identification documents concerning one or...

6 CFR 37.15 - Physical security features for the driver's license or identification card.

Code of Federal Regulations, 2013 CFR

2013-01-01

.... (3) Level 3. Inspection by forensic specialists. (d) Document security and integrity. States must... independent laboratory experienced with adversarial analysis of identification documents concerning one or...

6 CFR 37.15 - Physical security features for the driver's license or identification card.

Code of Federal Regulations, 2011 CFR

2011-01-01

.... (3) Level 3. Inspection by forensic specialists. (d) Document security and integrity. States must... independent laboratory experienced with adversarial analysis of identification documents concerning one or...

S. 1111: This Act may be cited as the Radiation Protection Act of 1991, introduced in the US Senate, One Hundred Second Congress, First Session, May 21, 1991

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

Not Available

1991-01-01

This bill was introduced into the US Senate on May 21, 1991 to protect the public from health risks from radiation exposure from low-level radioactive waste. Key features of this legislation which are discussed include the following: state authority for fees or charges for waste storage and disposal facilities; and state authority to regulate disposal of low-level radioactive waste.

Organization of excitable dynamics in hierarchical biological networks.

PubMed

Müller-Linow, Mark; Hilgetag, Claus C; Hütt, Marc-Thorsten

2008-09-26

This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

A Novel Image Recuperation Approach for Diagnosing and Ranking Retinopathy Disease Level Using Diabetic Fundus Image

PubMed Central

2015-01-01

Retinal fundus images are widely used in diagnosing and providing treatment for several eye diseases. Prior works using retinal fundus images detected the presence of exudation with the aid of publicly available dataset using extensive segmentation process. Though it was proved to be computationally efficient, it failed to create a diabetic retinopathy feature selection system for transparently diagnosing the disease state. Also the diagnosis of diseases did not employ machine learning methods to categorize candidate fundus images into true positive and true negative ratio. Several candidate fundus images did not include more detailed feature selection technique for diabetic retinopathy. To apply machine learning methods and classify the candidate fundus images on the basis of sliding window a method called, Diabetic Fundus Image Recuperation (DFIR) is designed in this paper. The initial phase of DFIR method select the feature of optic cup in digital retinal fundus images based on Sliding Window Approach. With this, the disease state for diabetic retinopathy is assessed. The feature selection in DFIR method uses collection of sliding windows to obtain the features based on the histogram value. The histogram based feature selection with the aid of Group Sparsity Non-overlapping function provides more detailed information of features. Using Support Vector Model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy diseases. The ranking of disease level for each candidate set provides a much promising result for developing practically automated diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, specificity rate, ranking efficiency and feature selection time. PMID:25974230

Supporting the Growing Needs of the GIS Industry

NASA Technical Reports Server (NTRS)

2003-01-01

Visual Learning Systems, Inc. (VLS), of Missoula, Montana, has developed a commercial software application called Feature Analyst. Feature Analyst was conceived under a Small Business Innovation Research (SBIR) contract with NASA's Stennis Space Center, and through the Montana State University TechLink Center, an organization funded by NASA and the U.S. Department of Defense to link regional companies with Federal laboratories for joint research and technology transfer. The software provides a paradigm shift to automated feature extraction, as it utilizes spectral, spatial, temporal, and ancillary information to model the feature extraction process; presents the ability to remove clutter; incorporates advanced machine learning techniques to supply unparalleled levels of accuracy; and includes an exceedingly simple interface for feature extraction.

Character-level neural network for biomedical named entity recognition.

PubMed

Gridach, Mourad

2017-06-01

Biomedical named entity recognition (BNER), which extracts important named entities such as genes and proteins, is a challenging task in automated systems that mine knowledge in biomedical texts. The previous state-of-the-art systems required large amounts of task-specific knowledge in the form of feature engineering, lexicons and data pre-processing to achieve high performance. In this paper, we introduce a novel neural network architecture that benefits from both word- and character-level representations automatically, by using a combination of bidirectional long short-term memory (LSTM) and conditional random field (CRF) eliminating the need for most feature engineering tasks. We evaluate our system on two datasets: JNLPBA corpus and the BioCreAtIvE II Gene Mention (GM) corpus. We obtained state-of-the-art performance by outperforming the previous systems. To the best of our knowledge, we are the first to investigate the combination of deep neural networks, CRF, word embeddings and character-level representation in recognizing biomedical named entities. Copyright © 2017 Elsevier Inc. All rights reserved.

Corresponding-states behavior of SPC/E-based modified (bent and hybrid) water models

NASA Astrophysics Data System (ADS)

Weiss, Volker C.

2017-02-01

The remarkable and sometimes anomalous properties of water can be traced back at the molecular level to the tetrahedral coordination of molecules due to the ability of a water molecule to form four hydrogen bonds to its neighbors; this feature allows for the formation of a network that greatly influences the thermodynamic behavior. Computer simulations are becoming increasingly important for our understanding of water. Molecular models of water, such as SPC/E, are needed for this purpose, and they have proved to capture many important features of real water. Modifications of the SPC/E model have been proposed, some changing the H-O-H angle (bent models) and others increasing the importance of dispersion interactions (hybrid models), to study the structural features that set water apart from other polar fluids and from simple fluids such as argon. Here, we focus on the properties at liquid-vapor equilibrium and study the coexistence curve, the interfacial tension, and the vapor pressure in a corresponding-states approach. In particular, we calculate Guggenheim's ratio for the reduced apparent enthalpy of vaporization and Guldberg's ratio for the reduced normal boiling point. This analysis offers additional insight from a more macroscopic, thermodynamic perspective and augments that which has already been learned at the molecular level from simulations. In the hybrid models, the relative importance of dispersion interactions is increased, which turns the modified water into a Lennard-Jones-like fluid. Consequently, in a corresponding-states framework, the typical behavior of simple fluids, such as argon, is seen to be approached asymptotically. For the bent models, decreasing the bond angle turns the model essentially into a polar diatomic fluid in which the particles form linear molecular arrangements; as a consequence, characteristic features of the corresponding-states behavior of hydrogen halides emerge.

Distributive Marketing Education: Innovative Instructional Techniques in Distributive Marketing Education.

ERIC Educational Resources Information Center

Office of Education (DHEW), Washington, DC.

The conference featured more than 40 presentations representing existing and planned innovative programs in all levels of distributive marketing education in six States. In addition to the presentations (not reproduced in their entirety in the report), there were sessions and workshops for secondary, post secondary, and adult levels and for city…

Electrically controlled crossing of energy levels in quantum dots in two-dimensional topological insulators

NASA Astrophysics Data System (ADS)

Sukhanov, Aleksei A.

2017-05-01

We study the energy spectra of bound states in quantum dots (QDs) formed by an electrostatic potential in two-dimensional topological insulator (TI) and their transformation with changes in QD depth and radius. It is found that, unlike a trivial insulator, the energy difference between the levels of the ground state and first excited state can decrease with decreasing the radius and increasing the depth of the QD so that these levels intersect under some critical condition. The crossing of the levels results in unusual features of optical properties caused by intraceneter electron transitions. In particular, it leads to significant changes of light absorption due to electron transitions between such levels and to the transient electroluminescence induced by electrical tuning of QD and TI parameters. In the case of magnetic TIs, the polarization direction of the absorbed or emitted circularly polarized light is changed due to the level crossing.

Weighted score-level feature fusion based on Dempster-Shafer evidence theory for action recognition

NASA Astrophysics Data System (ADS)

Zhang, Guoliang; Jia, Songmin; Li, Xiuzhi; Zhang, Xiangyin

2018-01-01

The majority of human action recognition methods use multifeature fusion strategy to improve the classification performance, where the contribution of different features for specific action has not been paid enough attention. We present an extendible and universal weighted score-level feature fusion method using the Dempster-Shafer (DS) evidence theory based on the pipeline of bag-of-visual-words. First, the partially distinctive samples in the training set are selected to construct the validation set. Then, local spatiotemporal features and pose features are extracted from these samples to obtain evidence information. The DS evidence theory and the proposed rule of survival of the fittest are employed to achieve evidence combination and calculate optimal weight vectors of every feature type belonging to each action class. Finally, the recognition results are deduced via the weighted summation strategy. The performance of the established recognition framework is evaluated on Penn Action dataset and a subset of the joint-annotated human metabolome database (sub-JHMDB). The experiment results demonstrate that the proposed feature fusion method can adequately exploit the complementarity among multiple features and improve upon most of the state-of-the-art algorithms on Penn Action and sub-JHMDB datasets.

The National Hydrography Dataset

USGS Publications Warehouse

,

1999-01-01

The National Hydrography Dataset (NHD) is a newly combined dataset that provides hydrographic data for the United States. The NHD is the culmination of recent cooperative efforts of the U.S. Environmental Protection Agency (USEPA) and the U.S. Geological Survey (USGS). It combines elements of USGS digital line graph (DLG) hydrography files and the USEPA Reach File (RF3). The NHD supersedes RF3 and DLG files by incorporating them, not by replacing them. Users of RF3 or DLG files will find the same data in a new, more flexible format. They will find that the NHD is familiar but greatly expanded and refined. The DLG files contribute a national coverage of millions of features, including water bodies such as lakes and ponds, linear water features such as streams and rivers, and also point features such as springs and wells. These files provide standardized feature types, delineation, and spatial accuracy. From RF3, the NHD acquires hydrographic sequencing, upstream and downstream navigation for modeling applications, and reach codes. The reach codes provide a way to integrate data from organizations at all levels by linking the data to this nationally consistent hydrographic network. The feature names are from the Geographic Names Information System (GNIS). The NHD provides comprehensive coverage of hydrographic data for the United States. Some of the anticipated end-user applications of the NHD are multiuse hydrographic modeling and water-quality studies of fish habitats. Although based on 1:100,000-scale data, the NHD is planned so that it can incorporate and encourage the development of the higher resolution data that many users require. The NHD can be used to promote the exchange of data between users at the national, State, and local levels. Many users will benefit from the NHD and will want to contribute to the dataset as well.

Memory Meets Control in Hippocampal and Striatal Binding of Stimuli, Responses, and Attentional Control States

PubMed Central

Brashier, Nadia M.

2015-01-01

The human brain encodes experience in an integrative fashion by binding together the various features of an event (i.e., stimuli and responses) into memory “event files.” A subsequent reoccurrence of an event feature can then cue the retrieval of the memory file to “prime” cognition and action. Intriguingly, recent behavioral studies indicate that, in addition to linking concrete stimulus and response features, event coding may also incorporate more abstract, “internal” event features such as attentional control states. In the present study, we used fMRI in healthy human volunteers to determine the neural mechanisms supporting this type of holistic event binding. Specifically, we combined fMRI with a task protocol that dissociated the expression of event feature-binding effects pertaining to concrete stimulus and response features, stimulus categories, and attentional control demands. Using multivariate neural pattern classification, we show that the hippocampus and putamen integrate event attributes across all of these levels in conjunction with other regions representing concrete-feature-selective (primarily visual cortex), category-selective (posterior frontal cortex), and control demand-selective (insula, caudate, anterior cingulate, and parietal cortex) event information. Together, these results suggest that the hippocampus and putamen are involved in binding together holistic event memories that link physical stimulus and response characteristics with internal representations of stimulus categories and attentional control states. These bindings then presumably afford shortcuts to adaptive information processing and response selection in the face of recurring events. SIGNIFICANCE STATEMENT Memory binds together the different features of our experience, such as an observed stimulus and concurrent motor responses, into so-called event files. Recent behavioral studies suggest that the observer's internal attentional state might also become integrated into the event memory. Here, we used fMRI to determine the brain areas responsible for binding together event information pertaining to concrete stimulus and response features, stimulus categories, and internal attentional control states. We found that neural signals in the hippocampus and putamen contained information about all of these event attributes and could predict behavioral priming effects stemming from these features. Therefore, medial temporal lobe and dorsal striatum structures appear to be involved in binding internal control states to event memories. PMID:26538657

Disease named entity recognition from biomedical literature using a novel convolutional neural network.

PubMed

Zhao, Zhehuan; Yang, Zhihao; Luo, Ling; Wang, Lei; Zhang, Yin; Lin, Hongfei; Wang, Jian

2017-12-28

Automatic disease named entity recognition (DNER) is of utmost importance for development of more sophisticated BioNLP tools. However, most conventional CRF based DNER systems rely on well-designed features whose selection is labor intensive and time-consuming. Though most deep learning methods can solve NER problems with little feature engineering, they employ additional CRF layer to capture the correlation information between labels in neighborhoods which makes them much complicated. In this paper, we propose a novel multiple label convolutional neural network (MCNN) based disease NER approach. In this approach, instead of the CRF layer, a multiple label strategy (MLS) first introduced by us, is employed. First, the character-level embedding, word-level embedding and lexicon feature embedding are concatenated. Then several convolutional layers are stacked over the concatenated embedding. Finally, MLS strategy is applied to the output layer to capture the correlation information between neighboring labels. As shown by the experimental results, MCNN can achieve the state-of-the-art performance on both NCBI and CDR corpora. The proposed MCNN based disease NER method achieves the state-of-the-art performance with little feature engineering. And the experimental results show the MLS strategy's effectiveness of capturing the correlation information between labels in the neighborhood.

Automatic building extraction from LiDAR data fusion of point and grid-based features

NASA Astrophysics Data System (ADS)

Du, Shouji; Zhang, Yunsheng; Zou, Zhengrong; Xu, Shenghua; He, Xue; Chen, Siyang

2017-08-01

This paper proposes a method for extracting buildings from LiDAR point cloud data by combining point-based and grid-based features. To accurately discriminate buildings from vegetation, a point feature based on the variance of normal vectors is proposed. For a robust building extraction, a graph cuts algorithm is employed to combine the used features and consider the neighbor contexture information. As grid feature computing and a graph cuts algorithm are performed on a grid structure, a feature-retained DSM interpolation method is proposed in this paper. The proposed method is validated by the benchmark ISPRS Test Project on Urban Classification and 3D Building Reconstruction and compared to the state-art-of-the methods. The evaluation shows that the proposed method can obtain a promising result both at area-level and at object-level. The method is further applied to the entire ISPRS dataset and to a real dataset of the Wuhan City. The results show a completeness of 94.9% and a correctness of 92.2% at the per-area level for the former dataset and a completeness of 94.4% and a correctness of 95.8% for the latter one. The proposed method has a good potential for large-size LiDAR data.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

PubMed

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Association of blood lead level with neurological features in 972 children affected by an acute severe lead poisoning outbreak in Zamfara State, northern Nigeria.

PubMed

Greig, Jane; Thurtle, Natalie; Cooney, Lauren; Ariti, Cono; Ahmed, Abdulkadir Ola; Ashagre, Teshome; Ayela, Anthony; Chukwumalu, Kingsley; Criado-Perez, Alison; Gómez-Restrepo, Camilo; Meredith, Caitlin; Neri, Antonio; Stellmach, Darryl; Sani-Gwarzo, Nasir; Nasidi, Abdulsalami; Shanks, Leslie; Dargan, Paul I

2014-01-01

In 2010, Médecins Sans Frontières (MSF) investigated reports of high mortality in young children in Zamfara State, Nigeria, leading to confirmation of villages with widespread acute severe lead poisoning. In a retrospective analysis, we aimed to determine venous blood lead level (VBLL) thresholds and risk factors for encephalopathy using MSF programmatic data from the first year of the outbreak response. We included children aged ≤5 years with VBLL ≥45 µg/dL before any chelation and recorded neurological status. Odds ratios (OR) for neurological features were estimated; the final model was adjusted for age and baseline VBLL, using random effects for village of residence. 972 children met inclusion criteria: 885 (91%) had no neurological features; 34 (4%) had severe features; 47 (5%) had reported recent seizures; and six (1%) had other neurological abnormalities. The geometric mean VBLLs for all groups with neurological features were >100 µg/dL vs 65.9 µg/dL for those without neurological features. The adjusted OR for neurological features increased with increasing VBLL: from 2.75, 95%CI 1.27-5.98 (80-99.9 µg/dL) to 22.95, 95%CI 10.54-49.96 (≥120 µg/dL). Neurological features were associated with younger age (OR 4.77 [95% CI 2.50-9.11] for 1-<2 years and 2.69 [95%CI 1.15-6.26] for 2-<3 years, both vs 3-5 years). Severe neurological features were seen at VBLL <105 µg/dL only in those with malaria. Increasing VBLL (from ≥80 µg/dL) and age 1-<3 years were strongly associated with neurological features; in those tested for malaria, a positive test was also strongly associated. These factors will help clinicians managing children with lead poisoning in prioritising therapy and developing chelation protocols.

The Emotion Recognition System Based on Autoregressive Model and Sequential Forward Feature Selection of Electroencephalogram Signals

PubMed Central

Hatamikia, Sepideh; Maghooli, Keivan; Nasrabadi, Ali Motie

2014-01-01

Electroencephalogram (EEG) is one of the useful biological signals to distinguish different brain diseases and mental states. In recent years, detecting different emotional states from biological signals has been merged more attention by researchers and several feature extraction methods and classifiers are suggested to recognize emotions from EEG signals. In this research, we introduce an emotion recognition system using autoregressive (AR) model, sequential forward feature selection (SFS) and K-nearest neighbor (KNN) classifier using EEG signals during emotional audio-visual inductions. The main purpose of this paper is to investigate the performance of AR features in the classification of emotional states. To achieve this goal, a distinguished AR method (Burg's method) based on Levinson-Durbin's recursive algorithm is used and AR coefficients are extracted as feature vectors. In the next step, two different feature selection methods based on SFS algorithm and Davies–Bouldin index are used in order to decrease the complexity of computing and redundancy of features; then, three different classifiers include KNN, quadratic discriminant analysis and linear discriminant analysis are used to discriminate two and three different classes of valence and arousal levels. The proposed method is evaluated with EEG signals of available database for emotion analysis using physiological signals, which are recorded from 32 participants during 40 1 min audio visual inductions. According to the results, AR features are efficient to recognize emotional states from EEG signals, and KNN performs better than two other classifiers in discriminating of both two and three valence/arousal classes. The results also show that SFS method improves accuracies by almost 10-15% as compared to Davies–Bouldin based feature selection. The best accuracies are %72.33 and %74.20 for two classes of valence and arousal and %61.10 and %65.16 for three classes, respectively. PMID:25298928

Restructuring consciousness -the psychedelic state in light of integrated information theory.

PubMed

Gallimore, Andrew R

2015-01-01

The psychological state elicited by the classic psychedelics drugs, such as LSD and psilocybin, is one of the most fascinating and yet least understood states of consciousness. However, with the advent of modern functional neuroimaging techniques, the effect of these drugs on neural activity is now being revealed, although many of the varied phenomenological features of the psychedelic state remain challenging to explain. Integrated information theory (IIT) is one of the foremost contemporary theories of consciousness, providing a mathematical formalization of both the quantity and quality of conscious experience. This theory can be applied to all known states of consciousness, including the psychedelic state. Using the results of functional neuroimaging data on the psychedelic state, the effects of psychedelic drugs on both the level and structure of consciousness can be explained in terms of the conceptual framework of IIT. This new IIT-based model of the psychedelic state provides an explanation for many of its phenomenological features, including unconstrained cognition, alterations in the structure and meaning of concepts and a sense of expanded awareness. This model also suggests that whilst cognitive flexibility, creativity, and imagination are enhanced during the psychedelic state, this occurs at the expense of cause-effect information, as well as degrading the brain's ability to organize, categorize, and differentiate the constituents of conscious experience. Furthermore, the model generates specific predictions that can be tested using a combination of functional imaging techniques, as has been applied to the study of levels of consciousness during anesthesia and following brain injury.

Design principles of paradoxical signaling in the immune system

NASA Astrophysics Data System (ADS)

Hart, Yuval

A widespread feature of cell-cell signaling systems is paradoxical pleiotropy: the same secreted signaling molecule can induce opposite effects in the responding cells. For example, the cytokine IL-2 can promote proliferation and death of T-cells. The role of such paradoxical signaling remains unclear. We suggest that this mechanism provides homeostatic concentration of cells, independent of initial conditions. The crux of the paradoxical mechanism is the combination of a positive and a negative feedback loops creating two stable states - an OFF state and an ON state. Experimentally, we found that CD4 + cells grown in culture with a 30-fold difference in initial concentrations reached a homeostatic concentration nearly independent of initial cell levels (ON-state). Below an initial threshold, cell density decayed to extinction (OFF-state). Mathematical modeling explained the observed cell and cytokine dynamics and predicted conditions that shifted cell fate from homeostasis to the OFF-state. We suggest that paradoxical signaling provides cell circuits with specific dynamical features that are robust to environmental perturbations.

A new Gaussian MCTDH program: Implementation and validation on the levels of the water and glycine molecules

NASA Astrophysics Data System (ADS)

Skouteris, D.; Barone, V.

2014-06-01

We report the main features of a new general implementation of the Gaussian Multi-Configuration Time-Dependent Hartree model. The code allows effective computations of time-dependent phenomena, including calculation of vibronic spectra (in one or more electronic states), relative state populations, etc. Moreover, by expressing the Dirac-Frenkel variational principle in terms of an effective Hamiltonian, we are able to provide a new reliable estimate of the representation error. After validating the code on simple one-dimensional systems, we analyze the harmonic and anharmonic vibrational spectra of water and glycine showing that reliable and converged energy levels can be obtained with reasonable computing resources. The data obtained on water and glycine are compared with results of previous calculations using the vibrational second-order perturbation theory method. Additional features and perspectives are also shortly discussed.

Automated EEG sleep staging in the term-age baby using a generative modelling approach.

PubMed

Pillay, Kirubin; Dereymaeker, Anneleen; Jansen, Katrien; Naulaers, Gunnar; Van Huffel, Sabine; De Vos, Maarten

2018-06-01

We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification. EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording's feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen's kappa agreement calculated between the estimates and clinicians' visual labels. For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively. This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.

Automated EEG sleep staging in the term-age baby using a generative modelling approach

NASA Astrophysics Data System (ADS)

Pillay, Kirubin; Dereymaeker, Anneleen; Jansen, Katrien; Naulaers, Gunnar; Van Huffel, Sabine; De Vos, Maarten

2018-06-01

Objective. We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification. Approach. EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording’s feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen’s kappa agreement calculated between the estimates and clinicians’ visual labels. Main results. For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively. Significance. This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.

The Japanese Labor Market in a Comparative Perspective with the United States. A Transaction-Cost Interpretation.

ERIC Educational Resources Information Center

Hashimoto, Masanori

A comparison is made of some of the notable features of the Japanese and U.S. labor markets. In Japan, as compared to the United States, for example, levels of employment tenure are higher, employer-employee attachment stronger, earnings-tenure profiles more steeply sloped, layoffs and dismissals much less frequent, and joint consultation and…

Concept cells through associative learning of high-level representations.

PubMed

Reddy, Leila; Thorpe, Simon J

2014-10-22

In this issue of Neuron, Quian Quiroga et al. (2014) show that neurons in the human medial temporal lobe (MTL) follow subjects' perceptual states rather than the features of the visual input. Patients with MTL damage however have intact perceptual abilities but suffer instead from extreme forgetfulness. Thus, the reported MTL neurons could create new memories of the current perceptual state.

Spatially Regularized Machine Learning for Task and Resting-state fMRI

PubMed Central

Song, Xiaomu; Panych, Lawrence P.; Chen, Nan-kuei

2015-01-01

Background Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades. New Method A spatially regularized support vector machine (SVM) technique was developed for the reliable brain mapping in task- and resting-state. Unlike most existing SVM-based brain mapping techniques, which implement supervised classifications of specific brain functional states or disorders, the proposed method performs a semi-supervised classification for the general brain function mapping where spatial correlation of fMRI is integrated into the SVM learning. The method can adapt to intra- and inter-subject variations induced by fMRI nonstationarity, and identify a true boundary between active and inactive voxels, or between functionally connected and unconnected voxels in a feature space. Results The method was evaluated using synthetic and experimental data at the individual and group level. Multiple features were evaluated in terms of their contributions to the spatially regularized SVM learning. Reliable mapping results in both task- and resting-state were obtained from individual subjects and at the group level. Comparison with Existing Methods A comparison study was performed with independent component analysis, general linear model, and correlation analysis methods. Experimental results indicate that the proposed method can provide a better or comparable mapping performance at the individual and group level. Conclusions The proposed method can provide accurate and reliable mapping of brain function in task- and resting-state, and is applicable to a variety of quantitative fMRI studies. PMID:26470627

Spontaneous emission near the edge of a photonic band gap

NASA Astrophysics Data System (ADS)

John, Sajeev; Quang, Tran

1994-08-01

The spectral and dynamical features of spontaneous emission from two and three-level atoms in which one transition frequency lay near the edge of a photonic band gap (PBG) were derived. These features included temporal oscillations, fractionalized steady-state atomic population on the excited state, spectral splitting and subnatural bandwidth. The effect of N-1 unexcited atoms were also taken into account. The direct consequences of photon localization as embodied in the photon-atom bound state were observed. One feasible experimental accomplishment of these effects may ensue from laser-cooled atoms in the void regions of a PBG medium. Another option is the application of an organic impurity molecule such as pentacene. Such molecules were known to show extremely narrow linewidths when placed in fitting solid hosts.

Core signalling motif displaying multistability through multi-state enzymes.

PubMed

Feng, Song; Sáez, Meritxell; Wiuf, Carsten; Feliu, Elisenda; Soyer, Orkun S

2016-10-01

Bistability, and more generally multistability, is a key system dynamics feature enabling decision-making and memory in cells. Deciphering the molecular determinants of multistability is thus crucial for a better understanding of cellular pathways and their (re)engineering in synthetic biology. Here, we show that a key motif found predominantly in eukaryotic signalling systems, namely a futile signalling cycle, can display bistability when featuring a two-state kinase. We provide necessary and sufficient mathematical conditions on the kinetic parameters of this motif that guarantee the existence of multiple steady states. These conditions foster the intuition that bistability arises as a consequence of competition between the two states of the kinase. Extending from this result, we find that increasing the number of kinase states linearly translates into an increase in the number of steady states in the system. These findings reveal, to our knowledge, a new mechanism for the generation of bistability and multistability in cellular signalling systems. Further the futile cycle featuring a two-state kinase is among the smallest bistable signalling motifs. We show that multi-state kinases and the described competition-based motif are part of several natural signalling systems and thereby could enable them to implement complex information processing through multistability. These results indicate that multi-state kinases in signalling systems are readily exploited by natural evolution and could equally be used by synthetic approaches for the generation of multistable information processing systems at the cellular level. © 2016 The Authors.

Phase diagram of congested traffic flow: An empirical study

PubMed

Lee; Lee; Kim

2000-10-01

We analyze traffic data from a highway section containing one effective on-ramp. Based on two criteria, local velocity variation patterns and expansion (or nonexpansion) of congested regions, three distinct congested traffic states are identified. These states appear at different levels of the upstream flux and the on-ramp flux, thereby generating a phase digram of the congested traffic flow. Observed traffic states are compared with recent theoretical analyses and both agreeing and disagreeing features are found.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

Visual Saliency Detection Based on Multiscale Deep CNN Features.

PubMed

Guanbin Li; Yizhou Yu

2016-11-01

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving the state-of-the-art performance on all public benchmarks, improving the F-measure by 6.12% and 10%, respectively, on the DUT-OMRON data set and our new data set (HKU-IS), and lowering the mean absolute error by 9% and 35.3%, respectively, on these two data sets.

Regenerative fuel cell systems for space station

NASA Technical Reports Server (NTRS)

Hoberecht, M. A.; Sheibley, D. W.

1985-01-01

Regenerative fuel cell (RFC) systems are the leading energy storage candidates for Space Station. Key design features are the advanced state of technology readiness and high degree of system level design flexibility. Technology readiness was demonstrated through testing at the single cell, cell stack, mechanical ancillary component, subsystem, and breadboard levels. Design flexibility characteristics include independent sizing of power and energy storage portions of the system, integration of common reactants with other space station systems, and a wide range of various maintenance approaches. The design features led to selection of a RFC system as the sole electrochemical energy storage technology option for the space station advanced development program.

Level Anticrossing of Impurity States in Semiconductor Nanocrystals

PubMed Central

Baimuratov, Anvar S.; Rukhlenko, Ivan D.; Turkov, Vadim K.; Ponomareva, Irina O.; Leonov, Mikhail Yu.; Perova, Tatiana S.; Berwick, Kevin; Baranov, Alexander V.; Fedorov, Anatoly V.

2014-01-01

The size dependence of the quantized energies of elementary excitations is an essential feature of quantum nanostructures, underlying most of their applications in science and technology. Here we report on a fundamental property of impurity states in semiconductor nanocrystals that appears to have been overlooked—the anticrossing of energy levels exhibiting different size dependencies. We show that this property is inherent to the energy spectra of charge carriers whose spatial motion is simultaneously affected by the Coulomb potential of the impurity ion and the confining potential of the nanocrystal. The coupling of impurity states, which leads to the anticrossing, can be induced by interactions with elementary excitations residing inside the nanocrystal or an external electromagnetic field. We formulate physical conditions that allow a straightforward interpretation of level anticrossings in the nanocrystal energy spectrum and an accurate estimation of the states' coupling strength. PMID:25369911

Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation.

PubMed

Al-Shaikhli, Saif Dawood Salman; Yang, Michael Ying; Rosenhahn, Bodo

2016-12-01

This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation. The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary. The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy. In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

[Rocky Mountain spotted fever in children: clinical and epidemiological features].

PubMed

Martínez-Medina, Miguel Angel; Alvarez-Hernández, Gerardo; Padilla-Zamudioa, José Guillermo; Rojas-Guerra, Maria Guadalupe

2007-01-01

To report the clinical features of the Rocky Mountain spotted fever (RMSF) in children of southern Sonora, Mexico. Nine cases were studied at the Sonora State Children's Hospital. One case was defined by clinical features and positive serological tests (indirect immunofluorescence assay or reaction to Proteus OX 19). Demographic and clinical characteristics of the patients were registered. The study subjects were children from two to twelve years ofage. All patients have had contact with tick-infested dogs and had fever, as well as petechial rash. Laboratory findings included high levels of hepatic aminotransferase, hyponatremia and thrombocytopenia. Therapy with chloramphenicol and doxyciclyne was administered after the first seven days of the onset of illness. The mortality rate was 22%. This study supports the presence of RMSF in the state of Sonora, Mexico, which should be considered as a public health hazard, requiring immediate actions for prevention and control.

Eyes Matched to the Prize: The State of Matched Filters in Insect Visual Circuits.

PubMed

Kohn, Jessica R; Heath, Sarah L; Behnia, Rudy

2018-01-01

Confronted with an ever-changing visual landscape, animals must be able to detect relevant stimuli and translate this information into behavioral output. A visual scene contains an abundance of information: to interpret the entirety of it would be uneconomical. To optimally perform this task, neural mechanisms exist to enhance the detection of important features of the sensory environment while simultaneously filtering out irrelevant information. This can be accomplished by using a circuit design that implements specific "matched filters" that are tuned to relevant stimuli. Following this rule, the well-characterized visual systems of insects have evolved to streamline feature extraction on both a structural and functional level. Here, we review examples of specialized visual microcircuits for vital behaviors across insect species, including feature detection, escape, and estimation of self-motion. Additionally, we discuss how these microcircuits are modulated to weigh relevant input with respect to different internal and behavioral states.

Restructuring consciousness –the psychedelic state in light of integrated information theory

PubMed Central

Gallimore, Andrew R.

2015-01-01

The psychological state elicited by the classic psychedelics drugs, such as LSD and psilocybin, is one of the most fascinating and yet least understood states of consciousness. However, with the advent of modern functional neuroimaging techniques, the effect of these drugs on neural activity is now being revealed, although many of the varied phenomenological features of the psychedelic state remain challenging to explain. Integrated information theory (IIT) is one of the foremost contemporary theories of consciousness, providing a mathematical formalization of both the quantity and quality of conscious experience. This theory can be applied to all known states of consciousness, including the psychedelic state. Using the results of functional neuroimaging data on the psychedelic state, the effects of psychedelic drugs on both the level and structure of consciousness can be explained in terms of the conceptual framework of IIT. This new IIT-based model of the psychedelic state provides an explanation for many of its phenomenological features, including unconstrained cognition, alterations in the structure and meaning of concepts and a sense of expanded awareness. This model also suggests that whilst cognitive flexibility, creativity, and imagination are enhanced during the psychedelic state, this occurs at the expense of cause-effect information, as well as degrading the brain's ability to organize, categorize, and differentiate the constituents of conscious experience. Furthermore, the model generates specific predictions that can be tested using a combination of functional imaging techniques, as has been applied to the study of levels of consciousness during anesthesia and following brain injury. PMID:26124719

Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm.

PubMed

Khushaba, Rami N; Kodagoda, Sarath; Lal, Sara; Dissanayake, Gamini

2011-01-01

Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of electroencephalogram (EEG), electrooculogram (EOG), and electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual-information (MI)- based wavelet packet transform (FMIWPT) feature-extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required MI using a novel approach based on fuzzy memberships providing an accurate-information content-estimation measure. The quality of the extracted features was assessed on datasets collected from 31 drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-- 97% on an average across all subjects.

Level crossing analysis of chemically induced dynamic nuclear polarization: Towards a common description of liquid-state and solid-state cases

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

Sosnovsky, Denis V.; Ivanov, Konstantin L., E-mail: ivanov@tomo.nsc.ru; Novosibirsk State University, Pirogova 2, 630090, Novosibirsk

Chemically Induced Dynamic Nuclear Polarization (CIDNP) is an efficient method of creating non-equilibrium polarization of nuclear spins by using chemical reactions, which have radical pairs as intermediates. The CIDNP effect originates from (i) electron spin-selective recombination of radical pairs and (ii) the dependence of the inter-system crossing rate in radical pairs on the state of magnetic nuclei. The CIDNP effect can be investigated by using Nuclear Magnetic Resonance (NMR) methods. The gain from CIDNP is then two-fold: it allows one to obtain considerable amplification of NMR signals; in addition, it provides a very useful tool for investigating elusive radicals andmore » radical pairs. While the mechanisms of the CIDNP effect in liquids are well established and understood, detailed analysis of solid-state CIDNP mechanisms still remains challenging; likewise a common theoretical frame for the description of CIDNP in both solids and liquids is missing. Difficulties in understanding the spin dynamics that lead to the CIDNP effect in the solid-state case are caused by the anisotropy of spin interactions, which increase the complexity of spin evolution. In this work, we propose to analyze CIDNP in terms of level crossing phenomena, namely, to attribute features in the CIDNP magnetic field dependence to Level Crossings (LCs) and Level Anti-Crossings (LACs) in a radical pair. This approach allows one to describe liquid-state CIDNP; the same holds for the solid-state case where anisotropic interactions play a significant role in CIDNP formation. In solids, features arise predominantly from LACs, since in most cases anisotropic couplings result in perturbations, which turn LCs into LACs. We have interpreted the CIDNP mechanisms in terms of the LC/LAC concept. This consideration allows one to find analytical expressions for a wide magnetic field range, where several different mechanisms are operative; furthermore, the LAC description gives a way to determine CIDNP sign rules. Thus, LCs/LACs provide a consistent description of CIDNP in both liquids and solids with the prospect of exploiting it for the analysis of short-lived radicals and for optimizing the polarization level.« less

Triaxiality of neutron-rich 84,86,88Ge from low-energy nuclear spectra

NASA Astrophysics Data System (ADS)

Lettmann, M.; Werner, V.; Pietralla, N.; Doornenbal, P.; Obertelli, A.; Rodríguez, T. R.; Sieja, K.; Authelet, G.; Baba, H.; Calvet, D.; Château, F.; Chen, S.; Corsi, A.; Delbart, A.; Gheller, J.-M.; Giganon, A.; Gillibert, A.; Lapoux, V.; Motobayashi, T.; Niikura, M.; Paul, N.; Roussé, J.-Y.; Sakurai, H.; Santamaria, C.; Steppenbeck, D.; Taniuchi, R.; Uesaka, T.; Ando, T.; Arici, T.; Blazhev, A.; Browne, F.; Bruce, A.; Caroll, R. J.; Chung, L. X.; Cortés, M. L.; Dewald, M.; Ding, B.; Flavigny, F.; Franchoo, S.; Górska, M.; Gottardo, A.; Jungclaus, A.; Lee, J.; Linh, B. D.; Liu, J.; Liu, Z.; Lizarazo, C.; Momiyama, S.; Moschner, K.; Nagamine, S.; Nakatsuka, N.; Nita, C.; Nobs, C. R.; Olivier, L.; Patel, Z.; Podolyák, Zs.; Rudigier, M.; Saito, T.; Shand, C.; Söderström, P.-A.; Stefan, I.; Vaquero, V.; Wimmer, K.; Xu, Z.

2017-07-01

γ -ray transitions between low-spin states of the neutron-rich 84,86,88Ge were measured by means of in-flight γ -ray spectroscopy at 270 MeV/u. Excited 61+,41,2 + , and 21,2 + states of Ge,8684 and 41+ and 21,2 + states of 88Ge were observed. Furthermore, a candidate for a 31+ state of 86Ge was identified. This state plays a key role in the discussion of ground-state triaxiality of 86Ge, along with other features of its low-energy level scheme. A new region of triaxially deformed nuclei is proposed in the Ge isotopic chain.

Association of Blood Lead Level with Neurological Features in 972 Children Affected by an Acute Severe Lead Poisoning Outbreak in Zamfara State, Northern Nigeria

PubMed Central

Greig, Jane; Thurtle, Natalie; Cooney, Lauren; Ariti, Cono; Ahmed, Abdulkadir Ola; Ashagre, Teshome; Ayela, Anthony; Chukwumalu, Kingsley; Criado-Perez, Alison; Gómez-Restrepo, Camilo; Meredith, Caitlin; Neri, Antonio; Stellmach, Darryl; Sani-Gwarzo, Nasir; Nasidi, Abdulsalami; Shanks, Leslie; Dargan, Paul I.

2014-01-01

Background In 2010, Médecins Sans Frontières (MSF) investigated reports of high mortality in young children in Zamfara State, Nigeria, leading to confirmation of villages with widespread acute severe lead poisoning. In a retrospective analysis, we aimed to determine venous blood lead level (VBLL) thresholds and risk factors for encephalopathy using MSF programmatic data from the first year of the outbreak response. Methods and Findings We included children aged ≤5 years with VBLL ≥45 µg/dL before any chelation and recorded neurological status. Odds ratios (OR) for neurological features were estimated; the final model was adjusted for age and baseline VBLL, using random effects for village of residence. 972 children met inclusion criteria: 885 (91%) had no neurological features; 34 (4%) had severe features; 47 (5%) had reported recent seizures; and six (1%) had other neurological abnormalities. The geometric mean VBLLs for all groups with neurological features were >100 µg/dL vs 65.9 µg/dL for those without neurological features. The adjusted OR for neurological features increased with increasing VBLL: from 2.75, 95%CI 1.27–5.98 (80–99.9 µg/dL) to 22.95, 95%CI 10.54–49.96 (≥120 µg/dL). Neurological features were associated with younger age (OR 4.77 [95% CI 2.50–9.11] for 1–<2 years and 2.69 [95%CI 1.15–6.26] for 2–<3 years, both vs 3–5 years). Severe neurological features were seen at VBLL <105 µg/dL only in those with malaria. Interpretation Increasing VBLL (from ≥80 µg/dL) and age 1–<3 years were strongly associated with neurological features; in those tested for malaria, a positive test was also strongly associated. These factors will help clinicians managing children with lead poisoning in prioritising therapy and developing chelation protocols. PMID:24740291

LLWnotes - Volume 11, Number 3

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

NONE

1996-04-01

This document is the April 1996 issue of LLWnotes. It contains articles and news items on the following topics: news items related to states and compacts, Low-Level Radioactive Waste (LLW) Forum activities, and court rulings and calendars. State and compact items featured include Texas licensing procedures, renewal of Envirocare`s license, and Ward Valley. Massachusetts Board suspension of some siting tasks and Massachusetts Court rules for US DOE regarding rebates are also reported.

A Back-to-Front Derivation: The Equal Spacing of Quantum Levels Is a Proof of Simple Harmonic Oscillator Physics

ERIC Educational Resources Information Center

Andrews, David L.; Romero, Luciana C. Davila

2009-01-01

The dynamical behaviour of simple harmonic motion can be found in numerous natural phenomena. Within the quantum realm of atomic, molecular and optical systems, two main features are associated with harmonic oscillations: a finite ground-state energy and equally spaced quantum energy levels. Here it is shown that there is in fact a one-to-one…

Robot-Arm Dynamic Control by Computer

NASA Technical Reports Server (NTRS)

Bejczy, Antal K.; Tarn, Tzyh J.; Chen, Yilong J.

1987-01-01

Feedforward and feedback schemes linearize responses to control inputs. Method for control of robot arm based on computed nonlinear feedback and state tranformations to linearize system and decouple robot end-effector motions along each of cartesian axes augmented with optimal scheme for correction of errors in workspace. Major new feature of control method is: optimal error-correction loop directly operates on task level and not on joint-servocontrol level.

Invisible Electronic States and Their Dynamics Revealed by Perturbations

NASA Astrophysics Data System (ADS)

Merer, Anthony J.

2011-06-01

Sooner or later everyone working in the field of spectroscopy encounters perturbations. These can range in size from a small shift of a single rotational level to total destruction of the vibrational and rotational patterns of an electronic state. To some workers perturbations are a source of terror, but to others they are the most fascinating features of molecular spectra, because they give information about molecular dynamics, and about states that would otherwise be invisible as a result of unfavorable selection rules. An example of the latter is the essentially complete characterization of the tilde{b}^3A_2 state of SO_2 from the vibronic perturbations it causes in the tilde{a}^3B_1 state. The S_1-trans state of acetylene is a beautiful example of dynamics in action. The level patterns of the three bending vibrations change dramatically with increasing vibrational excitation as a result of the vibrational angular momentum and the approach to the isomerization barrier. Several vibrational levels of the S_1-cis isomer, previously thought to be unobservable, can now be assigned. They obtain their intensity through interactions with nearby levels of the trans isomer.

Neural signal processing and closed-loop control algorithm design for an implanted neural recording and stimulation system.

PubMed

Hamilton, Lei; McConley, Marc; Angermueller, Kai; Goldberg, David; Corba, Massimiliano; Kim, Louis; Moran, James; Parks, Philip D; Sang Chin; Widge, Alik S; Dougherty, Darin D; Eskandar, Emad N

2015-08-01

A fully autonomous intracranial device is built to continually record neural activities in different parts of the brain, process these sampled signals, decode features that correlate to behaviors and neuropsychiatric states, and use these features to deliver brain stimulation in a closed-loop fashion. In this paper, we describe the sampling and stimulation aspects of such a device. We first describe the signal processing algorithms of two unsupervised spike sorting methods. Next, we describe the LFP time-frequency analysis and feature derivation from the two spike sorting methods. Spike sorting includes a novel approach to constructing a dictionary learning algorithm in a Compressed Sensing (CS) framework. We present a joint prediction scheme to determine the class of neural spikes in the dictionary learning framework; and, the second approach is a modified OSort algorithm which is implemented in a distributed system optimized for power efficiency. Furthermore, sorted spikes and time-frequency analysis of LFP signals can be used to generate derived features (including cross-frequency coupling, spike-field coupling). We then show how these derived features can be used in the design and development of novel decode and closed-loop control algorithms that are optimized to apply deep brain stimulation based on a patient's neuropsychiatric state. For the control algorithm, we define the state vector as representative of a patient's impulsivity, avoidance, inhibition, etc. Controller parameters are optimized to apply stimulation based on the state vector's current state as well as its historical values. The overall algorithm and software design for our implantable neural recording and stimulation system uses an innovative, adaptable, and reprogrammable architecture that enables advancement of the state-of-the-art in closed-loop neural control while also meeting the challenges of system power constraints and concurrent development with ongoing scientific research designed to define brain network connectivity and neural network dynamics that vary at the individual patient level and vary over time.

Direct observation of vibrational energy dispersal via methyl torsions.

PubMed

Gardner, Adrian M; Tuttle, William D; Whalley, Laura E; Wright, Timothy G

2018-02-28

Explicit evidence for the role of methyl rotor levels in promoting energy dispersal is reported. A set of coupled zero-order vibration/vibration-torsion (vibtor) levels in the S 1 state of para -fluorotoluene ( p FT) are investigated. Two-dimensional laser-induced fluorescence (2D-LIF) and two-dimensional zero-kinetic-energy (2D-ZEKE) spectra are reported, and the assignment of the main features in both sets of spectra reveals that the methyl torsion is instrumental in providing a route for coupling between vibrational levels of different symmetry classes. We find that there is very localized, and selective, dissipation of energy via doorway states, and that, in addition to an increase in the density of states, a critical role of the methyl group is a relaxation of symmetry constraints compared to direct vibrational coupling.

Photomask linewidth comparison by PTB and NIST

NASA Astrophysics Data System (ADS)

Bergmann, D.; Bodermann, B.; Bosse, H.; Buhr, E.; Dai, G.; Dixson, R.; Häßler-Grohne, W.; Hahm, K.; Wurm, M.

2015-10-01

We report the initial results of a recent bilateral comparison of linewidth or critical dimension (CD) calibrations on photomask line features between two national metrology institutes (NMIs): the National Institute of Standards and Technology (NIST) in the United States and the Physikalisch-Technische Bundesanstalt (PTB) in Germany. For the comparison, a chrome on glass (CoG) photomask was used which has a layout of line features down to 100 nm nominal size. Different measurement methods were used at both institutes. These included: critical dimension atomic force microscopy (CD-AFM), CD scanning electron microscopy (CD-SEM) and ultraviolet (UV) transmission optical microscopy. The measurands are CD at 50 % height of the features as well as sidewall angle and line width roughness (LWR) of the features. On the isolated opaque features, we found agreement of the CD measurements at the 3 nm to 5 nm level on most features - usually within the combined expanded uncertainties of the measurements.

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

PubMed

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Extracellular redox state regulates features associated with prostate cancer cell invasion.

PubMed

Chaiswing, Luksana; Zhong, Weixiong; Cullen, Joseph J; Oberley, Larry W; Oberley, Terry D

2008-07-15

We have examined the possible role of extracellular reduction-oxidation (redox) state in regulation of biological/biochemical features associated with prostate cancer cell invasion. DU145, PC-3, and RWPE1-derived human prostate cancer (WPE1-NB26) cell lines were used for the present in vitro analysis. Increasing levels of nitric oxide using S-nitroso-N-acetylpenicillamine resulted in a decrease in cell invasion ability, whereas increasing levels of extracellular superoxide radical (O(2)(*-)) using xanthine/xanthine oxidase resulted in an increase in cell invasion ability in these three cell lines. WPE1-NB26 cells exhibited an increased glutathione/glutathione disulfide ratio in the medium in comparison with RWPE1 cells (immortalized but nonmalignant prostate epithelial cells), suggesting an alteration of extracellular redox state of WPE1-NB26 cells. We hypothesized that O(2)(*-) production at or near the plasma membrane or in the adjacent extracellular matrix at least partially regulated prostate cancer cell invasion. Using adenovirus-mediated extracellular superoxide dismutase (EC-SOD) gene transduction to enzymatically decrease O(2)(*-) levels, we showed that in the presence of heparin, adenovirus EC-SOD gene transduction resulted in an increase in the expression of EC-SOD outside the cells with resultant inhibition of cell invasion ability. This inhibition correlated with reduced metalloproteinase [matrix metalloproteinase (MMP) 2/membrane type 1-MMP] activities and increased levels of extracellular nitrite. Our results suggest a prominent role of extracellular redox status in regulation of cell invasion, which may provide opportunities for therapeutic interventions.

Recipe for generating Weyl semimetals with extended topologically protected features

NASA Astrophysics Data System (ADS)

Wang, R.; Zhao, J. Z.; Jin, Y. J.; Xu, W. P.; Gan, L.-Y.; Wu, X. Z.; Xu, H.; Tong, S. Y.

2017-09-01

We present a recipe that leads to Weyl semimetals with extended topologically protected features. We show that compounds in a family that possess time-reversal symmetry and share a noncentrosymmetric cubic structure with the space group F -43 m (no. 216) host robust Weyl fermions with extended and easily measurable protected features. The candidates in this family are compounds with different chemical formulas, A B2 , ABC, AB C2 , and ABCD, and their Fermi levels are predominantly populated by nontrivial Weyl fermions. Symmetry of the system requires that the Weyl nodes with opposite chirality are well separated in momentum space. Adjacent Weyl points have the same chirality; thus these Weyl nodes would not annihilate each other with respect to lattice perturbations. As Fermi arcs and surface states connect Weyl nodes with opposite chirality, the large separation of the latter in momentum space guarantees the appearance of very long arcs and surface states. This work demonstrates that the use of system symmetry by first-principles calculations is a powerful approach for discovering new Weyl semimetals with attractive features whose protected fermions may be candidates of many applications.

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-01-01

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System

PubMed Central

Kwon, Sungjun; Lee, Dongseok; Kim, Jeehoon; Lee, Youngki; Kang, Seungwoo; Seo, Sangwon; Park, Kwangsuk

2016-01-01

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user’s ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user’s high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user’s daily smartphone use. PMID:26978364

Sinabro: A Smartphone-Integrated Opportunistic Electrocardiogram Monitoring System.

PubMed

Kwon, Sungjun; Lee, Dongseok; Kim, Jeehoon; Lee, Youngki; Kang, Seungwoo; Seo, Sangwon; Park, Kwangsuk

2016-03-11

In our preliminary study, we proposed a smartphone-integrated, unobtrusive electrocardiogram (ECG) monitoring system, Sinabro, which monitors a user's ECG opportunistically during daily smartphone use without explicit user intervention. The proposed system also monitors ECG-derived features, such as heart rate (HR) and heart rate variability (HRV), to support the pervasive healthcare apps for smartphones based on the user's high-level contexts, such as stress and affective state levels. In this study, we have extended the Sinabro system by: (1) upgrading the sensor device; (2) improving the feature extraction process; and (3) evaluating extensions of the system. We evaluated these extensions with a good set of algorithm parameters that were suggested based on empirical analyses. The results showed that the system could capture ECG reliably and extract highly accurate ECG-derived features with a reasonable rate of data drop during the user's daily smartphone use.

Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions.

PubMed

Li, Zuojin; Li, Shengbo Eben; Li, Renjie; Cheng, Bo; Shi, Jinliang

2017-03-02

This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: "wake" and "drowsy". The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the "awake" state, and 15.15% false detections of the "drowsy" state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue.

Action recognition using mined hierarchical compound features.

PubMed

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical approach outperforms all other methods reported thus far in the literature and can achieve real-time operation.

2005 5th Annual CMMI Technology Conference and User Group. Volume 3 - Wednesday

DTIC Science & Technology

2005-11-17

Product-Related Mistakes 28. Requirements gold-plating 29. Feature creep 30. Developer gold-plating 31. Push me, pull me negotiation 32. Research...STATE UNIVERSITY 14 IV&V Layer – Select Criticality Levels for IV&V Techniques using pull -down menus PORTLAND STATE UNIVERSITY 15...of time • Develop a proposal describing how to accomplish the goal and identifying what resources would be required Look for better solutions! • Pull

Preparation of Greenberger-Horne-Zeilinger Entangled States in the Atom-Cavity Systems

NASA Astrophysics Data System (ADS)

Xu, Nan

2018-02-01

We present a new simple scheme for the preparation of Greenberger-Horne-Zeilinger maximally entangled states of two two-level atoms. The distinct feature of the effective Hamiltonian is that there is no energy exchange between the atoms and the cavity.. Thus the scheme is insensitive to the effect of cavity field and the atom radiation.This protocol may be realizable in the realm of current physical experiment.

A study of two cases of comma-cloud cyclogenesis using a semigeostrophic model

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

Craig, G.C.; Cho, Hanru

1992-12-01

The linear stability of two atmospheric flows is studied, with basic-state data taken from environments where comma clouds are observed to flow. Each basic state features a baroclinic zone associated with an upper-level jet, with conditional instability on the north side. The semigeostrophic approximation is utilized, along with a simple parameterization for cumulus heating, and the eigenvalue problem is solved employing a Chebyshev spectral technique. 47 refs.

Pulmonary embolism detection using localized vessel-based features in dual energy CT

NASA Astrophysics Data System (ADS)

Dicente Cid, Yashin; Depeursinge, Adrien; Foncubierta Rodríguez, Antonio; Platon, Alexandra; Poletti, Pierre-Alexandre; Müller, Henning

2015-03-01

Pulmonary embolism (PE) affects up to 600,000 patients and contributes to at least 100,000 deaths every year in the United States alone. Diagnosis of PE can be difficult as most symptoms are unspecific and early diagnosis is essential for successful treatment. Computed Tomography (CT) images can show morphological anomalies that suggest the existence of PE. Various image-based procedures have been proposed for improving computer-aided diagnosis of PE. We propose a novel method for detecting PE based on localized vessel-based features computed in Dual Energy CT (DECT) images. DECT provides 4D data indexed by the three spatial coordinates and the energy level. The proposed features encode the variation of the Hounsfield Units across the different levels and the CT attenuation related to the amount of iodine contrast in each vessel. A local classification of the vessels is obtained through the classification of these features. Moreover, the localization of the vessel in the lung provides better comparison between patients. Results show that the simple features designed are able to classify pulmonary embolism patients with an AUC (area under the receiver operating curve) of 0.71 on a lobe basis. Prior segmentation of the lung lobes is not necessary because an automatic atlas-based segmentation obtains similar AUC levels (0.65) for the same dataset. The automatic atlas reaches 0.80 AUC in a larger dataset with more control cases.

A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

PubMed Central

Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

2017-01-01

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

Conservation assessment of current and proposed nature reserves of Tamaulipas State, Mexico

USGS Publications Warehouse

Cantú, César; Wright, R. Gerald; Scott, J. Michael; Strand, Eva

2003-01-01

The Mexican state of Tamaulipas located in the northeastern portion of the country currently has five state nature reserves covering slightly less than 3% of its land area. These reserves were established for a variety of reasons, many unrelated to the protection of biological resources. In 2000 in response to a growing concern about the lack of organized conservation reserve planning to protect the important biological and physical features of Mexico, the Mexican Commission for Knowledge and Use of Biodiversity (CONABIO) proposed 13 new terrestrial reserves for Tamaulipas. If established these new reserves would increase the proportion of terrestrial protected lands in the state to over 21%. We compiled a geographic information system (GIS) using existing digital thematic maps of physical and biological features to examine how the existing and proposed reserves might serve to protect the biodiversity and physical features of the state. We found that most of the existing protected sites occur in areas with elevations > 1000-2000 m with temperate climate and dominated by pine forest, oak forest, and cloud forest vegetation cover types. The state's dominant biotic region - low elevation coastal plain with tropical and arid climate types and xeric scrub vegetation - is disproportionately underrepresented in the current reserve system. The creation of the proposed areas would substantially increase the protection of mid and high elevation lands. The largest gap in the protected lands network would be low elevation, level, coastal lands.

Dynamics of entropy and nonclassical properties of the state of a Λ-type three-level atom interacting with a single-mode cavity field with intensity-dependent coupling in a Kerr medium

NASA Astrophysics Data System (ADS)

Faghihi, M. J.; Tavassoly, M. K.

2012-02-01

In this paper, we study the interaction between a three-level atom and a quantized single-mode field with ‘intensity-dependent coupling’ in a ‘Kerr medium’. The three-level atom is considered to be in a Λ-type configuration. Under particular initial conditions, which may be prepared for the atom and the field, the dynamical state vector of the entire system will be explicitly obtained, for the arbitrary nonlinearity function f(n) associated with any physical system. Then, after evaluating the variation of the field entropy against time, we will investigate the quantum statistics as well as some of the nonclassical properties of the introduced state. During our calculations we investigate the effects of intensity-dependent coupling, Kerr medium and detuning parameters on the depth and domain of the nonclassicality features of the atom-field state vector. Finally, we compare our obtained results with those of V-type three-level atoms.

Magnetic-field-induced mixed-level Kondo effect in two-level systems

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

Wong, Arturo; Ngo, Anh T.; Ulloa, Sergio E.

2016-10-17

We consider a two-orbital impurity system with intra-and interlevel Coulomb repulsion that is coupled to a single conduction channel. This situation can generically occur in multilevel quantum dots or in systems of coupled quantum dots. For finite energy spacing between spin-degenerate orbitals, an in-plane magnetic field drives the system from a local-singlet ground state to a "mixed-level" Kondo regime, where the Zeeman-split levels are degenerate for opposite-spin states. We use the numerical renormalization group approach to fully characterize this mixed-level Kondo state and discuss its properties in terms of the applied Zeeman field, temperature, and system parameters. Under suitable conditions,more » the total spectral function is shown to develop a Fermi-level resonance, so that the linear conductance of the system peaks at a finite Zeeman field while it decreases as a function of temperature. These features, as well as the local moment and entropy contribution of the impurity system, are commensurate with Kondo physics, which can be studied in suitably tuned quantum dot systems.« less

Inherent structure versus geometric metric for state space discretization.

PubMed

Liu, Hanzhong; Li, Minghai; Fan, Jue; Huo, Shuanghong

2016-05-30

Inherent structure (IS) and geometry-based clustering methods are commonly used for analyzing molecular dynamics trajectories. ISs are obtained by minimizing the sampled conformations into local minima on potential/effective energy surface. The conformations that are minimized into the same energy basin belong to one cluster. We investigate the influence of the applications of these two methods of trajectory decomposition on our understanding of the thermodynamics and kinetics of alanine tetrapeptide. We find that at the microcluster level, the IS approach and root-mean-square deviation (RMSD)-based clustering method give totally different results. Depending on the local features of energy landscape, the conformations with close RMSDs can be minimized into different minima, while the conformations with large RMSDs could be minimized into the same basin. However, the relaxation timescales calculated based on the transition matrices built from the microclusters are similar. The discrepancy at the microcluster level leads to different macroclusters. Although the dynamic models established through both clustering methods are validated approximately Markovian, the IS approach seems to give a meaningful state space discretization at the macrocluster level in terms of conformational features and kinetics. © 2016 Wiley Periodicals, Inc.

[Features of adaptive responses in right-handers and left-handers, and their relationship to the functional activity of the brain].

PubMed

Barkar, A A; Markina, L D

2014-01-01

In the article there is considered the relationship between adaptation state of the organism and features of bioelectric activity of the brain in right-handers and left-handers. Practically healthy persons of both genders, 23-45 years of age, with the chronic stress disorder were examined. Adaptation status was evaluated with a computer software "Anti-stress", features of bioelectric brain activity were detected by means of spectral and coherent EEG analysis, also the character of motor and sensory asymmetries was determined. The obtained data showed that the response of the organism to excitators of varying strength is a system one and manifested at different levels; adaptation status and bioelectrical activity in right-handers and left-handers have features.

Uncertainty and inference in the world of paleoecological data

NASA Astrophysics Data System (ADS)

McLachlan, J. S.; Dawson, A.; Dietze, M.; Finley, M.; Hooten, M.; Itter, M.; Jackson, S. T.; Marlon, J. R.; Raiho, A.; Tipton, J.; Williams, J.

2017-12-01

Proxy data in paleoecology and paleoclimatology share a common set of biases and uncertainties: spatiotemporal error associated with the taphonomic processes of deposition, preservation, and dating; calibration error between proxy data and the ecosystem states of interest; and error in the interpolation of calibrated estimates across space and time. Researchers often account for this daunting suite of challenges by applying qualitave expert judgment: inferring the past states of ecosystems and assessing the level of uncertainty in those states subjectively. The effectiveness of this approach can be seen by the extent to which future observations confirm previous assertions. Hierarchical Bayesian (HB) statistical approaches allow an alternative approach to accounting for multiple uncertainties in paleo data. HB estimates of ecosystem state formally account for each of the common uncertainties listed above. HB approaches can readily incorporate additional data, and data of different types into estimates of ecosystem state. And HB estimates of ecosystem state, with associated uncertainty, can be used to constrain forecasts of ecosystem dynamics based on mechanistic ecosystem models using data assimilation. Decisions about how to structure an HB model are also subjective, which creates a parallel framework for deciding how to interpret data from the deep past.Our group, the Paleoecological Observatory Network (PalEON), has applied hierarchical Bayesian statistics to formally account for uncertainties in proxy based estimates of past climate, fire, primary productivity, biomass, and vegetation composition. Our estimates often reveal new patterns of past ecosystem change, which is an unambiguously good thing, but we also often estimate a level of uncertainty that is uncomfortably high for many researchers. High levels of uncertainty are due to several features of the HB approach: spatiotemporal smoothing, the formal aggregation of multiple types of uncertainty, and a coarseness in statistical models of taphonomic process. Each of these features provides useful opportunities for statisticians and data-generating researchers to assess what we know about the signal and the noise in paleo data and to improve inference about past changes in ecosystem state.

Conference on Continental margin mass wasting and Pleistocene sea-level changes, August 13-15, 1980

USGS Publications Warehouse

Folger, David W.; Hathaway, J.C.

1987-01-01

A conference on Continental Margin Mass Wasting and Pleistocene Sea-Level Changes was held in Woods Hole, Mass., August 13-15, 1980. Forty-seven participants, representing many government, academic, and industrial organizations, discussed the current state of knowledge of the features of marine mass wasting and of the interrelations of factors influencing them. These factors include sediment source, composition, textures, sedimentation rates, climatic and sea-level changes, gas and gas hydrate (clathrate) contents of sediments, geotechnical characteristics, oceanographic and morphological factors, ground-water processes, and seismic events. The part played by these factors in the processes and features of mass movement and the engineering considerations imposed by the emplacement of manmade structures on the sea floor were considered vital to the evaluation of hazards involved in offshore exploration and development. The conference concluded with a call for bold programs to establish the probability of occurrence and the quantitative importance of these factors and to devise more reliable means of measurement, particularly in place, of the characteristics of the sediment and features involved.

Geospatial Data Science Publications | Geospatial Data Science | NREL

Science.gov Websites

research in these publications. Featured Publications U.S. Renewable Energy Technical Potentials: A GIS -Based Analysis, NREL Technical Report (2012) 2016 Offshore Wind Energy Resource Assessment for the -Temperature Geothermal Resources of the United States, 40th GRC Annual Meeting (2016) High-Level Overview of

Media Objectives for Teachers.

ERIC Educational Resources Information Center

Arrasjid, Harun, Ed.; Arrasjid, Dorine, Ed.

The purpose of this book is to provide a compendium of instructional objectives concerning instructional media and materials. It is useful for either teacher training programs or college level media courses. Each objective is stated behaviorally, such as "list the important features of a tape recorder." Objectives are arranged by medium. The media…

High Precision Prediction of Functional Sites in Protein Structures

PubMed Central

Buturovic, Ljubomir; Wong, Mike; Tang, Grace W.; Altman, Russ B.; Petkovic, Dragutin

2014-01-01

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta. PMID:24632601

Self-esteem instability and personality: the connections between feelings of self-worth and the big five dimensions of personality.

PubMed

Zeigler-Hill, Virgil; Holden, Christopher J; Enjaian, Brian; Southard, Ashton C; Besser, Avi; Li, Haijiang; Zhang, Qinglin

2015-02-01

Relatively few studies have focused on the connections between self-esteem and basic personality dimensions. The purpose of the present studies was to examine whether self-esteem level and self-esteem instability were associated with the Big Five personality dimensions and whether self-esteem instability moderated the associations that self-esteem level had with these personality features. This was accomplished by conducting a series of studies that included samples from the United States, Israel, and China. Across these studies, self-esteem level was associated with high levels of extraversion, emotional stability, agreeableness, conscientiousness, and openness, whereas self-esteem instability was associated with low levels of emotional stability, agreeableness, and conscientiousness. Individuals with stable high self-esteem reported the highest levels of emotional stability, agreeableness, and conscientiousness, whereas those with stable low self-esteem had the lowest levels of openness. The results of these studies suggest that feelings of self-worth are associated with self-reported and perceived personality features. © 2014 by the Society for Personality and Social Psychology, Inc.

Image segmentation via foreground and background semantic descriptors

NASA Astrophysics Data System (ADS)

Yuan, Ding; Qiang, Jingjing; Yin, Jihao

2017-09-01

In the field of image processing, it has been a challenging task to obtain a complete foreground that is not uniform in color or texture. Unlike other methods, which segment the image by only using low-level features, we present a segmentation framework, in which high-level visual features, such as semantic information, are used. First, the initial semantic labels were obtained by using the nonparametric method. Then, a subset of the training images, with a similar foreground to the input image, was selected. Consequently, the semantic labels could be further refined according to the subset. Finally, the input image was segmented by integrating the object affinity and refined semantic labels. State-of-the-art performance was achieved in experiments with the challenging MSRC 21 dataset.

Exploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data

PubMed Central

Ahn, Sangtae; Nguyen, Thien; Jang, Hyojung; Kim, Jae G.; Jun, Sung C.

2016-01-01

Investigations of the neuro-physiological correlates of mental loads, or states, have attracted significant attention recently, as it is particularly important to evaluate mental fatigue in drivers operating a motor vehicle. In this research, we collected multimodal EEG/ECG/EOG and fNIRS data simultaneously to develop algorithms to explore neuro-physiological correlates of drivers' mental states. Each subject performed simulated driving under two different conditions (well-rested and sleep-deprived) on different days. During the experiment, we used 68 electrodes for EEG/ECG/EOG and 8 channels for fNIRS recordings. We extracted the prominent features of each modality to distinguish between the well-rested and sleep-deprived conditions, and all multimodal features, except EOG, were combined to quantify mental fatigue during driving. Finally, a novel driving condition level (DCL) was proposed that distinguished clearly between the features of well-rested and sleep-deprived conditions. This proposed DCL measure may be applicable to real-time monitoring of the mental states of vehicle drivers. Further, the combination of methods based on each classifier yielded substantial improvements in the classification accuracy between these two conditions. PMID:27242483

Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.

PubMed

Abbas, Qaisar; Fondon, Irene; Sarmiento, Auxiliadora; Jiménez, Soledad; Alemany, Pedro

2017-11-01

Diabetic retinopathy (DR) is leading cause of blindness among diabetic patients. Recognition of severity level is required by ophthalmologists to early detect and diagnose the DR. However, it is a challenging task for both medical experts and computer-aided diagnosis systems due to requiring extensive domain expert knowledge. In this article, a novel automatic recognition system for the five severity level of diabetic retinopathy (SLDR) is developed without performing any pre- and post-processing steps on retinal fundus images through learning of deep visual features (DVFs). These DVF features are extracted from each image by using color dense in scale-invariant and gradient location-orientation histogram techniques. To learn these DVF features, a semi-supervised multilayer deep-learning algorithm is utilized along with a new compressed layer and fine-tuning steps. This SLDR system was evaluated and compared with state-of-the-art techniques using the measures of sensitivity (SE), specificity (SP) and area under the receiving operating curves (AUC). On 750 fundus images (150 per category), the SE of 92.18%, SP of 94.50% and AUC of 0.924 values were obtained on average. These results demonstrate that the SLDR system is appropriate for early detection of DR and provide an effective treatment for prediction type of diabetes.

A Feature-based Approach to Big Data Analysis of Medical Images

PubMed Central

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M.

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches in O(log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct. PMID:26221685

A Feature-Based Approach to Big Data Analysis of Medical Images.

PubMed

Toews, Matthew; Wachinger, Christian; Estepar, Raul San Jose; Wells, William M

2015-01-01

This paper proposes an inference method well-suited to large sets of medical images. The method is based upon a framework where distinctive 3D scale-invariant features are indexed efficiently to identify approximate nearest-neighbor (NN) feature matches-in O (log N) computational complexity in the number of images N. It thus scales well to large data sets, in contrast to methods based on pair-wise image registration or feature matching requiring O(N) complexity. Our theoretical contribution is a density estimator based on a generative model that generalizes kernel density estimation and K-nearest neighbor (KNN) methods.. The estimator can be used for on-the-fly queries, without requiring explicit parametric models or an off-line training phase. The method is validated on a large multi-site data set of 95,000,000 features extracted from 19,000 lung CT scans. Subject-level classification identifies all images of the same subjects across the entire data set despite deformation due to breathing state, including unintentional duplicate scans. State-of-the-art performance is achieved in predicting chronic pulmonary obstructive disorder (COPD) severity across the 5-category GOLD clinical rating, with an accuracy of 89% if both exact and one-off predictions are considered correct.

Temperature-driven Topological Phase Transition in MoTe2

NASA Astrophysics Data System (ADS)

Notis Berger, Ayelet; Andrade, Erick; Kerelsky, Alex; Cheong, Sang-Wook; Li, Jian; Bernevig, B. Andrei; Pasupathy, Abhay

The discovery of several candidates predicted to be weyl semimetals has made it possible to experimentally study weyl fermions and their exotic properties. One example is MoTe2, a transition metal dichalcogenide. At temperatures below 240 K it is predicted to be a type II Weyl semimetal with four Weyl points close to the fermi level. As with most weyl semimetals, the complicated band structure causes difficulty in distinguishing features related to bulk states and those related to topological fermi arc surface states characteristic of weyl semimetals. MoTe2 is unique because of its temperature-driven phase change. At high temperatures, MoTe2 is monoclinic, with trivial surface states. When cooled below 240K, it undergoes a first order phase transition to become an orthorhombic weyl semimetal with topologically protected fermi arc surface states. We present STM and STS measurements on MoTe2 crystals in both states. In the orthorhombic phase, we observe scattering that is consistent with the presence of the Fermi-arc surface states. Upon warming into the monoclinic phase, these features disappear in the observed interference patterns, providing direct evidence of the topological nature of the fermi arcs in the Weyl phase

Electronic fingerprints of DNA bases on graphene.

PubMed

Ahmed, Towfiq; Kilina, Svetlana; Das, Tanmoy; Haraldsen, Jason T; Rehr, John J; Balatsky, Alexander V

2012-02-08

We calculate the electronic local density of states (LDOS) of DNA nucleotide bases (A,C,G,T), deposited on graphene. We observe significant base-dependent features in the LDOS in an energy range within a few electronvolts of the Fermi level. These features can serve as electronic fingerprints for the identification of individual bases in scanning tunneling spectroscopy (STS) experiments that perform image and site dependent spectroscopy on biomolecules. Thus the fingerprints of DNA-graphene hybrid structures may provide an alternative route to DNA sequencing using STS. © 2012 American Chemical Society

A Method of Character Detection and Segmentation for Highway Guide Signs

NASA Astrophysics Data System (ADS)

Xu, Jiawei; Zhang, Chongyang

2018-01-01

In this paper, a method of character detection and segmentation for highway signs in China is proposed. It consists of four steps. Firstly, the highway sign area is detectedby colour and geometric features, andthe possible character region is obtained by multi-level projection strategy. Secondly, pseudo target character region is removed by local binary patterns (LBP) feature. Thirdly, convolutional neural network (CNN)is used to classify target regions. Finally, adaptive projection strategies are used to segment characters strings. Experimental results indicate that the proposed method achieves new state-of-the-art results.

Relativistic effects in the photoionization of hydrogen-like ions with screened Coulomb interaction

NASA Astrophysics Data System (ADS)

Xie, L. Y.; Wang, J. G.; Janev, R. K.

2014-06-01

The relativistic effects in the photoionization of hydrogen-like ion with screened Coulomb interaction of Yukawa type are studied for a broad range of screening lengths and photoelectron energies. The bound and continuum wave functions have been determined by solving the Dirac equation. The study is focused on the relativistic effects manifested in the characteristic features of photoionization cross section for electric dipole nl →ɛ,l±1 transitions: shape resonances, Cooper minima and cross section enhancements due to near-zero-energy states. It is shown that the main source of relativistic effects in these cross section features is the fine-structure splitting of bound state energy levels. The relativistic effects are studied in the photoionization of Fe25+ ion, as an example.

Relativistic effects in the photoionization of hydrogen-like ions with screened Coulomb interaction

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

Xie, L. Y.; Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, P.O. Box 8009-26, Beijing 100088; Wang, J. G.

2014-06-15

The relativistic effects in the photoionization of hydrogen-like ion with screened Coulomb interaction of Yukawa type are studied for a broad range of screening lengths and photoelectron energies. The bound and continuum wave functions have been determined by solving the Dirac equation. The study is focused on the relativistic effects manifested in the characteristic features of photoionization cross section for electric dipole nl→ε,l±1 transitions: shape resonances, Cooper minima and cross section enhancements due to near-zero-energy states. It is shown that the main source of relativistic effects in these cross section features is the fine-structure splitting of bound state energy levels.more » The relativistic effects are studied in the photoionization of Fe{sup 25+} ion, as an example.« less

[Spectral features analysis of Pinus massoniana with pest of Dendrolimus punctatus Walker and levels detection].

PubMed

Xu, Zhang-Hua; Liu, Jian; Yu, Kun-Yong; Gong, Cong-Hong; Xie, Wan-Jun; Tang, Meng-Ya; Lai, Ri-Wen; Li, Zeng-Lu

2013-02-01

Taking 51 field measured hyperspectral data with different pest levels in Yanping, Fujian Province as objects, the spectral reflectance and first derivative features of 4 levels of healthy, mild, moderate and severe insect pest were analyzed. On the basis of 7 detecting parameters construction, the pest level detecting models were built. The results showed that (1) the spectral reflectance of Pinus massoniana with pests were significantly lower than that of healthy state, and the higher the pest level, the lower the reflectance; (2) with the increase in pest level, the spectral reflectance curves' "green peak" and "red valley" of Pinus massoniana gradually disappeared, and the red edge was leveleds (3) the pest led to spectral "green peak" red shift, red edge position blue shift, but the changes in "red valley" and near-infrared position were complicated; (4) CARI, RES, REA and REDVI were highly relevant to pest levels, and the correlations between REP, RERVI, RENDVI and pest level were weak; (5) the multiple linear regression model with the variables of the 7 detection parameters could effectively detect the pest levels of Dendrolimus punctatus Walker, with both the estimation rate and accuracy above 0.85.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques

PubMed Central

Cheng, Wei; Ji, Xiaoxi; Zhang, Jie; Feng, Jianfeng

2012-01-01

Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15% across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27% and the specificity being 85.11%. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders. PMID:22888314

The Effect of Observers’ Mood on the Local Processing of Emotional Faces: Evidence from Short-Lived and Prolonged Mood States

PubMed Central

Mokhtari, Setareh; Buttle, Heather

2015-01-01

We examined the effect of induced mood, varying in valence and longevity, on local processing of emotional faces. It was found that negative facial expression conveyed by the global level of the face interferes with efficient processing of the local features. The results also showed that the duration of involvement with a mood influenced the local processing. We observed that attending to the local level of faces is not different in short-lived happy and sad mood states. However, as the mood state is experienced for a longer period, local processing was impaired in happy mood compared to sad mood. Taken together, we concluded that both facial expressions and affective states influence processing of the local parts of faces. Moreover, we suggest that mediating factors like the duration of involvement with the mood play a role in the interrelation between mood, attention, and perception. PMID:25883696

Bacteriological analysis of indoor and outdoor water parks in Wisconsin.

PubMed

Davis, Tracynda L; Standridge, Jon H; Degnan, Alan J

2009-09-01

Water parks are a rapidly growing element of the United States tourist industry. To reduce incidence of abrasion and impact injuries in such parks, designers are searching for padding materials that can withstand the harsh oxidative environments of chlorinated water. Although padded features help reduce physical injuries, they may also compromise the microbiological safety of water attractions. This study describes bacteriological testing performed on 31 different pad materials, play features and pools from 10 Wisconsin water parks. Materials and surrounding pool waters were sampled and tested quantitatively for total coliforms, Escherichia coli, E. coli 0157:H7, enterococci, staphylococci, heterotrophic bacteria, and Pseudomonas aeruginosa, using standard methods. Each location was sampled during three visits, and results were averaged. Pool waters were within acceptable levels of target organisms and disinfectant residuals, but target organisms were found on water features, even those submerged in chlorinated water. Bacteria were detected more frequently in pools using pad materials compared with pools without. These findings provide data that will help the public health community understand the relations between designs, materials and maintenance of water features. Additionally, the information will help state regulators and owner/operators develop guidelines to improve public health and safety at water parks.

Observers' cognitive states modulate how visual inputs relate to gaze control.

PubMed

Kardan, Omid; Henderson, John M; Yourganov, Grigori; Berman, Marc G

2016-09-01

Previous research has shown that eye-movements change depending on both the visual features of our environment, and the viewer's top-down knowledge. One important question that is unclear is the degree to which the visual goals of the viewer modulate how visual features of scenes guide eye-movements. Here, we propose a systematic framework to investigate this question. In our study, participants performed 3 different visual tasks on 135 scenes: search, memorization, and aesthetic judgment, while their eye-movements were tracked. Canonical correlation analyses showed that eye-movements were reliably more related to low-level visual features at fixations during the visual search task compared to the aesthetic judgment and scene memorization tasks. Different visual features also had different relevance to eye-movements between tasks. This modulation of the relationship between visual features and eye-movements by task was also demonstrated with classification analyses, where classifiers were trained to predict the viewing task based on eye movements and visual features at fixations. Feature loadings showed that the visual features at fixations could signal task differences independent of temporal and spatial properties of eye-movements. When classifying across participants, edge density and saliency at fixations were as important as eye-movements in the successful prediction of task, with entropy and hue also being significant, but with smaller effect sizes. When classifying within participants, brightness and saturation were also significant contributors. Canonical correlation and classification results, together with a test of moderation versus mediation, suggest that the cognitive state of the observer moderates the relationship between stimulus-driven visual features and eye-movements. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Teleportation of atomic and photonic states in low-Q cavity QED

NASA Astrophysics Data System (ADS)

Peng, Zhao-Hui; Zou, Jian; Liu, Xiao-Juan; Kuang, Le-Man

2012-11-01

We propose two alternative teleportation protocols in low-Q cavity QED. Through the input-output process of photons, we can generate atom-photon entangled states as the quantum channel. Then we propose to teleport single-atom (two-atom entangled) state using coherent photonic states, and to teleport single photonic state with the assistance of three-level atom. The distinct feature of our protocols is that we can teleport both atomic and photonic states via the input-output process of photons in the low-Q cavity. Furthermore, as our protocols work in low-Q cavities and only involve virtual excitation of atoms, they are insensitive to both cavity decay and atomic spontaneous emission, and may be feasible with current technology.

Spin-polarized confined states in Ag films on Fe(110)

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

Moras, Paolo; Bihlmayer, G.; Vescovo, Elio

Spin- and angle-resolved photoemission spectroscopy of thin Ag(111) films on ferromagnetic Fe(110) shows a series of spin-polarized peaks. These features derive from Ag sp-bands, which form quantum well states and resonances due to confinement by a spin-dependent interface potential barrier. The spin-up states are broader and located at higher binding energy than the corresponding spin-down states at Gamma, although the differences attenuate near the Fermi level. The spin-down states display multiple gap openings, which interrupt their parabolic-like dispersion. As a result, first-principles calculations attribute these findings to the symmetry- and spin-selective hybridization of the Ag states with the exchange-split bandsmore » of the substrate.« less

Spin-polarized confined states in Ag films on Fe(110)

DOE PAGES

Moras, Paolo; Bihlmayer, G.; Vescovo, Elio; ...

2017-11-16

Spin- and angle-resolved photoemission spectroscopy of thin Ag(111) films on ferromagnetic Fe(110) shows a series of spin-polarized peaks. These features derive from Ag sp-bands, which form quantum well states and resonances due to confinement by a spin-dependent interface potential barrier. The spin-up states are broader and located at higher binding energy than the corresponding spin-down states at Gamma, although the differences attenuate near the Fermi level. The spin-down states display multiple gap openings, which interrupt their parabolic-like dispersion. As a result, first-principles calculations attribute these findings to the symmetry- and spin-selective hybridization of the Ag states with the exchange-split bandsmore » of the substrate.« less

Precise leveling, space geodesy and geodynamics

NASA Technical Reports Server (NTRS)

Reilinger, R.

1981-01-01

The implications of currently available leveling data on understanding the crustal dynamics of the continental United States are investigated. Neotectonic deformation, near surface movements, systematic errors in releveling measurements, and the implications of this information for earthquake prediction are described. Vertical crustal movements in the vicinity of the 1931 Valentine, Texas, earthquake which may represent coseismic deformation are investigated. The detection of vertical fault displacements by precise leveling in western Kentucky is reported. An empirical basis for defining releveling anomalies and its implications for crustal deformation in southern California is presented. Releveling measurements in the eastern United States and their meaning in the context of possible crustal deformation, including uplift of the Appalachian Mountains, eastward tilting of the Atlantic Coastal Plain, and apparent movements associated with a number of structural features along the east coast, are reported.

S -matrix calculations of energy levels of sodiumlike ions

DOE PAGES

Sapirstein, J.; Cheng, K. T.

2015-06-24

A recent S -matrix-based QED calculation of energy levels of the lithium isoelectronic sequence is extended to the general case of a valence electron outside an arbitrary filled core. Emphasis is placed on modifications of the lithiumlike formulas required because more than one core state is present, and an unusual feature of the two-photon exchange contribution involving autoionizing states is discussed. Here, the method is illustrated with a calculation of the energy levels of sodiumlike ions, with results for 3s 1/2, 3p 1/2, and 3p 3/2 energies tabulated for the range Z = 30 – 100 . Comparison with experimentmore » and other calculations is given, and prospects for extension of the method to ions with more complex electronic structure discussed.« less

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

PubMed

Brosch, Tom; Tang, Lisa Y W; Youngjin Yoo; Li, David K B; Traboulsee, Anthony; Tam, Roger

2016-05-01

We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.

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manufacturing analysis-focuses on jobs creation and economic output at the national, state, and community levels economic development and activity through investment in solar and wind projects. Featured Study In Economic construction period and 350 jobs annually during 20-year operation Total economic impact over 20-year life

Sensitive Technology Assessment of ACOT.

ERIC Educational Resources Information Center

Baker, Eva L.

This paper explores the ideas and the model underlying the evaluation of the Apple Classroom of Tomorrow project (ACOT), a 2-year-old research and development project incorporating at least seven different grade levels which is located in five different school sites in four states. The major features of ACOT are identified as the ideas of computer…

Notes on an Outreach Forum for High School Chemistry Teachers - An Unexpected Success

NASA Astrophysics Data System (ADS)

Mayfield, Darwin L.

1997-05-01

Public realization in the United States of deficiencies in understanding basic facts and processes in science and mathematics is mounting. Teachers in these areas at all levels are key players in the challenges to come. This paper describes the activities of one small group of high school chemistry teachers as they have explored these challenges. The group of approximately sixteen has met regularly on the campus of California State University, Long Beach during the past seven years. The meetings (two or three each semester) are informal three-hour sessions over the dinner hour (box dinners are provided). A feature of each meeting is discussion of articles selected from the Journal of Chemical Education including retesting with variation of "Tested Demonstrations". Subscriptions to the Journal are provided to members. No fees are charged nor course credit given. The article outlines many of the program features, describes recruitment and changes in membership over time, examines possibilities for replication and emphasizes the great desire of secondary level chemistry teachers for exchange of ideas with peers. It explores the question "what did we do right?" in launching this successful forum.

Task-related modulations of BOLD low-frequency fluctuations within the default mode network

NASA Astrophysics Data System (ADS)

Tommasin, Silvia; Mascali, Daniele; Gili, Tommaso; Eid Assan, Ibrahim; Moraschi, Marta; Fratini, Michela; Wise, Richard G.; Macaluso, Emiliano; Mangia, Silvia; Giove, Federico

2017-07-01

Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33±6 years, 8F/12M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the steady-state execution of a sustained working memory n-back task. We found that the steady state execution of such a task impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to steady-state task execution, can contribute to a better understanding of how brain networks rearrange themselves in response of a task.

The Arab-Israeli Conflict: Effects of Islamization

DTIC Science & Technology

1980-06-06

to the propaed -stare. This ;•: 15 disagreement is still today a prominent feature of thp uolit- ical life of the state of Israel. The principal matter...of a "land offerirg the proper conditions for a people to develop "its o ... and soclaZ life as a naton, actln fora better ’uture. He suggested, for...state was dealinr witb the practical aspeot of the political life , the discord between 18 seculars and religious rose to the level of an actual rift. The

Salient region detection by fusing bottom-up and top-down features extracted from a single image.

PubMed

Tian, Huawei; Fang, Yuming; Zhao, Yao; Lin, Weisi; Ni, Rongrong; Zhu, Zhenfeng

2014-10-01

Recently, some global contrast-based salient region detection models have been proposed based on only the low-level feature of color. It is necessary to consider both color and orientation features to overcome their limitations, and thus improve the performance of salient region detection for images with low-contrast in color and high-contrast in orientation. In addition, the existing fusion methods for different feature maps, like the simple averaging method and the selective method, are not effective sufficiently. To overcome these limitations of existing salient region detection models, we propose a novel salient region model based on the bottom-up and top-down mechanisms: the color contrast and orientation contrast are adopted to calculate the bottom-up feature maps, while the top-down cue of depth-from-focus from the same single image is used to guide the generation of final salient regions, since depth-from-focus reflects the photographer's preference and knowledge of the task. A more general and effective fusion method is designed to combine the bottom-up feature maps. According to the degree-of-scattering and eccentricities of feature maps, the proposed fusion method can assign adaptive weights to different feature maps to reflect the confidence level of each feature map. The depth-from-focus of the image as a significant top-down feature for visual attention in the image is used to guide the salient regions during the fusion process; with its aid, the proposed fusion method can filter out the background and highlight salient regions for the image. Experimental results show that the proposed model outperforms the state-of-the-art models on three public available data sets.

First observation of the quantized exciton-polariton field and effect of interactions on a single polariton

PubMed Central

Silva, Blanca; Fieramosca, Antonio; Tasco, Vittorianna; del Valle, Elena; Ballarini, Dario; Gigli, Giuseppe; Sanvitto, Daniele

2018-01-01

Polaritons are quasi-particles that originate from the coupling of light with matter and that demonstrate quantum phenomena at the many-particle mesoscopic level, such as Bose-Einstein condensation and superfluidity. A highly sought and long-time missing feature of polaritons is a genuine quantum manifestation of their dynamics at the single-particle level. Although they are conceptually perceived as entangled states and theoretical proposals abound for an explicit manifestation of their single-particle properties, so far their behavior has remained fully accounted for by classical and mean-field theories. We report the first experimental demonstration of a genuinely quantum state of the microcavity polariton field, by swapping a photon for a polariton in a two-photon entangled state generated by parametric downconversion. When bringing this single-polariton quantum state in contact with a polariton condensate, we observe a disentangling with the external photon. This manifestation of a polariton quantum state involving a single quantum unlocks new possibilities for quantum information processing with interacting bosons. PMID:29725616

First observation of the quantized exciton-polariton field and effect of interactions on a single polariton.

PubMed

Cuevas, Álvaro; López Carreño, Juan Camilo; Silva, Blanca; De Giorgi, Milena; Suárez-Forero, Daniel G; Sánchez Muñoz, Carlos; Fieramosca, Antonio; Cardano, Filippo; Marrucci, Lorenzo; Tasco, Vittorianna; Biasiol, Giorgio; Del Valle, Elena; Dominici, Lorenzo; Ballarini, Dario; Gigli, Giuseppe; Mataloni, Paolo; Laussy, Fabrice P; Sciarrino, Fabio; Sanvitto, Daniele

2018-04-01

Polaritons are quasi-particles that originate from the coupling of light with matter and that demonstrate quantum phenomena at the many-particle mesoscopic level, such as Bose-Einstein condensation and superfluidity. A highly sought and long-time missing feature of polaritons is a genuine quantum manifestation of their dynamics at the single-particle level. Although they are conceptually perceived as entangled states and theoretical proposals abound for an explicit manifestation of their single-particle properties, so far their behavior has remained fully accounted for by classical and mean-field theories. We report the first experimental demonstration of a genuinely quantum state of the microcavity polariton field, by swapping a photon for a polariton in a two-photon entangled state generated by parametric downconversion. When bringing this single-polariton quantum state in contact with a polariton condensate, we observe a disentangling with the external photon. This manifestation of a polariton quantum state involving a single quantum unlocks new possibilities for quantum information processing with interacting bosons.

Attosecond transient absorption probing of electronic superpositions of bound states in neon. Detection of quantum beats

DOE PAGES

Beck, Annelise R; Bernhardt, Birgitta; Warrick, Erika R.; ...

2014-11-07

Electronic wavepackets composed of multiple bound excited states of atomic neon lying between 19.6 and 21.5 eV are launched using an isolated attosecond pulse. Individual quantum beats of the wavepacket are detected by perturbing the induced polarization of the medium with a time-delayed few-femtosecond near-infrared (NIR) pulse via coupling the individual states to multiple neighboring levels. All of the initially excited states are monitored simultaneously in the attosecond transient absorption spectrum, revealing Lorentzian to Fano lineshape spectral changes as well as quantum beats. The most prominent beating of the several that were observed was in the spin–orbit split 3d absorptionmore » features, which has a 40 femtosecond period that corresponds to the spin–orbit splitting of 0.1 eV. The few-level models and multilevel calculations confirm that the observed magnitude of oscillation depends strongly on the spectral bandwidth and tuning of the NIR pulse and on the location of possible coupling states.« less

TargetCrys: protein crystallization prediction by fusing multi-view features with two-layered SVM.

PubMed

Hu, Jun; Han, Ke; Li, Yang; Yang, Jing-Yu; Shen, Hong-Bin; Yu, Dong-Jun

2016-11-01

The accurate prediction of whether a protein will crystallize plays a crucial role in improving the success rate of protein crystallization projects. A common critical problem in the development of machine-learning-based protein crystallization predictors is how to effectively utilize protein features extracted from different views. In this study, we aimed to improve the efficiency of fusing multi-view protein features by proposing a new two-layered SVM (2L-SVM) which switches the feature-level fusion problem to a decision-level fusion problem: the SVMs in the 1st layer of the 2L-SVM are trained on each of the multi-view feature sets; then, the outputs of the 1st layer SVMs, which are the "intermediate" decisions made based on the respective feature sets, are further ensembled by a 2nd layer SVM. Based on the proposed 2L-SVM, we implemented a sequence-based protein crystallization predictor called TargetCrys. Experimental results on several benchmark datasets demonstrated the efficacy of the proposed 2L-SVM for fusing multi-view features. We also compared TargetCrys with existing sequence-based protein crystallization predictors and demonstrated that the proposed TargetCrys outperformed most of the existing predictors and is competitive with the state-of-the-art predictors. The TargetCrys webserver and datasets used in this study are freely available for academic use at: http://csbio.njust.edu.cn/bioinf/TargetCrys .

Psychological features and quality of life in 50 adult patients with epilepsy and their caregivers from the Lecco epilepsy center, Italy.

PubMed

Petruzzi, Alessandra; Rigamonti, Andrea; Finocchiaro, Claudia Yvonne; Borelli, Paolo; Lamperti, Elena; Silvani, Antonio; Regazzoni, Rossana; Stanzani, Lorenzo; Salmaggi, Andrea

2017-06-01

Epilepsy is one of the most common neurological disorders. To the best of our knowledge, in Italy, the relationship between patients' and caregivers' psychological state has rarely been analyzed. Thus, we sought to evaluate both the psychological state of patients with epilepsy and that of their caregivers and the interrelationship between them. We also assessed the existing relation between psychological features and some clinical and demographic information, such as number of antiepileptic drugs (AEDs), epilepsy duration and education level of patients and their caregivers. We enrolled in the study 50 consecutive adult patients attending the epilepsy clinic of "A. Manzoni" Hospital and their caregivers. Both patients and their caregivers were administered Hospital Anxiety and Depression Scale (HADS) and 36-item Short-Form Health Survey (SF-36). Anxiety, depression and quality of life values of both patients and their caregivers did not differ significantly from the normative samples. No statistically significant correlation between epilepsy duration and patients' and caregivers' psychological features was found. Patients which took more than one AED reported lower values of "Vitality" (p <.05) and "Social Functioning" (p <.05) than their own caregivers. Caregivers with higher education level presented lower "Vitality" values than caregivers with lower education level (p <.05). Patients with pharmacoresistant seizures reported lower values of "Mental Health" than patients with non-pharmacoresistant seizures (p <.05). In this context, the role of coping mechanisms by patients and caregivers may explain apparently unexpected findings and suggests that strategies aimed at reinforcing them may be effective in selected cases. Therefore, while the severity of epilepsy may have an impact on the psychological state of adult patients with epilepsy and their caregivers, our results highlight the role of multidimensional determinants, including stigma. Further studies are needed to identify the factors related to epilepsy, patients, caregivers, treatments, and the environment that may be modifiable in order to improve self-perceived QoL. Copyright © 2017 Elsevier Inc. All rights reserved.

Breakdown in the temporal and spatial organization of spontaneous brain activity during general anesthesia.

PubMed

Zhang, Jianfeng; Huang, Zirui; Chen, Yali; Zhang, Jun; Ghinda, Diana; Nikolova, Yuliya; Wu, Jinsong; Xu, Jianghui; Bai, Wenjie; Mao, Ying; Yang, Zhong; Duncan, Niall; Qin, Pengmin; Wang, Hao; Chen, Bing; Weng, Xuchu; Northoff, Georg

2018-05-01

Which temporal features that can characterize different brain states (i.e., consciousness or unconsciousness) is a fundamental question in the neuroscience of consciousness. Using resting-state functional magnetic resonance imaging (rs-fMRI), we investigated the spatial patterns of two temporal features: the long-range temporal correlations (LRTCs), measured by power-law exponent (PLE), and temporal variability, measured by standard deviation (SD) during wakefulness and anesthetic-induced unconsciousness. We found that both PLE and SD showed global reductions across the whole brain during anesthetic state comparing to wakefulness. Importantly, the relationship between PLE and SD was altered in anesthetic state, in terms of a spatial "decoupling." This decoupling was mainly driven by a spatial pattern alteration of the PLE, rather than the SD, in the anesthetic state. Our results suggest differential physiological grounds of PLE and SD and highlight the functional importance of the topographical organization of LRTCs in maintaining an optimal spatiotemporal configuration of the neural dynamics during normal level of consciousness. The central role of the spatial distribution of LRTCs, reflecting temporo-spatial nestedness, may support the recently introduced temporo-spatial theory of consciousness (TTC). © 2018 Wiley Periodicals, Inc.

Dynamic conductivity modified by impurity resonant states in doping three-dimensional Dirac semimetals

NASA Astrophysics Data System (ADS)

Li, Shuai; Wang, Chen; Zheng, Shi-Han; Wang, Rui-Qiang; Li, Jun; Yang, Mou

2018-04-01

The impurity effect is studied in three-dimensional Dirac semimetals in the framework of a T-matrix method to consider the multiple scattering events of Dirac electrons off impurities. It has been found that a strong impurity potential can significantly restructure the energy dispersion and the density of states of Dirac electrons. An impurity-induced resonant state emerges and significantly modifies the pristine optical response. It is shown that the impurity state disturbs the common longitudinal optical conductivity by creating either an optical conductivity peak or double absorption jumps, depending on the relative position of the impurity band and the Fermi level. More importantly, these conductivity features appear in the forbidden region between the Drude and interband transition, completely or partially filling the Pauli block region of optical response. The underlying physics is that the appearance of resonance states as well as the broadening of the bands leads to a more complicated selection rule for the optical transitions, making it possible to excite new electron-hole pairs in the forbidden region. These features in optical conductivity provide valuable information to understand the impurity behaviors in 3D Dirac materials.

Self-balancing feature of Lithium-Sulfur batteries

NASA Astrophysics Data System (ADS)

Knap, Vaclav; Stroe, Daniel-Ioan; Christensen, Andreas E.; Propp, Karsten; Fotouhi, Abbas; Auger, Daniel J.; Schaltz, Erik; Teodorescu, Remus

2017-12-01

The Li-S batteries are a prospective battery technology, which despite to its currently remaining drawbacks offers useable performance and interesting features. The polysulfide shuttle mechanism, a characteristic phenomenon for the Li-S batteries, causes a significant self-discharge at higher state-of-charge (SOC) levels, which leads to the energy dissipation of cells with higher charge. In an operation of series-connected Li-S cells, the shuttle mechanism results into a self-balancing effect which is studied here. A model for prediction of the self-balancing effect is proposed in this work and it is validated by experiments. Our results confirm the self-balancing feature of Li-S cells and illustrate their dependence on various conditions such as temperature, charging limits and idling time at high SOC.

Fusing visual and behavioral cues for modeling user experience in games.

PubMed

Shaker, Noor; Asteriadis, Stylianos; Yannakakis, Georgios N; Karpouzis, Kostas

2013-12-01

Estimating affective and cognitive states in conditions of rich human-computer interaction, such as in games, is a field of growing academic and commercial interest. Entertainment and serious games can benefit from recent advances in the field as, having access to predictors of the current state of the player (or learner) can provide useful information for feeding adaptation mechanisms that aim to maximize engagement or learning effects. In this paper, we introduce a large data corpus derived from 58 participants that play the popular Super Mario Bros platform game and attempt to create accurate models of player experience for this game genre. Within the view of the current research, features extracted both from player gameplay behavior and game levels, and player visual characteristics have been used as potential indicators of reported affect expressed as pairwise preferences between different game sessions. Using neuroevolutionary preference learning and automatic feature selection, highly accurate models of reported engagement, frustration, and challenge are constructed (model accuracies reach 91%, 92%, and 88% for engagement, frustration, and challenge, respectively). As a step further, the derived player experience models can be used to personalize the game level to desired levels of engagement, frustration, and challenge as game content is mapped to player experience through the behavioral and expressivity patterns of each player.

Explosive Cyclogenesis Over the Eastern United States.

NASA Astrophysics Data System (ADS)

MacDonald, Bruce Calvin

Cases of explosive cyclogenesis occurring over the east central United States are identified and analyzed. Other selected cases of weak or nonintensifying cyclones over the same area are identified and studied for comparative purposes. Signatures of explosively deepening cyclones (bombs) are derived from the analyses, including vertical profiles of vorticity, divergence, and latent heating, and also the relative importance of terms in the vorticity tendency equation and the relative importance of convective and stable latent heating. Composite analyses for the differing phases of bomb development and for regular cyclones are presented. Analyses of individual cases reveal the importance of a low-level jet streak, low-level moisture content, and moisture gradients in the lower troposphere. A numerical model is used to further examine the important processes in explosive cyclogenesis. A mesoscale feature is introduced to improve the prediction of sea -level pressure. This feature is based on the tendency of the large scale height field and vorticity field to adjust concurrently at each time step. The model is also used to provide air parcel trajectories to indicate the importance of parcels with high vorticity and moisture content as an ingredient in explosive cyclogenesis. Sensitivity studies are carried out with the model in order to determine the effect of changes in the initial vorticity and moisture field on cyclogenesis.

Fermi-level tuning of the Dirac surface state in (Bi1-x Sb x )2Se3 thin films

NASA Astrophysics Data System (ADS)

Satake, Yosuke; Shiogai, Junichi; Takane, Daichi; Yamada, Keiko; Fujiwara, Kohei; Souma, Seigo; Sato, Takafumi; Takahashi, Takashi; Tsukazaki, Atsushi

2018-02-01

We report on the electronic states and the transport properties of three-dimensional topological insulator (Bi1-x Sb x )2Se3 ternary alloy thin films grown on an isostructural Bi2Se3 buffer layer on InP substrates. By angle-resolved photoemission spectroscopy, we clearly detected Dirac surface states with a large bulk band gap of 0.2-0.3 eV in the (Bi1-x Sb x )2Se3 film with x  =  0.70. In addition, we observed by Hall effect measurements that the dominant charge carrier converts from electron (n-type) to hole (p-type) at around x  =  0.7, indicating that the Fermi level can be controlled across the Dirac point. Indeed, the carrier transport was shown to be governed by Dirac surface state in 0.63  ⩽  x  ⩽  0.75. These features suggest that Fermi-level tunable (Bi1-x Sb x )2Se3-based heterostructures provide a platform for extracting exotic topological phenomena.

Magneto-electronic properties of graphene nanoribbons in the spatially modulated electric field

NASA Astrophysics Data System (ADS)

Chen, S. C.; Wang, T. S.; Lee, C. H.; Lin, M. F.

2008-09-01

The Peierls tight-binding model with the nearest-neighbor interactions is used to calculate the magneto-electronic structure of graphene nanoribbons under a spatially modulated electric field along the y-axis. A uniform perpendicular magnetic field could make energy dispersions change into the quasi-Landau levels. Such levels are composed of the dispersionless and parabolic energy bands. A spatially modulated electric field would further induce a lot of oscillating parabolic bands with several band-edge states. It drastically modifies energy dispersions, alters subband spacings, destroys symmetry of energy spectrum about k=0, and changes features of band-edge states (number and energy). The above-mentioned magneto-electronic structures are directly reflected in density of states (DOS). The modulation effect changes shape, number, positions, and intensities of peaks in DOS. The predicted result could be tested by the optical measurements.

Formulation of the relativistic quantum Hall effect and parity anomaly

NASA Astrophysics Data System (ADS)

Yonaga, Kouki; Hasebe, Kazuki; Shibata, Naokazu

2016-06-01

We present a relativistic formulation of the quantum Hall effect on Haldane sphere. An explicit form of the pseudopotential is derived for the relativistic quantum Hall effect with/without mass term. We clarify particular features of the relativistic quantum Hall states with the use of the exact diagonalization study of the pseudopotential Hamiltonian. Physical effects of the mass term to the relativistic quantum Hall states are investigated in detail. The mass term acts as an interpolating parameter between the relativistic and nonrelativistic quantum Hall effects. It is pointed out that the mass term unevenly affects the many-body physics of the positive and negative Landau levels as a manifestation of the "parity anomaly." In particular, we explicitly demonstrate the instability of the Laughlin state of the positive first relativistic Landau level with the reduction of the charge gap.

Full-dimensional quantum mechanics calculations for the spectroscopic characterization of the isomerization transition states of HOCO/DOCO systems.

PubMed

Ma, Dandan; Ren, Haisheng; Ma, Jianyi

2018-02-14

Full-dimensional quantum mechanics calculations were performed to determine the vibrational energy levels of HOCO and DOCO based on an accurate potential energy surface. Almost all of the vibrational energy levels up to 3500 cm -1 from the vibrational ground state were assigned, and the calculated energy levels in this work are well in agreement with the reported results by Bowman. The corresponding full dimensional wavefunctions present some special features. When the energy level approaches the barrier height, the trans-HOCO and cis-HOCO states strongly couple through tunneling interactions, and the tunneling interaction and Fermi resonance were observed in the DOCO system. The energy level patterns of trans-HOCO, cis-HOCO and trans-DOCO provide a reasonable fitted barrier height using the fitting formula of Field et al., however, a discrepancy exists for the cis-DOCO species which is considered as a random event. Our full-dimensional calculations give positive evidence for the accuracy of the spectroscopic characterization model of the isomerization transition state reported by Field et al., which was developed from one-dimensional model systems. Furthermore, the special case of cis-DOCO in this work means that the isotopic substitution can solve the problem of the accidental failure of Field's spectroscopic characterization model.

Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

PubMed Central

Hu, Shiqiang; Zhang, Huanlong; Luo, Lingkun

2014-01-01

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance. PMID:25105164

Log-Gabor Weber descriptor for face recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Sang, Nong; Gao, Changxin

2015-09-01

The Log-Gabor transform, which is suitable for analyzing gradually changing data such as in iris and face images, has been widely used in image processing, pattern recognition, and computer vision. In most cases, only the magnitude or phase information of the Log-Gabor transform is considered. However, the complementary effect taken by combining magnitude and phase information simultaneously for an image-feature extraction problem has not been systematically explored in the existing works. We propose a local image descriptor for face recognition, called Log-Gabor Weber descriptor (LGWD). The novelty of our LGWD is twofold: (1) to fully utilize the information from the magnitude or phase feature of multiscale and orientation Log-Gabor transform, we apply the Weber local binary pattern operator to each transform response. (2) The encoded Log-Gabor magnitude and phase information are fused at the feature level by utilizing kernel canonical correlation analysis strategy, considering that feature level information fusion is effective when the modalities are correlated. Experimental results on the AR, Extended Yale B, and UMIST face databases, compared with those available from recent experiments reported in the literature, show that our descriptor yields a better performance than state-of-the art methods.

Automatic Authorship Detection Using Textual Patterns Extracted from Integrated Syntactic Graphs

PubMed Central

Gómez-Adorno, Helena; Sidorov, Grigori; Pinto, David; Vilariño, Darnes; Gelbukh, Alexander

2016-01-01

We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution. PMID:27589740

EEG-based workload estimation across affective contexts

PubMed Central

Mühl, Christian; Jeunet, Camille; Lotte, Fabien

2014-01-01

Workload estimation from electroencephalographic signals (EEG) offers a highly sensitive tool to adapt the human–computer interaction to the user state. To create systems that reliably work in the complexity of the real world, a robustness against contextual changes (e.g., mood), has to be achieved. To study the resilience of state-of-the-art EEG-based workload classification against stress we devise a novel experimental protocol, in which we manipulated the affective context (stressful/non-stressful) while the participant solved a task with two workload levels. We recorded self-ratings, behavior, and physiology from 24 participants to validate the protocol. We test the capability of different, subject-specific workload classifiers using either frequency-domain, time-domain, or both feature varieties to generalize across contexts. We show that the classifiers are able to transfer between affective contexts, though performance suffers independent of the used feature domain. However, cross-context training is a simple and powerful remedy allowing the extraction of features in all studied feature varieties that are more resilient to task-unrelated variations in signal characteristics. Especially for frequency-domain features, across-context training is leading to a performance comparable to within-context training and testing. We discuss the significance of the result for neurophysiology-based workload detection in particular and for the construction of reliable passive brain–computer interfaces in general. PMID:24971046

Deep donor state of the copper acceptor as a source of green luminescence in ZnO

NASA Astrophysics Data System (ADS)

Lyons, J. L.; Alkauskas, A.; Janotti, A.; Van de Walle, C. G.

2017-07-01

Copper impurities have long been linked with green luminescence (GL) in ZnO. Copper is known to introduce an acceptor level close to the conduction band of ZnO, and the GL has conventionally been attributed to transitions involving an excited state which localizes holes on neighboring oxygen atoms. To date, a theoretical description of the optical properties of such deep centers has been difficult to achieve due to the limitations of functionals in the density functional theory. Here, we employ a screened hybrid density functional to calculate the properties of Cu in ZnO. In agreement with the experiment, we find that CuZn features an acceptor level near the conduction band of ZnO. However, we find that CuZn also gives rise to a deep donor level 0.46 eV above the valence band of ZnO; the calculated optical transitions involving this state agree well with the GL observed in ZnO:Cu.

Diamond /111/ studied by electron energy loss spectroscopy in the characteristic loss region

NASA Technical Reports Server (NTRS)

Pepper, S. V.

1982-01-01

Unoccupied surface states on diamond (111) annealed at greater than 900 C are studied by electron energy loss spectroscopy with valence band excitation. A feature found at 2.1 eV loss energy is attributed to an excitation from occupied surface states into unoccupied surface states of energy within the bulk band gap. A surface band gap of approximately 1 eV is estimated. This result supports a previous suggestion for unoccupied band gap states based on core level energy loss spectroscopy. Using the valence band excitation energy loss spectrosocpy, it is also suggested that hydrogen is removed from the as-polished diamond surface by a Menzel-Gomer-Redhead mechanism.

Programs That Work, from the Promising Practices Network on Children, Families and Communities. RAND Tool

ERIC Educational Resources Information Center

Kilburn, M. Rebecca, Ed.

2014-01-01

The Promising Practices Network (PPN) on Children, Families and Communities (www.promisingpractices.net) began as a partnership between four state-level organizations that help public and private organizations improve the well-being of children and families. The PPN website, archived in June 2014, featured summaries of programs and practices that…

Organizational Principles and Content of Early Foreign Language Learning in the USA

ERIC Educational Resources Information Center

Biletska, Iryna

2015-01-01

According to the results of leading American scientists that convincingly demonstrate the effectiveness and necessity of early foreign language learning the features of teaching foreign languages at elementary level in the United States have been analyzed. It has been found out that the US government is working on the improvement of foreign…

Managed and natural landscapes: what do people like?

Treesearch

Arthur W. Magill

1992-01-01

Visitors to wildland areas of the United States see an untold variety of natural and manmade features that comprise our national landscape. Efforts to assess public perceptions and concerns about the land and its management led to development of sensitivity levels that were presumed to be a measure of viewer concern for what they saw in the landscape. Sensitivity...

Race for Results: Building a Path to Opportunity for All Children

ERIC Educational Resources Information Center

Annie E. Casey Foundation, 2014

2014-01-01

In this policy report, the Annie E. Casey Foundation explores the intersection of kids, race, and opportunity. The report features the new Race for Results index, which compares how children are progressing on key milestones across racial and ethnic groups at the national and state level. The index is based on 12 indicators that measure a…

Leptin, Insulin, and Cinnamon Polyphenols Attenuate Glial Swelling and Mitochondrial Dysfunction in Ischemic Injury

USDA-ARS?s Scientific Manuscript database

Obesity is a major risk factor for stroke, and tissue injury following a stroke may be more severe in the obese. A key feature of obesity is increased serum levels of obesity-related hormones including leptin and insulin, indicating a state of resistance to these hormones. Insulin resistance is gen...

Clinical features distinguishing grief from depressive episodes: A qualitative analysis.

PubMed

Parker, Gordon; McCraw, Stacey; Paterson, Amelia

2015-05-01

The independence or interdependence of grief and major depression has been keenly argued in relation to recent DSM definitions and encouraged the current study. We report a phenomenological study seeking to identify the experiential and phenomenological differences between depression and grief as judged qualitatively by those who had experienced clinical (n=125) or non-clinical depressive states (n=28). Analyses involving the whole sample indicated that, in contrast to grief, depression involved feelings of hopelessness and helplessness, being endless and was associated with a lack of control, having an internal self-focus impacting on self-esteem, being more severe and stressful, being marked by physical symptoms and often lacking a justifiable cause. Grief was distinguished from depression by the individual viewing their experience as natural and to be expected, a consequence of a loss, and with an external focus (i.e. the loss of the other). Some identified differences may have reflected the impact of depressive "type" (e.g. melancholia) rather than depression per se, and argue for a two-tiered model differentiating normative depressive and grief states at their base level and then "clinical" depressive and 'pathological' grief states by their associated clinical features. Comparative analyses between the clinical and non-clinical groups were limited by the latter sub-set being few in number. The provision of definitions may have shaped subjects׳ nominated differentiating features. The study identified a distinct number of phenomenological and clinical differences between grief and depression and few shared features, but more importantly, argued for the development of a two-tiered model defining both base states and clinical expressions. Copyright © 2015 Elsevier B.V. All rights reserved.

Universal test fixture for monolithic mm-wave integrated circuits calibrated with an augmented TRD algorithm

NASA Technical Reports Server (NTRS)

Romanofsky, Robert R.; Shalkhauser, Kurt A.

1989-01-01

The design and evaluation of a novel fixturing technique for characterizing millimeter wave solid state devices is presented. The technique utilizes a cosine-tapered ridge guide fixture and a one-tier de-embedding procedure to produce accurate and repeatable device level data. Advanced features of this technique include nondestructive testing, full waveguide bandwidth operation, universality of application, and rapid, yet repeatable, chip-level characterization. In addition, only one set of calibration standards is required regardless of the device geometry.

Simulation of Etching in Chlorine Discharges Using an Integrated Feature Evolution-Plasma Model

NASA Technical Reports Server (NTRS)

Hwang, Helen H.; Bose, Deepak; Govindan, T. R.; Meyyappan, M.; Biegel, Bryan (Technical Monitor)

2002-01-01

To better utilize its vast collection of heterogeneous resources that are geographically distributed across the United States, NASA is constructing a computational grid called the Information Power Grid (IPG). This paper describes various tools and techniques that we are developing to measure and improve the performance of a broad class of NASA applications when run on the IPG. In particular, we are investigating the areas of grid benchmarking, grid monitoring, user-level application scheduling, and decentralized system-level scheduling.

Web technologies for rapid assessment of pollution of the atmosphere of the industrial city

NASA Astrophysics Data System (ADS)

Shaparev, N.; Tokarev, A.; Yakubailik, O.; Soldatov, A.

2018-05-01

The functionality, architectural features, the user interface of the geoinformation web-system of environmental monitoring of Krasnoyarsk is discussed. This system is created in service-oriented architecture. Data collection from the automated stations to monitor the state of atmospheric air has been implemented. An original device to measure the level of contamination of the atmosphere by fine dust PM2.5 has developed. Assessment of the level of air pollution is based on the quality index AQI atmosphere.

Neural connectivity in Internet gaming disorder and alcohol use disorder: A resting-state EEG coherence study.

PubMed

Park, Su Mi; Lee, Ji Yoon; Kim, Yeon Jin; Lee, Jun-Young; Jung, Hee Yeon; Sohn, Bo Kyung; Kim, Dai Jin; Choi, Jung-Seok

2017-05-02

The present study compared neural connectivity and the level of phasic synchronization between neural populations in patients with Internet gaming disorder (IGD), patients with alcohol use disorder (AUD), and healthy controls (HCs) using resting-state electroencephalography (EEG) coherence analyses. For this study, 92 adult males were categorized into three groups: IGD (n = 30), AUD (n = 30), and HC (n = 32). The IGD group exhibited increased intrahemispheric gamma (30-40 Hz) coherence compared to the AUD and HC groups regardless of psychological features (e.g., depression, anxiety, and impulsivity) and right fronto-central gamma coherence positively predicted the scores of the Internet addiction test in all groups. In contrast, the AUD group showed marginal tendency of increased intrahemispheric theta (4-8 Hz) coherence relative to the HC group and this was dependent on the psychological features. The present findings indicate that patients with IGD and AUD exhibit different neurophysiological patterns of brain connectivity and that an increase in the fast phasic synchrony of gamma coherence might be a core neurophysiological feature of IGD.

Detecting features in the dark energy equation of state: a wavelet approach

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

Hojjati, Alireza; Pogosian, Levon; Zhao, Gong-Bo, E-mail: alireza_hojjati@sfu.ca, E-mail: levon@sfu.ca, E-mail: gong-bo.zhao@port.ac.uk

2010-04-01

We study the utility of wavelets for detecting the redshift evolution of the dark energy equation of state w(z) from the combination of supernovae (SNe), CMB and BAO data. We show that local features in w, such as bumps, can be detected efficiently using wavelets. To demonstrate, we first generate a mock supernovae data sample for a SNAP-like survey with a bump feature in w(z) hidden in, then successfully discover it by performing a blind wavelet analysis. We also apply our method to analyze the recently released ''Constitution'' SNe data, combined with WMAP and BAO from SDSS, and find weakmore » hints of dark energy dynamics. Namely, we find that models with w(z) < −1 for 0.2 < z < 0.5, and w(z) > −1 for 0.5 < z < 1, are mildly favored at 95% confidence level. This is in good agreement with several recent studies using other methods, such as redshift binning with principal component analysis (PCA) (e.g. Zhao and Zhang, arXiv: 0908.1568)« less

An Assessment of State-of-the-Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval

NASA Astrophysics Data System (ADS)

Skourup, Henriette; Farrell, Sinéad Louise; Hendricks, Stefan; Ricker, Robert; Armitage, Thomas W. K.; Ridout, Andy; Andersen, Ole Baltazar; Haas, Christian; Baker, Steven

2017-11-01

State-of-the-art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high-frequency domain, primarily due to unresolved gravity features, can result in errors in the estimated along-track freeboard. These errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer-term, multisensor oceanographic time series of sea level change in the Arctic. This study focuses on an assessment of five state-of-the-art Arctic MSS models (UCL13/04 and DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, intersatellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat-2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03-0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimeters in parts of the Arctic. While the maximum observed freeboard difference found in the central Arctic was 0.59 m (UCL13 MSS minus EGM2008 GGM), the standard deviation in freeboard differences is 0.03-0.06 m.

On the important role of the anti-Jahn-Teller effect in underdoped cuprate superconductors

NASA Astrophysics Data System (ADS)

Kamimura, Hiroshi; Matsuno, Shunichi; Mizokawa, Takashi; Sasaoka, Kenji; Shiraishi, Kenji; Ushio, Hideki

2013-04-01

In this paper it is shown that the "anti-Jahn-Teller effect" plays an essential role in giving rise to a small Fermi surface of Fermi pockets above Tc and d-wave superconductivity below Tc in underdoped cuprates. In the first part of the present paper, we review the latest developments of the model proposed by Kamimura and Suwa, which bears important characteristics born from the interplay of Jahn-Teller Physics and Mott Physics. It is shown that the feature of Fermi surfaces in underdoped LSCO is the Fermi pockets in the nodal region constructed by doped holes under the coexistence of a metallic state and of the local antiferromagnetic order. In the antinodal region in the momentum space, there are no Fermi surfaces. Then it is discussed that the phonon-involved mechanism based on the Kamimura-Suwa model leads to the d-wave superconductivity. In particular, it is shown that the origin of strong electron-phonon interactions in cuprates is due to the anti-Jahn-Teller effect. In the second part a recent theoretical result on the energy distribution curves (EDCs) of angle-resolved photoemission spectroscopy (ARPES) below Tc is discussed. It is shown that the feature of ARPES profiles of underdoped cuprates consists of a coherent peak in the nodal region and the real transitions of photoexcited electrons from occupied states below the Fermi level to a free-electron state above the vacuum level in the antinodal region, where the latter transitions form a broad hump. From this feature, the origin of the two distinct gaps observed by ARPES is elucidated without introducing the concept of the pseudogap. Finally, a remark is made on the phase diagram of underdoped cuprates.

Can state-of-the-art HVS-based objective image quality criteria be used for image reconstruction techniques based on ROI analysis?

NASA Astrophysics Data System (ADS)

Dostal, P.; Krasula, L.; Klima, M.

2012-06-01

Various image processing techniques in multimedia technology are optimized using visual attention feature of the human visual system. Spatial non-uniformity causes that different locations in an image are of different importance in terms of perception of the image. In other words, the perceived image quality depends mainly on the quality of important locations known as regions of interest. The performance of such techniques is measured by subjective evaluation or objective image quality criteria. Many state-of-the-art objective metrics are based on HVS properties; SSIM, MS-SSIM based on image structural information, VIF based on the information that human brain can ideally gain from the reference image or FSIM utilizing the low-level features to assign the different importance to each location in the image. But still none of these objective metrics utilize the analysis of regions of interest. We solve the question if these objective metrics can be used for effective evaluation of images reconstructed by processing techniques based on ROI analysis utilizing high-level features. In this paper authors show that the state-of-the-art objective metrics do not correlate well with subjective evaluation while the demosaicing based on ROI analysis is used for reconstruction. The ROI were computed from "ground truth" visual attention data. The algorithm combining two known demosaicing techniques on the basis of ROI location is proposed to reconstruct the ROI in fine quality while the rest of image is reconstructed with low quality. The color image reconstructed by this ROI approach was compared with selected demosaicing techniques by objective criteria and subjective testing. The qualitative comparison of the objective and subjective results indicates that the state-of-the-art objective metrics are still not suitable for evaluation image processing techniques based on ROI analysis and new criteria is demanded.

Monitoring of Non-cigarette Tobacco Use using Google Trends

PubMed Central

Cavazos-Rehg, Patricia A.; Krauss, Melissa J.; Spitznagel, Edward L.; Lowery, Ashley; Grucza, Richard A.; Chaloupka, Frank J.; Bierut, Laura Jean

2014-01-01

Background Google Trends is an innovative monitoring system with unique potential to monitor and predict important phenomena that may be occurring at a population-level. We sought to validate whether Google Trends can additionally detect regional trends in youth and adult tobacco use. Methods We compared 2011 Google Trends relative search volume data for cigars, cigarillos, little cigars and smokeless tobacco with state prevalence of youth (grades 9–12) and adult (age 18 and older) use of these products using data from the 2011 United States state-level Youth Risk Behaviors Surveillance System and the 2010–2011 United States National Survey on Drug Use and Health (NSDUH), respectively. We used the Pearson correlation coefficient to measure the associations. Results We found significant positive correlations between state Google Trends cigar relative search volume and prevalence of cigar use among youth (r=0.39, R-square=0.154, p= .018) and adults (r=0.49, R-square=0.243, p<.001). Similarly, we found that the correlations between state Google trends smokeless tobacco relative search volume and prevalence of smokeless tobacco use among youth and adults were both positive and significant (r=0.46, R-square=0.209, p=.003 and r=0.48, R-square=0.226, p<.001, respectively). Conclusion The results of this study validate that Google Trends has the potential to be a valuable monitoring tool for tobacco use. The near real-time monitoring features of Google Trends may complement traditional surveillance methods and lead to faster and more convenient monitoring of emerging trends in tobacco use. What this paper adds Efforts to promptly track tobacco use at a population-level are an important endeavor that could assist key stakeholders with making informed decisions about prevention plans and policies. Our findings establish the validity of Google Trends as an indicator of cigar and smokeless tobacco use by demonstrating that states with higher relative volumes of Google searches for cigar and smokeless tobacco products have higher prevalence of tobacco use of these products. The near real-time monitoring features of Google Trends could be used to supplement traditional survey methods to obtain deeper insight about youth and adult tobacco use. PMID:24500269

A state-based approach to trend recognition and failure prediction for the Space Station Freedom

NASA Technical Reports Server (NTRS)

Nelson, Kyle S.; Hadden, George D.

1992-01-01

A state-based reasoning approach to trend recognition and failure prediction for the Altitude Determination, and Control System (ADCS) of the Space Station Freedom (SSF) is described. The problem domain is characterized by features (e.g., trends and impending failures) that develop over a variety of time spans, anywhere from several minutes to several years. Our state-based reasoning approach, coupled with intelligent data screening, allows features to be tracked as they develop in a time-dependent manner. That is, each state machine has the ability to encode a time frame for the feature it detects. As features are detected, they are recorded and can be used as input to other state machines, creating a hierarchical feature recognition scheme. Furthermore, each machine can operate independently of the others, allowing simultaneous tracking of features. State-based reasoning was implemented in the trend recognition and the prognostic modules of a prototype Space Station Freedom Maintenance and Diagnostic System (SSFMDS) developed at Honeywell's Systems and Research Center.

Color-selective attention need not be mediated by spatial attention.

PubMed

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

2009-06-08

It is well-established that attention can select stimuli for preferential processing on the basis of non-spatial features such as color, orientation, or direction of motion. Evidence is mixed, however, as to whether feature-selective attention acts by increasing the signal strength of to-be-attended features irrespective of their spatial locations or whether it acts by guiding the spotlight of spatial attention to locations containing the relevant feature. To address this question, we designed a task in which feature-selective attention could not be mediated by spatial selection. Participants observed a display of intermingled dots of two colors, which rapidly and unpredictably changed positions, with the task of detecting brief intervals of reduced luminance of 20% of the dots of one or the other color. Both behavioral indices and electrophysiological measures of steady-state visual evoked potentials showed selectively enhanced processing of the attended-color items. The results demonstrate that feature-selective attention produces a sensory gain enhancement at early levels of the visual cortex that occurs without mediation by spatial attention.

Single season changes in resting state network power and the connectivity between regions distinguish head impact exposure level in high school and youth football players

NASA Astrophysics Data System (ADS)

Murugesan, Gowtham; Saghafi, Behrouz; Davenport, Elizabeth; Wagner, Ben; Urban, Jillian; Kelley, Mireille; Jones, Derek; Powers, Alex; Whitlow, Christopher; Stitzel, Joel; Maldjian, Joseph; Montillo, Albert

2018-02-01

The effect of repetitive sub-concussive head impact exposure in contact sports like American football on brain health is poorly understood, especially in the understudied populations of youth and high school players. These players, aged 9-18 years old may be particularly susceptible to impact exposure as their brains are undergoing rapid maturation. This study helps fill the void by quantifying the association between head impact exposure and functional connectivity, an important aspect of brain health measurable via resting-state fMRI (rs-fMRI). The contributions of this paper are three fold. First, the data from two separate studies (youth and high school) are combined to form a high-powered analysis with 60 players. These players experience head acceleration within overlapping impact exposure making their combination particularly appropriate. Second, multiple features are extracted from rs-fMRI and tested for their association with impact exposure. One type of feature is the power spectral density decomposition of intrinsic, spatially distributed networks extracted via independent components analysis (ICA). Another feature type is the functional connectivity between brain regions known often associated with mild traumatic brain injury (mTBI). Third, multiple supervised machine learning algorithms are evaluated for their stability and predictive accuracy in a low bias, nested cross-validation modeling framework. Each classifier predicts whether a player sustained low or high levels of head impact exposure. The nested cross validation reveals similarly high classification performance across the feature types, and the Support Vector, Extremely randomized trees, and Gradboost classifiers achieve F1-score up to 75%.

Differences between radiosonde and dropsonde temperature profiles over the Arctic Ocean

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

Skony, S.M.; Kahl, J.D.W.; Zaitseva, N.A.

1994-10-01

The boundary layer structure measured by 402 pairs of approximately collocated radiosonde and dropsonde temperature profiles over the Arctic Ocean during the period 1957-1961 is examined. The radiosonde profiles were obtained at the Russian drifting ice camps `North Pole 7` and `North Pole 8,` and the dropsonde profiles were measured during the United States Air Force `Ptarmigan` series of weather reconnaissance flights. The boundary layer structure is characterized by the features of the low-level tropospheric temperature inversion. The results indicate that the dropsonde soundings, although containing relatively few measurement levels, contain sufficient vertical resolution to characterize the temperature inversion. Systematicmore » differences were noted in wintertime inversion features and near-surface temperatures as measured by dropsondes and radiosondes. These differences are attributed to contrasting temperature lag errors accompanying ascending and descending sensors.« less

Insulin resistance and the metabolism of branched-chain amino acids in humans.

PubMed

Adeva, María M; Calviño, Jesús; Souto, Gema; Donapetry, Cristóbal

2012-07-01

Peripheral resistance to insulin action is the major mechanism causing the metabolic syndrome and eventually type 2 diabetes mellitus. The metabolic derangement associated with insulin resistance is extensive and not restricted to carbohydrates. The branched-chain amino acids (BCAAs) are particularly responsive to the inhibitory insulin action on amino acid release by skeletal muscle and their metabolism is profoundly altered in conditions featuring insulin resistance, insulin deficiency, or both. Obesity, the metabolic syndrome and diabetes mellitus display a gradual increase in the plasma concentration of BCAAs, from the obesity-related low-grade insulin-resistant state to the severe deficiency of insulin action in diabetes ketoacidosis. Obesity-associated hyperinsulinemia succeeds in maintaining near-normal or slightly elevated plasma concentration of BCAAs, despite the insulin-resistant state. The low circulating levels of insulin and/or the deeper insulin resistance occurring in diabetes mellitus are associated with more marked elevation in the plasma concentration of BCAAs. In diabetes ketoacidosis, the increase in plasma BCAAs is striking, returning to normal when adequate metabolic control is achieved. The metabolism of BCAAs is also disturbed in other situations typically featuring insulin resistance, including kidney and liver dysfunction. However, notwithstanding the insulin-resistant state, the plasma level of BCAAs in these conditions is lower than in healthy subjects, suggesting that these organs are involved in maintaining BCAAs blood concentration. The pathogenesis of the decreased BCAAs plasma level in kidney and liver dysfunction is unclear, but a decreased afflux of these amino acids into the blood stream has been observed.

The interaction of MnH(X 7Σ+) with He: Ab initio potential energy surface and bound states

NASA Astrophysics Data System (ADS)

Turpin, Florence; Halvick, Philippe; Stoecklin, Thierry

2010-06-01

The potential energy surface of the ground state of the He-MnH(X Σ7+) van der Waals complex is presented. Within the supermolecular approach of intermolecular energy calculations, a grid of ab initio points was computed at the multireference configuration interaction level using the aug-cc-pVQZ basis set for helium and hydrogen and the relativistic aug-cc-pVQZ-DK basis set for manganese. The potential energy surface was then fitted to a global analytical form which main features are discussed. As a first application of this potential energy surface, we present accurate calculations of bound energy levels of the H3e-MnH and H4e-MnH complexes.

The interaction of MnH(X 7Sigma+) with He: ab initio potential energy surface and bound states.

PubMed

Turpin, Florence; Halvick, Philippe; Stoecklin, Thierry

2010-06-07

The potential energy surface of the ground state of the He-MnH(X (7)Sigma(+)) van der Waals complex is presented. Within the supermolecular approach of intermolecular energy calculations, a grid of ab initio points was computed at the multireference configuration interaction level using the aug-cc-pVQZ basis set for helium and hydrogen and the relativistic aug-cc-pVQZ-DK basis set for manganese. The potential energy surface was then fitted to a global analytical form which main features are discussed. As a first application of this potential energy surface, we present accurate calculations of bound energy levels of the (3)He-MnH and (4)He-MnH complexes.

77 FR 840 - Pricing for 2012 Products Featuring $1 Coins

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-06

... DEPARTMENT OF THE TREASURY United States Mint Pricing for 2012 Products Featuring $1 Coins AGENCY: United States Mint, Department of the Treasury. ACTION: Notice. SUMMARY: The United States Mint is announcing 2012 pricing for products featuring $1 coins, as follows: Product Retail price Presidential $1...

Current signature sensor

NASA Technical Reports Server (NTRS)

Perotti, Jose M. (Inventor); Lucena, Angel (Inventor); Ihlefeld, Curtis (Inventor); Burns, Bradley (Inventor); Bassignani, Karin E. (Inventor)

2005-01-01

A solenoid health monitoring system uses a signal conditioner and controller assembly in one embodiment that includes analog circuitry and a DSP controller. The analog circuitry provides signal conditioning to the low-level raw signal coming from a signal acquisition assembly. Software running in a DSP analyzes the incoming data (recorded current signature) and determines the state of the solenoid whether it is energized, de-energized, or in a transitioning state. In one embodiment, the software identifies key features in the current signature during the transition phase and is able to determine the health of the solenoid.

Possible Peroxo State of the Dicopper Site of Particulate Methane Monooxygenase from Combined Quantum Mechanics and Molecular Mechanics Calculations.

PubMed

Itoyama, Shuhei; Doitomi, Kazuki; Kamachi, Takashi; Shiota, Yoshihito; Yoshizawa, Kazunari

2016-03-21

Enzymatic methane hydroxylation is proposed to efficiently occur at the dinuclear copper site of particulate methane monooxygenase (pMMO), which is an integral membrane metalloenzyme in methanotrophic bacteria. The resting state and a possible peroxo state of the dicopper active site of pMMO are discussed by using combined quantum mechanics and molecular mechanics calculations on the basis of reported X-ray crystal structures of the resting state of pMMO by Rosenzweig and co-workers. The dicopper site has a unique structure, in which one copper is coordinated by two histidine imidazoles and another is chelated by a histidine imidazole and primary amine of an N-terminal histidine. The resting state of the dicopper site is assignable to the mixed-valent Cu(I)Cu(II) state from a computed Cu-Cu distance of 2.62 Å from calculations at the B3LYP-D/TZVP level of theory. A μ-η(2):η(2)-peroxo-Cu(II)2 structure similar to those of hemocyanin and tyrosinase is reasonably obtained by using the resting state structure and dioxygen. Computed Cu-Cu and O-O distances are 3.63 and 1.46 Å, respectively, in the open-shell singlet state. Structural features of the dicopper peroxo species of pMMO are compared with those of hemocyanin and tyrosinase and synthetic dicopper model compounds. Optical features of the μ-η(2):η(2)-peroxo-Cu(II)2 state are calculated and analyzed with TD-DFT calculations.

Electronic Transport and Possible Superconductivity at Van Hove Singularities in Carbon Nanotubes.

PubMed

Yang, Y; Fedorov, G; Shafranjuk, S E; Klapwijk, T M; Cooper, B K; Lewis, R M; Lobb, C J; Barbara, P

2015-12-09

Van Hove singularities (VHSs) are a hallmark of reduced dimensionality, leading to a divergent density of states in one and two dimensions and predictions of new electronic properties when the Fermi energy is close to these divergences. In carbon nanotubes, VHSs mark the onset of new subbands. They are elusive in standard electronic transport characterization measurements because they do not typically appear as notable features and therefore their effect on the nanotube conductance is largely unexplored. Here we report conductance measurements of carbon nanotubes where VHSs are clearly revealed by interference patterns of the electronic wave functions, showing both a sharp increase of quantum capacitance, and a sharp reduction of energy level spacing, consistent with an upsurge of density of states. At VHSs, we also measure an anomalous increase of conductance below a temperature of about 30 K. We argue that this transport feature is consistent with the formation of Cooper pairs in the nanotube.

Characterization and Modeling of Nonfilamentary Ta/TaOx/TiO2/Ti Analog Synaptic Device

PubMed Central

Wang, Yu-Fen; Lin, Yen-Chuan; Wang, I-Ting; Lin, Tzu-Ping; Hou, Tuo-Hung

2015-01-01

A two-terminal analog synaptic device that precisely emulates biological synaptic features is expected to be a critical component for future hardware-based neuromorphic computing. Typical synaptic devices based on filamentary resistive switching face severe limitations on the implementation of concurrent inhibitory and excitatory synapses with low conductance and state fluctuation. For overcoming these limitations, we propose a Ta/TaOx/TiO2/Ti device with superior analog synaptic features. A physical simulation based on the homogeneous (nonfilamentary) barrier modulation induced by oxygen ion migration accurately reproduces various DC and AC evolutions of synaptic states, including the spike-timing-dependent plasticity and paired-pulse facilitation. Furthermore, a physics-based compact model for facilitating circuit-level design is proposed on the basis of the general definition of memristor devices. This comprehensive experimental and theoretical study of the promising electronic synapse can facilitate realizing large-scale neuromorphic systems. PMID:25955658

Symmetry in cold-to-hot and hot-to-cold valuation gaps.

PubMed

Fisher, Geoffrey; Rangel, Antonio

2014-01-01

Individuals commonly mispredict their future preferences when they make decisions in a visceral state different from their anticipated state at consumption. In the research reported here, we asked subjects to bid on different foods while exogenously varying their hunger levels at the time of decision and at the time of consumption. This procedure allowed us to test whether cold-to-hot and hot-to-cold gaps are symmetric in size and driven by similar mechanisms. We found that the effect size was symmetric: Hungry subjects overbid 20¢ for a snack they would eat later when they were satiated, and satiated subjects underbid 19¢ for a snack they would eat later when they were hungry. Furthermore, we found evidence that these gaps were driven by symmetric mechanisms that operate on the evaluation of visceral features of food, such as taste, as opposed to more cognitive features, such as healthiness.

Combining Video, Audio and Lexical Indicators of Affect in Spontaneous Conversation via Particle Filtering

PubMed Central

Savran, Arman; Cao, Houwei; Shah, Miraj; Nenkova, Ani; Verma, Ragini

2013-01-01

We present experiments on fusing facial video, audio and lexical indicators for affect estimation during dyadic conversations. We use temporal statistics of texture descriptors extracted from facial video, a combination of various acoustic features, and lexical features to create regression based affect estimators for each modality. The single modality regressors are then combined using particle filtering, by treating these independent regression outputs as measurements of the affect states in a Bayesian filtering framework, where previous observations provide prediction about the current state by means of learned affect dynamics. Tested on the Audio-visual Emotion Recognition Challenge dataset, our single modality estimators achieve substantially higher scores than the official baseline method for every dimension of affect. Our filtering-based multi-modality fusion achieves correlation performance of 0.344 (baseline: 0.136) and 0.280 (baseline: 0.096) for the fully continuous and word level sub challenges, respectively. PMID:25300451

Combining Video, Audio and Lexical Indicators of Affect in Spontaneous Conversation via Particle Filtering.

PubMed

Savran, Arman; Cao, Houwei; Shah, Miraj; Nenkova, Ani; Verma, Ragini

2012-01-01

We present experiments on fusing facial video, audio and lexical indicators for affect estimation during dyadic conversations. We use temporal statistics of texture descriptors extracted from facial video, a combination of various acoustic features, and lexical features to create regression based affect estimators for each modality. The single modality regressors are then combined using particle filtering, by treating these independent regression outputs as measurements of the affect states in a Bayesian filtering framework, where previous observations provide prediction about the current state by means of learned affect dynamics. Tested on the Audio-visual Emotion Recognition Challenge dataset, our single modality estimators achieve substantially higher scores than the official baseline method for every dimension of affect. Our filtering-based multi-modality fusion achieves correlation performance of 0.344 (baseline: 0.136) and 0.280 (baseline: 0.096) for the fully continuous and word level sub challenges, respectively.

Computerized lung cancer malignancy level analysis using 3D texture features

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Huang, Xia; Tseng, Tzu-Liang; Zhang, Jianying; Qian, Wei

2016-03-01

Based on the likelihood of malignancy, the nodules are classified into five different levels in Lung Image Database Consortium (LIDC) database. In this study, we tested the possibility of using threedimensional (3D) texture features to identify the malignancy level of each nodule. Five groups of features were implemented and tested on 172 nodules with confident malignancy levels from four radiologists. These five feature groups are: grey level co-occurrence matrix (GLCM) features, local binary pattern (LBP) features, scale-invariant feature transform (SIFT) features, steerable features, and wavelet features. Because of the high dimensionality of our proposed features, multidimensional scaling (MDS) was used for dimension reduction. RUSBoost was applied for our extracted features for classification, due to its advantages in handling imbalanced dataset. Each group of features and the final combined features were used to classify nodules highly suspicious for cancer (level 5) and moderately suspicious (level 4). The results showed that the area under the curve (AUC) and accuracy are 0.7659 and 0.8365 when using the finalized features. These features were also tested on differentiating benign and malignant cases, and the reported AUC and accuracy were 0.8901 and 0.9353.

Revised and extended level scheme of the doubly-odd nucleus {sup 188}Ir

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

Jungclaus, A.; Modamio, V.; Egido, J. L.

2008-02-15

High-spin states in the doubly odd Z=77 nucleus {sup 188}Ir were studied using the reaction {sup 186}W({sup 7}Li, 5n) at 59 MeV and the GASP spectrometer for {gamma}-ray detection. The level structures recently suggested to be built on the known 4.1(3) ms isomeric state of this nucleus have been considerably revised and extended and an isomer with a lifetime of 17.7(2) ns has been identified within the main decay sequence. In addition two rotational bands built on low spin states below the ms isomer have been observed for the first time. The basic features of the excitation scheme of {supmore » 188}Ir are discussed within the Hartree-Fock-Bogoliubov theory within the Lipkin-Nogami approach with the finite-range density-dependent Gogny force.« less

Appearance of the minority dz2 surface state and disappearance of the image-potential state: Criteria for clean Fe(001)

NASA Astrophysics Data System (ADS)

Eibl, Christian; Schmidt, Anke B.; Donath, Markus

2012-10-01

The unoccupied surface electronic structure of clean and oxidized Fe(001) was studied with spin-resolved inverse photoemission and target current spectroscopy. For the clean surface, we detected a dz2 surface state with minority spin character just above the Fermi level, while the image-potential surface state disappears. The opposite is observed for the ordered p(1×1)O/Fe(001) surface: the dz2-type surface state is quenched, while the image-potential state shows up as a pronounced feature. This behavior indicates enhanced surface reflectivity at the oxidized surface. The appearance and disappearance of specific unoccupied surface states prove to be decisive criteria for a clean Fe(001) surface. In addition, enhanced spin asymmetry in the unoccupied states is observed for the oxidized surface. Our results have implications for the use of clean and oxidized Fe(001) films as spin-polarization detectors.

Motion Pictures for the Study of India: A Guide to Classroom Films.

ERIC Educational Resources Information Center

Vestal, Theodore M.

After a three year review of films on India available in the United States, the Resource Center offered this guide to those motion pictures adjudged best for use in American classrooms. There are twelve documentary films and four commercial feature films included for use at any level of school, college, or university study: Child of the Streets; A…

Early and School-Age Care in Santa Monica: Current System, Policy Options, and Recommendations

ERIC Educational Resources Information Center

Pierson, Ashley; Karoly, Lynn A.; Zellman, Gail L.; Beckett, Megan K.

2014-01-01

The landscape of early learning and out-of-school-time programs in the City of Santa Monica is complex, with numerous providers and funding streams. This complexity reflects its evolution in response to changes in federal, state, and local priorities and initiatives. Future shifts in funding levels, program auspices, and other features are likely.…

Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network

PubMed Central

Tommasin, Silvia; Mascali, Daniele; Gili, Tommaso; Assan, Ibrahim Eid; Moraschi, Marta; Fratini, Michela; Wise, Richard G.; Macaluso, Emiliano; Mangia, Silvia; Giove, Federico

2017-01-01

Spontaneous low-frequency Blood-Oxygenation Level-Dependent (BOLD) signals acquired during resting state are characterized by spatial patterns of synchronous fluctuations, ultimately leading to the identification of robust brain networks. The resting-state brain networks, including the Default Mode Network (DMN), are demonstrated to persist during sustained task execution, but the exact features of task-related changes of network properties are still not well characterized. In this work we sought to examine in a group of 20 healthy volunteers (age 33 ± 6 years, 8 F/12 M) the relationship between changes of spectral and spatiotemporal features of one prominent resting-state network, namely the DMN, during the continuous execution of a working memory n-back task. We found that task execution impacted on both functional connectivity and amplitude of BOLD fluctuations within large parts of the DMN, but these changes correlated between each other only in a small area of the posterior cingulate. We conclude that combined analysis of multiple parameters related to connectivity, and their changes during the transition from resting state to continuous task execution, can contribute to a better understanding of how brain networks rearrange themselves in response to a task. PMID:28845420

Overt attention in natural scenes: objects dominate features.

PubMed

Stoll, Josef; Thrun, Michael; Nuthmann, Antje; Einhäuser, Wolfgang

2015-02-01

Whether overt attention in natural scenes is guided by object content or by low-level stimulus features has become a matter of intense debate. Experimental evidence seemed to indicate that once object locations in a scene are known, salience models provide little extra explanatory power. This approach has recently been criticized for using inadequate models of early salience; and indeed, state-of-the-art salience models outperform trivial object-based models that assume a uniform distribution of fixations on objects. Here we propose to use object-based models that take a preferred viewing location (PVL) close to the centre of objects into account. In experiment 1, we demonstrate that, when including this comparably subtle modification, object-based models again are at par with state-of-the-art salience models in predicting fixations in natural scenes. One possible interpretation of these results is that objects rather than early salience dominate attentional guidance. In this view, early-salience models predict fixations through the correlation of their features with object locations. To test this hypothesis directly, in two additional experiments we reduced low-level salience in image areas of high object content. For these modified stimuli, the object-based model predicted fixations significantly better than early salience. This finding held in an object-naming task (experiment 2) and a free-viewing task (experiment 3). These results provide further evidence for object-based fixation selection--and by inference object-based attentional guidance--in natural scenes. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2016-06-01

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

Resonant-phonon-assisted THz quantum cascade lasers with metal-metal waveguides.

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

Callebaut, Hans; Kohen, Stephen; Kumar, Sushil

2004-06-01

We report our development of terahertz (THz) quantum-cascade lasers (QCLs) based on two novel features. First, the depopulation of the lower radiative level is achieved through resonant longitudinal optical (LO-)phonon scattering. This depopulation mechanism is robust at high temperatures and high injection levels. In contrast to infrared QCLs that also use LO-phonon scattering for depopulation, in our THz lasers the selectivity of the depopulation scattering is achieved through a combination of resonant tunneling and LO-phonon scattering, hence the term resonant phonon. This resonant-phonon scheme allows a highly selective depopulation of the lower radiative level with a sub-picosecond lifetime, while maintainingmore » a relatively long upper level lifetime (>5 ps) that is due to upper-to-ground-state scattering. The second feature of our lasers is that mode confinement is achieved by using a novel double-sided metal-metal waveguide, which yields an essentially unity mode confinement factor and therefore a low total cavity loss at THz frequencies. Based on these two unique features, we have achieved some record performance, including, but not limited to, the highest pulsed operating temperature of 137 K, the highest continuous-wave operating temperature of 97 K, and the longest wavelength of 141 {micro}m (corresponding to 2.1 THz) without the assistance of a magnetic field.« less

Development and evaluation of an ambulatory stress monitor based on wearable sensors.

PubMed

Choi, Jongyoon; Ahmed, Beena; Gutierrez-Osuna, Ricardo

2012-03-01

Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects. © 2012 IEEE

Joint sparsity based heterogeneous data-level fusion for target detection and estimation

NASA Astrophysics Data System (ADS)

Niu, Ruixin; Zulch, Peter; Distasio, Marcello; Blasch, Erik; Shen, Dan; Chen, Genshe

2017-05-01

Typical surveillance systems employ decision- or feature-level fusion approaches to integrate heterogeneous sensor data, which are sub-optimal and incur information loss. In this paper, we investigate data-level heterogeneous sensor fusion. Since the sensors monitor the common targets of interest, whose states can be determined by only a few parameters, it is reasonable to assume that the measurement domain has a low intrinsic dimensionality. For heterogeneous sensor data, we develop a joint-sparse data-level fusion (JSDLF) approach based on the emerging joint sparse signal recovery techniques by discretizing the target state space. This approach is applied to fuse signals from multiple distributed radio frequency (RF) signal sensors and a video camera for joint target detection and state estimation. The JSDLF approach is data-driven and requires minimum prior information, since there is no need to know the time-varying RF signal amplitudes, or the image intensity of the targets. It can handle non-linearity in the sensor data due to state space discretization and the use of frequency/pixel selection matrices. Furthermore, for a multi-target case with J targets, the JSDLF approach only requires discretization in a single-target state space, instead of discretization in a J-target state space, as in the case of the generalized likelihood ratio test (GLRT) or the maximum likelihood estimator (MLE). Numerical examples are provided to demonstrate that the proposed JSDLF approach achieves excellent performance with near real-time accurate target position and velocity estimates.

Cavity hydration dynamics in cytochrome c oxidase and functional implications

PubMed Central

Son, Chang Yun; Cui, Qiang

2017-01-01

Cytochrome c oxidase (CcO) is a transmembrane protein that uses the free energy of O2 reduction to generate the proton concentration gradient across the membrane. The regulation of competitive proton transfer pathways has been established to be essential to the vectorial transport efficiency of CcO, yet the underlying mechanism at the molecular level remains lacking. Recent studies have highlighted the potential importance of hydration-level change in an internal cavity that connects the proton entrance channel, the site of O2 reduction, and the putative proton exit route. In this work, we use atomistic molecular dynamics simulations to investigate the energetics and timescales associated with the volume fluctuation and hydration-level change in this central cavity. Extensive unrestrained molecular dynamics simulations (accumulatively ∼4 μs) and free energy computations for different chemical states of CcO support a model in which the volume and hydration level of the cavity are regulated by the protonation state of a propionate group of heme a3 and, to a lesser degree, the redox state of heme a and protonation state of Glu286. Markov-state model analysis of ∼2-μs trajectories suggests that hydration-level change occurs on the timescale of 100–200 ns before the proton-loading site is protonated. The computed energetic and kinetic features for the cavity wetting transition suggest that reversible hydration-level change of the cavity can indeed be a key factor that regulates the branching of proton transfer events and therefore contributes to the vectorial efficiency of proton transport. PMID:28973914

Peculiar Features of Microstructure Formation and Microhardness Variations During Torsional Straining of Tantalum Specimens in Bridgman Anvils

NASA Astrophysics Data System (ADS)

Ditenberg, I. A.; Tymentsev, A. N.; Korznikov, A. V.

2015-04-01

Using the method of transmission electron microscopy, peculiar features of evolution of microstructure and variations in microhardness of Та are investigated under torsional loading in the Bridgman anvil as a function of plastic deformation at room temperature. A quantitative examination of grain and defect's structure of the material under study and the values of local internal stresses is performed in different loading stages. The mechanisms of formation of submicrocrystalline and nanostructured states are analyzed and so is the microstructure variation as a function of the defect-structure characteristics, strain level, and spacing from the axis of torsion.

The effect of exchange interaction on quasiparticle Landau levels in narrow-gap quantum well heterostructures.

PubMed

Krishtopenko, S S; Gavrilenko, V I; Goiran, M

2012-04-04

Using the 'screened' Hartree-Fock approximation based on the eight-band k·p Hamiltonian, we have extended our previous work (Krishtopenko et al 2011 J. Phys.: Condens. Matter 23 385601) on exchange enhancement of the g-factor in narrow-gap quantum well heterostructures by calculating the exchange renormalization of quasiparticle energies, the density of states at the Fermi level and the quasiparticle g-factor for different Landau levels overlapping. We demonstrate that exchange interaction yields more pronounced Zeeman splitting of the density of states at the Fermi level and leads to the appearance of peak-shaped features in the dependence of the Landau level energies on the magnetic field at integer filling factors. We also find that the quasiparticle g-factor does not reach the maximum value at odd filling factors in the presence of large overlapping of spin-split Landau levels. We advance an argument that the behavior of the quasiparticle g-factor in weak magnetic fields is defined by a random potential of impurities in narrow-gap heterostructures. © 2012 IOP Publishing Ltd

Insights into the crystal-packing effects on the spin crossover of [Fe(II)(1-bpp)](2+)-based materials.

PubMed

Vela, Sergi; Novoa, Juan J; Ribas-Arino, Jordi

2014-12-28

Iron(II) complexes of the [Fe(II)(1-bpp2)](2+) type (1-bpp = 2,6-di(pyrazol-1-yl)pyridine) have been intensively investigated in the context of crystal engineering of switchable materials because their spin-crossover (SCO) properties dramatically depend on the counterions. Here, by means of DFT + U calculations at the molecular and solid state levels we provide a rationale for the different SCO behaviour of the BF4(-) and ClO4(-) salts of the parent complex; the former features Fe(II) complexes with a regular coordination geometry and undergoes a spin transition, whereas the Fe(II) complexes of the latter adopt a distorted structure and remain in the high-spin state at all temperatures. The different SCO behaviour of both salts can be explained on the basis of a combination of thermodynamic and kinetic effects. The shape of the SCO units at high temperature is thermodynamically controlled by the intermolecular interactions between the SCO units and counterions within the crystal. The spin trapping at low temperatures in the ClO4(-) salt, in turn, is traced back to a kinetic effect because our calculations have revealed the existence of a more stable polymorph having SCO units in their low-spin state that feature a regular structure. From the computational point of view, it is the first time that the U parameter is fine-tuned on the basis of CASPT2 calculations, thereby enabling an accurate description of the energetics of the spin transition at both molecular and solid-state levels.

Steady state volcanism - Evidence from eruption histories of polygenetic volcanoes

NASA Technical Reports Server (NTRS)

Wadge, G.

1982-01-01

Cumulative volcano volume curves are presented as evidence for steady-state behavior at certain volcanoes and to develop a model of steady-state volcanism. A minimum criteria of five eruptions over a year was chosen to characterize a steady-state volcano. The subsequent model features a constant head of magmatic pressure from a reservoir supplied from depth, a sawtooth curve produced by the magma arrivals or discharge from the subvolcanic reservoir, large volume eruptions with long repose periods, and conditions of nonsupply of magma. The behavior of Mts. Etna, Nyamuragira, and Kilauea are described and show continuous levels of plasma output resulting in cumulative volume increases. Further discussion is made of steady-state andesitic and dacitic volcanism, long term patterns of the steady state, and magma storage, and the lack of a sufficient number of steady-state volcanoes in the world is taken as evidence that further data is required for a comprehensive model.

Semantic attributes for people's appearance description: an appearance modality for video surveillance applications

NASA Astrophysics Data System (ADS)

Frikha, Mayssa; Fendri, Emna; Hammami, Mohamed

2017-09-01

Using semantic attributes such as gender, clothes, and accessories to describe people's appearance is an appealing modeling method for video surveillance applications. We proposed a midlevel appearance signature based on extracting a list of nameable semantic attributes describing the body in uncontrolled acquisition conditions. Conventional approaches extract the same set of low-level features to learn the semantic classifiers uniformly. Their critical limitation is the inability to capture the dominant visual characteristics for each trait separately. The proposed approach consists of extracting low-level features in an attribute-adaptive way by automatically selecting the most relevant features for each attribute separately. Furthermore, relying on a small training-dataset would easily lead to poor performance due to the large intraclass and interclass variations. We annotated large scale people images collected from different person reidentification benchmarks covering a large attribute sample and reflecting the challenges of uncontrolled acquisition conditions. These annotations were gathered into an appearance semantic attribute dataset that contains 3590 images annotated with 14 attributes. Various experiments prove that carefully designed features for learning the visual characteristics for an attribute provide an improvement of the correct classification accuracy and a reduction of both spatial and temporal complexities against state-of-the-art approaches.

The Comprehensive AOCMF Classification System: Mandible Fractures-Level 3 Tutorial

PubMed Central

Cornelius, Carl-Peter; Audigé, Laurent; Kunz, Christoph; Rudderman, Randal; Buitrago-Téllez, Carlos H.; Frodel, John; Prein, Joachim

2014-01-01

This tutorial outlines the details of the AOCMF image-based classification system for fractures of the mandibular arch (i.e. the non-condylar mandible) at the precision level 3. It is the logical expansion of the fracture allocation to topographic mandibular sites outlined in level 2, and is based on three-dimensional (3D) imaging techniques/computed tomography (CT)/cone beam CT). Level 3 allows an anatomical description of the individual conditions of the mandibular arch such as the preinjury dental state and the degree of alveolar atrophy. Trauma sequelae are then addressed: (1) tooth injuries and periodontal trauma, (2) fracture involvement of the alveolar process, (3) the degree of fracture fragmentation in three categories (none, minor, and major), and (4) the presence of bone loss. The grading of fragmentation needs a 3D evaluation of the fracture area, allowing visualization of the outer and inner mandibular cortices. To document these fracture features beyond topography the alphanumeric codes are supplied with distinctive appendices. This level 3 tutorial is accompanied by a brief survey of the peculiarities of the edentulous atrophic mandible. Illustrations and a few case examples serve as instruction and reference to improve the understanding and application of the presented features. PMID:25489389

Effect of Field Spread on Resting-State Magneto Encephalography Functional Network Analysis: A Computational Modeling Study.

PubMed

Silva Pereira, Silvana; Hindriks, Rikkert; Mühlberg, Stefanie; Maris, Eric; van Ede, Freek; Griffa, Alessandra; Hagmann, Patric; Deco, Gustavo

2017-11-01

A popular way to analyze resting-state electroencephalography (EEG) and magneto encephalography (MEG) data is to treat them as a functional network in which sensors are identified with nodes and the interaction between channel time series and the network connections. Although conceptually appealing, the network-theoretical approach to sensor-level EEG and MEG data is challenged by the fact that EEG and MEG time series are mixtures of source activity. It is, therefore, of interest to assess the relationship between functional networks of source activity and the ensuing sensor-level networks. Since these topological features are of high interest in experimental studies, we address the question of to what extent the network topology can be reconstructed from sensor-level functional connectivity (FC) measures in case of MEG data. Simple simulations that consider only a small number of regions do not allow to assess network properties; therefore, we use a diffusion magnetic resonance imaging-constrained whole-brain computational model of resting-state activity. Our motivation lies behind the fact that still many contributions found in the literature perform network analysis at sensor level, and we aim at showing the discrepancies between source- and sensor-level network topologies by using realistic simulations of resting-state cortical activity. Our main findings are that the effect of field spread on network topology depends on the type of interaction (instantaneous or lagged) and leads to an underestimation of lagged FC at sensor level due to instantaneous mixing of cortical signals, instantaneous interaction is more sensitive to field spread than lagged interaction, and discrepancies are reduced when using planar gradiometers rather than axial gradiometers. We, therefore, recommend using lagged interaction measures on planar gradiometer data when investigating network properties of resting-state sensor-level MEG data.

Using the experience-sampling method to examine the psychological mechanisms by which participatory art improves wellbeing.

PubMed

Holt, Nicola J

2018-01-01

To measure the immediate impact of art-making in everyday life on diverse indices of wellbeing ('in the moment' and longer term) in order to improve understanding of the psychological mechanisms by which art may improve mental health. Using the experience-sampling method, 41 artists were prompted (with a 'beep' on a handheld computer) at random intervals (10 times a day, for one week) to answer a short questionnaire. The questionnaire tracked art-making and enquired about mood, cognition and state of consciousness. This resulted in 2,495 sampled experiences, with a high response rate in which 89% of questionnaires were completed. Multi-level modelling was used to evaluate the impact of art-making on experience, with 2,495 'experiences' (experiential-level) nested within 41 participants (person-level). Recent art-making was significantly associated with experiential shifts: improvement in hedonic tone, vivid internal imagery and the flow state. Furthermore, the frequency of art-making across the week was associated with person-level measures of wellbeing: eudemonic happiness and self-regulation. Cross-level interactions, between experiential and person-level variables, suggested that hedonic tone improved more for those scoring low on eudemonic happiness, and further that, those high in eudemonic happiness were more likely to experience phenomenological features of the flow state and to experience inner dialogue while art-making. Art-making has both immediate and long-term associations with wellbeing. At the experiential level, art-making affects multiple dimensions of conscious experience: affective, cognitive and state factors. This suggests that there are multiple routes to wellbeing (improving hedonic tone, making meaning through inner dialogue and experiencing the flow state). Recommendations are made to consider these factors when both developing and evaluating public health interventions that involve participatory art.

Correlates of electroencephalographic resting states and erythrocyte membrane docosahexaenoic and eicosapentaenoic acid levels in individuals at ultra-high risk of psychosis.

PubMed

Lavoie, Suzie; Whitford, Thomas J; Benninger, Franz; Feucht, Martha; Kim, Sung-Wan; Klier, Claudia M; McNamara, Robert K; Rice, Simon; Schäfer, Miriam R; Amminger, G Paul

2016-01-01

Abnormal levels of polyunsaturated fatty acids (PUFAs) have been reported in individuals suffering from schizophrenia. The main aim of the present study was to investigate the relationship between erythrocyte membrane fatty acid levels and resting-state brain activity occurring in individuals at ultra-high risk (UHR) of psychosis. The association between erythrocyte membrane fatty acids levels and resting-state brain activity and its value in predicting psychosis was examined in 72 UHR individuals. In the frontal area, the activity in the fast frequency band Beta2 was positively associated with docosahexaenoic acid (DHA) levels (R = 0.321, P = 0.017), and in the fronto-central area, Beta2 activity showed a positive correlation with eicosapentaenoic acid (EPA) levels (R = 0.305, P = 0.009), regardless of psychosis transition status. Conversely, the slow frequency band Theta was significantly negatively associated with EPA levels in the parieto-occipital region (R = -0.251, P = 0.033. Results also showed that Alpha power was negatively correlated with DHA levels in UHR individuals who did not transition to psychosis, while this correlation was not present in individuals who later transitioned. Our results suggest that individuals at UHR for psychosis who have higher basal omega-3 fatty acids levels present with resting EEG features associated with better states of alertness and vigilance. Furthermore, the improvement in the Alpha synchrony observed along with increased DHA levels in participants who did not transition to psychosis is disturbed in those who did transition. However, these interesting results are limited by the small sample size and low statistical power of the study. © The Royal Australian and New Zealand College of Psychiatrists 2015.

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence.

PubMed

Qazi, Emad-Ul-Haq; Hussain, Muhammad; Aboalsamh, Hatim; Malik, Aamir Saeed; Amin, Hafeez Ullah; Bamatraf, Saeed

2016-01-01

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0-1.875 Hz (delta low) and 1.875-3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

Magnetic quantization in monolayer bismuthene

NASA Astrophysics Data System (ADS)

Chen, Szu-Chao; Chiu, Chih-Wei; Lin, Hui-Chi; Lin, Ming-Fa

The magnetic quantization in monolayer bismuthene is investigated by the generalized tight-binding model. The quite large Hamiltonian matrix is built from the tight-binding functions of the various sublattices, atomic orbitals and spin states. Due to the strong spin orbital coupling and sp3 bonding, monolayer bismuthene has the diverse low-lying energy bands such as the parabolic, linear and oscillating energy bands. The main features of band structures are further reflected in the rich magnetic quantization. Under a uniform perpendicular magnetic field (Bz) , three groups of Landau levels (LLs) with distinct features are revealed near the Fermi level. Their Bz-dependent energy spectra display the linear, square-root and non-monotonous dependences, respectively. These LLs are dominated by the combinations of the 6pz orbital and (6px,6py) orbitals as a result of strong sp3 bonding. Specifically, the LL anti-crossings only occur between LLs originating from the oscillating energy band.

Identifying the distinct features of geometric structures for hole trapping to generate radicals on rutile TiO₂(110) in photooxidation using density functional theory calculations with hybrid functional.

PubMed

Wang, Dong; Wang, Haifeng; Hu, P

2015-01-21

Using density functional theory calculations with HSE 06 functional, we obtained the structures of spin-polarized radicals on rutile TiO2(110), which is crucial to understand the photooxidation at the atomic level, and further calculate the thermodynamic stabilities of these radicals. By analyzing the results, we identify the structural features for hole trapping in the system, and reveal the mutual effects among the geometric structures, the energy levels of trapped hole states and their hole trapping capacities. Furthermore, the results from HSE 06 functional are compared to those from DFT + U and the stability trend of radicals against the number of slabs is tested. The effect of trapped holes on two important steps of the oxygen evolution reaction, i.e. water dissociation and the oxygen removal, is investigated and discussed.

Electron-impact electronic-state excitation of para-benzoquinone

NASA Astrophysics Data System (ADS)

Jones, D. B.; da Costa, R. F.; Kossoski, F.; Varella, M. T. do N.; Bettega, M. H. F.; Ferreira da Silva, F.; Limão-Vieira, P.; García, G.; Lima, M. A. P.; White, R. D.; Brunger, M. J.

2018-03-01

Angle resolved electron energy loss spectra (EELS) for para-benzoquinone (C6H4O2) have been recorded for incident electron energies of 20, 30, and 40 eV. Measured differential cross sections (DCSs) for electronic band features, composed of a combination of energetically unresolved electronic states, are subsequently derived from those EELS. Where possible, the obtained DCSs are compared with those calculated using the Schwinger multichannel method with pseudopotentials. These calculations were performed using a minimum orbital basis single configuration interaction framework at the static exchange plus polarisation level. Here, quite reasonable agreement between the experimental cross sections and the theoretical cross sections for the summation of unresolved states was observed.

Nuclear Structure of the Closed Subshell Nucleus 90Zr Studied with the (n,n'(gamma)) Reaction

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

Garrett, P E; Younes, Y; Becker, J A

States in {sup 90}Zr have been observed with the (n,n{prime}{gamma}) reaction using both spallation and monoenergetic accelerator-produced neutrons. A scheme comprised of 81 levels and 157 transitions was constructed concentrating on levels below 5.6 MeV in excitation energy. Spins have been determined by considering data from all experimental studies performed for {sup 90}Zr. Lifetimes have been deduced using the Doppler-shift attenuation method for many of the states and transition rates have been obtained. A spherical shell-model interpretation in terms of particle-hole excitations assuming a {sup 88}Sr closed core is given. In some cases, enhancements in B(M1) and B(E2) values aremore » observed that cannot be explained by assuming simple particle-hole excitations. Shell-model calculations using an extended f pg-shell model space reproduce the spectrum of excited states very well, and the gross features of the B(M1) and B(E2) transition rates. Transition rates for individual levels show discrepancies between calculations and experimental values.« less

On Steady-State Tropical Cyclones

DTIC Science & Technology

2014-01-01

components of the velocity vector, specific humidity, suspended liquid water, perturbation Exner function and perturbation density potential...vorticity and spin-up function, respectively. If the flow is symmetrically stable, the partial differential equation (10) is elliptic with a forcing term...Upper-level inflow jets A prominent feature of the radial velocity component shown in Figure 2(c) is the layered structure of inflow and outflow in the

Contemporary American Success Stories. Famous People of Asian Ancestry. Volume V. A Mitchell Lane Multicultural Biography Series.

ERIC Educational Resources Information Center

Marvis, Barbara J.

As part of a five-volume series, this collection of biographical sketches of Asian Americans who have made contributions to life in the United States profiles individuals who are role models for all children. Written at a reading level for grades five to six, the biographies feature Asian Americans of different ethnic backgrounds, exploring the…

Magnetoconductance signatures of subband structure in semiconductor nanowires

NASA Astrophysics Data System (ADS)

Holloway, Gregory; Haapamaki, Chris; Lapierre, Ray; Baugh, Jonathan

2015-03-01

Understanding the subband structure due to radial confinement in semiconductor nanowires can benefit technologies ranging from optical sensors to quantum information processing. An axial magnetic field couples to the orbital angular momentum, giving rise to non-trivial features in electronic transport as a function of magnetic field. Previous reports focused on conduction electrons confined to a thin shell near the nanowire surface, which lead to flux-periodic energies and conductance oscillations. Here, we calculate the eigenstates for more general radial potentials with moderate to low surface band bending such that electrons are distributed more uniformly across the nanowire cross-section. It is found that the energy spectrum becomes aperiodic in both gate voltage and magnetic field as the radial potential becomes flatter. The behavior of an energy level is dictated by its angular momentum, and this allows, in principle, each state to be identified based on its dependence on magnetic field and the chemical potential. We experimentally investigate a short-channel InAs nanowire FET in search of conductance features that reveal this subband structure. A quantitative measure for assigning conductance features to specific transverse states is introduced and applied to this device.

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

PubMed Central

Ibarra, Frank F.; Kardan, Omid; Hunter, MaryCarol R.; Kotabe, Hiroki P.; Meyer, Francisco A. C.; Berman, Marc G.

2017-01-01

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 future research relating to landscape and urban design with the aim of maximizing subjective well-being, which could lead to improved health outcomes on a larger scale. PMID:28503158

Document-Level Classification of CT Pulmonary Angiography Reports based on an Extension of the ConText Algorithm

PubMed Central

Chapman, Brian E.; Lee, Sean; Kang, Hyunseok Peter; Chapman, Wendy W.

2011-01-01

In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes’ classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes’ classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes’ classifier using bigrams. PMID:21459155

Substituent effects on the redox states of locally functionalized single-walled carbon nanotubes revealed by in situ photoluminescence spectroelectrochemistry.

PubMed

Shiraishi, Tomonari; Shiraki, Tomohiro; Nakashima, Naotoshi

2017-11-09

Single-walled carbon nanotubes (SWNTs) with local chemical modification have been recognized as a novel near infrared (NIR) photoluminescent nanomaterial due to the emergence of a new red-shifted photoluminescence (PL) with enhanced quantum yields. As a characteristic feature of the locally functionalized SWNTs (lf-SWNTs), PL wavelength changes occur with the structural dependence of the substituent structures in the modified aryl groups, showing up to a 60 nm peak shift according to an electronic property difference of the aryl groups. Up to now, however, the structural effect on the electronic states of the lf-SWNTs has been discussed only on the basis of theoretical calculations due to the very limited amount of modifications. Herein, we describe the successfully-determined electronic states of the aryl-modified lf-SWNTs with different substituents (Ar-X SWNTs) using an in situ PL spectroelectrochemical method based on electrochemical quenching of the PL intensities analyzed by the Nernst equation. In particular, we reveal that the local functionalization of (6,5)SWNTs induced potential changes in the energy levels of the HOMO and the LUMO by -23 to -38 meV and +20 to +22 meV, respectively, compared to those of the pristine SWNTs, which generates exciton trapping sites with narrower band gaps. Moreover, the HOMO levels of the Ar-X SWNTs specifically shift in a negative potential direction by 15 meV according to an enhancement of the electron-accepting property of the substituents in the aryl groups that corresponds to an increase in the Hammet substituent constants, suggesting the importance of the dipole effect from the aryl groups on the lf-SWNTs to the level shift of the frontier orbitals. Our method is a promising way to characterize the electronic features of the lf-SWNTs.

Understanding Deep Representations Learned in Modeling Users Likes.

PubMed

Guntuku, Sharath Chandra; Zhou, Joey Tianyi; Roy, Sujoy; Lin, Weisi; Tsang, Ivor W

2016-08-01

Automatically understanding and discriminating different users' liking for an image is a challenging problem. This is because the relationship between image features (even semantic ones extracted by existing tools, viz., faces, objects, and so on) and users' likes is non-linear, influenced by several subtle factors. This paper presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. Feature selection is applied before learning deep representation to identify the important features for a user to like an image. The proposed representation is shown to be effective in discriminating users based on images they like and also in recommending images that a given user likes, outperforming the state-of-the-art feature representations by  ∼ 15 %-20%. Beyond this test-set performance, an attempt is made to qualitatively understand the representations learned by the deep architecture used to model user likes.

Absence of Dirac states in BaZnBi 2 induced by spin-orbit coupling

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

Ren, Weijun; Wang, Aifeng; Graf, D.

We report magnetotransport properties of BaZnBi 2 single crystals. Whereas electronic structure features Dirac states, such states are removed from the Fermi level by spin-orbit coupling (SOC) and consequently electronic transport is dominated by the small hole and electron pockets. Our results are consistent with not only three-dimensional, but also with quasi-two-dimensional portions of the Fermi surface. The SOC-induced gap in Dirac states is much larger when compared to isostructural SrMnBi 2. This suggests that not only long-range magnetic order, but also mass of the alkaline-earth atoms A in ABX 2 ( A = alkaline-earth, B = transition-metal, and Xmore » = Bi/Sb) are important for the presence of low-energy states obeying the relativistic Dirac equation at the Fermi surface.« less

Absence of Dirac states in BaZnBi 2 induced by spin-orbit coupling

DOE PAGES

Ren, Weijun; Wang, Aifeng; Graf, D.; ...

2018-01-22

We report magnetotransport properties of BaZnBi 2 single crystals. Whereas electronic structure features Dirac states, such states are removed from the Fermi level by spin-orbit coupling (SOC) and consequently electronic transport is dominated by the small hole and electron pockets. Our results are consistent with not only three-dimensional, but also with quasi-two-dimensional portions of the Fermi surface. The SOC-induced gap in Dirac states is much larger when compared to isostructural SrMnBi 2. This suggests that not only long-range magnetic order, but also mass of the alkaline-earth atoms A in ABX 2 ( A = alkaline-earth, B = transition-metal, and Xmore » = Bi/Sb) are important for the presence of low-energy states obeying the relativistic Dirac equation at the Fermi surface.« less

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

PubMed

Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier

2018-05-21

Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

PubMed Central

Baydil, Banu; Daley, William P.; Larsen, Melinda; Yener, Bülent

2012-01-01

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues. PMID:22403724

Energy Gaps and Layer Polarization of Integer and Fractional Quantum Hall States in Bilayer Graphene.

PubMed

Shi, Yanmeng; Lee, Yongjin; Che, Shi; Pi, Ziqi; Espiritu, Timothy; Stepanov, Petr; Smirnov, Dmitry; Lau, Chun Ning; Zhang, Fan

2016-02-05

Owing to the spin, valley, and orbital symmetries, the lowest Landau level in bilayer graphene exhibits multicomponent quantum Hall ferromagnetism. Using transport spectroscopy, we investigate the energy gaps of integer and fractional quantum Hall (QH) states in bilayer graphene with controlled layer polarization. The state at filling factor ν=1 has two distinct phases: a layer polarized state that has a larger energy gap and is stabilized by high electric field, and a hitherto unobserved interlayer coherent state with a smaller gap that is stabilized by large magnetic field. In contrast, the ν=2/3 quantum Hall state and a feature at ν=1/2 are only resolved at finite electric field and large magnetic field. These results underscore the importance of controlling layer polarization in understanding the competing symmetries in the unusual QH system of BLG.

Quantized transport and steady states of Floquet topological insulators

NASA Astrophysics Data System (ADS)

Esin, Iliya; Rudner, Mark S.; Refael, Gil; Lindner, Netanel H.

2018-06-01

Robust electronic edge or surface modes play key roles in the fascinating quantized responses exhibited by topological materials. Even in trivial materials, topological bands and edge states can be induced dynamically by a time-periodic drive. Such Floquet topological insulators (FTIs) inherently exist out of equilibrium; the extent to which they can host quantized transport, which depends on the steady-state population of their dynamically induced edge states, remains a crucial question. In this work, we obtain the steady states of two-dimensional FTIs in the presence of the natural dissipation mechanisms present in solid state systems. We give conditions under which the steady-state distribution resembles that of a topological insulator in the Floquet basis. In this state, the distribution in the Floquet edge modes exhibits a sharp feature akin to a Fermi level, while the bulk hosts a small density of excitations. We determine the regimes where topological edge-state transport persists and can be observed in FTIs.

Block-localized wavefunction (BLW) method at the density functional theory (DFT) level.

PubMed

Mo, Yirong; Song, Lingchun; Lin, Yuchun

2007-08-30

The block-localized wavefunction (BLW) approach is an ab initio valence bond (VB) method incorporating the efficiency of molecular orbital (MO) theory. It can generate the wavefunction for a resonance structure or diabatic state self-consistently by partitioning the overall electrons and primitive orbitals into several subgroups and expanding each block-localized molecular orbital in only one subspace. Although block-localized molecular orbitals in the same subspace are constrained to be orthogonal (a feature of MO theory), orbitals between different subspaces are generally nonorthogonal (a feature of VB theory). The BLW method is particularly useful in the quantification of the electron delocalization (resonance) effect within a molecule and the charge-transfer effect between molecules. In this paper, we extend the BLW method to the density functional theory (DFT) level and implement the BLW-DFT method to the quantum mechanical software GAMESS. Test applications to the pi conjugation in the planar allyl radical and ions with the basis sets of 6-31G(d), 6-31+G(d), 6-311+G(d,p), and cc-pVTZ show that the basis set dependency is insignificant. In addition, the BLW-DFT method can also be used to elucidate the nature of intermolecular interactions. Examples of pi-cation interactions and solute-solvent interactions will be presented and discussed. By expressing each diabatic state with one BLW, the BLW method can be further used to study chemical reactions and electron-transfer processes whose potential energy surfaces are typically described by two or more diabatic states.

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

PubMed Central

Yu, Qingbao; Erhardt, Erik B.; Sui, Jing; Du, Yuhui; He, Hao; Hjelm, Devon; Cetin, Mustafa S.; Rachakonda, Srinivas; Miller, Robyn L.; Pearlson, Godfrey; Calhoun, Vince D.

2014-01-01

Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia which may underscore the abnormal brain performance in this mental illness. PMID:25514514

Identifying potential collapse features under highways : research implementation plan.

DOT National Transportation Integrated Search

2005-09-01

There are many unmapped features under the states roadways that threaten them with major localized : collapse. The most common of these features are abandoned underground mines in the eastern part of : the state and sinkholes in portions of limest...

Circulating Anti-Müllerian Hormone Levels in Daughters of Women with and without Polycystic Ovary Syndrome.

PubMed

Olszanecka-Glinianowicz, Magdalena; Zachurzok, Agnieszka; Drosdzol-Cop, Agnieszka; Bożętowicz-Wikarek, Maria; Owczarek, Aleksander; Gawlik, Aneta; Chudek, Jerzy; Skrzypulec-Plinta, Violetta; Małecka-Tendera, Ewa

2016-01-01

The aim of this study was to assess whether circulating anti-Müllerian hormone (AMH) levels in daughters of women with polycystic ovary syndrome (PCOSd) correspond with clinical and biochemical features of hyperandrogenism, polycystic ovary morphology and menstrual cycle disturbances. Menstrual cycle disturbances, hirsutism, acne and ultrasonographic ovarian morphology were assessed in 75 girls aged 13-18 years (35 PCOSd and 40 daughters of healthy women). Serum gonadotropins, androgens, sex hormone-binding globulin and plasma AMH were measured in a fasting state, and the free androgen index was calculated. A significant correlation between the AMH level and mean ovary volume was found (r = 0.36; p < 0.01). AMH levels were not related to hirsutism, acne and polycystic ovary morphology. Significantly higher AMH levels were found only in PCOSd with irregular menstruation or secondary amenorrhea. The results of logistic regression analysis showed that in that group for each 1-ng/ml increase in the AMH level, the odds ratio of the PCOS occurrence in the future was increased 1.27 times (95% CI: 1.09-1.47; p < 0.01). A higher AMH level in PCOSd is associated with menstrual cycle disturbances and larger ovarian volume but not with clinical and biochemical features of hyperandrogenism. Thus, the risk for PCOS development among genetically predisposed girls may be related to increased AMH levels. © 2016 S. Karger AG, Basel.

Representation learning: a unified deep learning framework for automatic prostate MR segmentation.

PubMed

Liao, Shu; Gao, Yaozong; Oto, Aytekin; Shen, Dinggang

2013-01-01

Image representation plays an important role in medical image analysis. The key to the success of different medical image analysis algorithms is heavily dependent on how we represent the input data, namely features used to characterize the input image. In the literature, feature engineering remains as an active research topic, and many novel hand-crafted features are designed such as Haar wavelet, histogram of oriented gradient, and local binary patterns. However, such features are not designed with the guidance of the underlying dataset at hand. To this end, we argue that the most effective features should be designed in a learning based manner, namely representation learning, which can be adapted to different patient datasets at hand. In this paper, we introduce a deep learning framework to achieve this goal. Specifically, a stacked independent subspace analysis (ISA) network is adopted to learn the most effective features in a hierarchical and unsupervised manner. The learnt features are adapted to the dataset at hand and encode high level semantic anatomical information. The proposed method is evaluated on the application of automatic prostate MR segmentation. Experimental results show that significant segmentation accuracy improvement can be achieved by the proposed deep learning method compared to other state-of-the-art segmentation approaches.

Circadian and Brain State Modulation of Network Hyperexcitability in Alzheimer's Disease.

PubMed

Brown, Rosalind; Lam, Alice D; Gonzalez-Sulser, Alfredo; Ying, Andrew; Jones, Mary; Chou, Robert Chang-Chih; Tzioras, Makis; Jordan, Crispin Y; Jedrasiak-Cape, Izabela; Hemonnot, Anne-Laure; Abou Jaoude, Maurice; Cole, Andrew J; Cash, Sydney S; Saito, Takashi; Saido, Takaomi; Ribchester, Richard R; Hashemi, Kevan; Oren, Iris

2018-01-01

Network hyperexcitability is a feature of Alzheimer' disease (AD) as well as numerous transgenic mouse models of AD. While hyperexcitability in AD patients and AD animal models share certain features, the mechanistic overlap remains to be established. We aimed to identify features of network hyperexcitability in AD models that can be related to epileptiform activity signatures in AD patients. We studied network hyperexcitability in mice expressing amyloid precursor protein (APP) with mutations that cause familial AD, and compared a transgenic model that overexpresses human APP (hAPP) (J20), to a knock-in model expressing APP at physiological levels (APP NL/F ). We recorded continuous long-term electrocorticogram (ECoG) activity from mice, and studied modulation by circadian cycle, behavioral, and brain state. We report that while J20s exhibit frequent interictal spikes (IISs), APP NL/F mice do not. In J20 mice, IISs were most prevalent during daylight hours and the circadian modulation was associated with sleep. Further analysis of brain state revealed that IIS in J20s are associated with features of rapid eye movement (REM) sleep. We found no evidence of cholinergic changes that may contribute to IIS-circadian coupling in J20s. In contrast to J20s, intracranial recordings capturing IIS in AD patients demonstrated frequent IIS in non-REM (NREM) sleep. The salient differences in sleep-stage coupling of IIS in APP overexpressing mice and AD patients suggests that different mechanisms may underlie network hyperexcitability in mice and humans. We posit that sleep-stage coupling of IIS should be an important consideration in identifying mouse AD models that most closely recapitulate network hyperexcitability in human AD.

Circadian and Brain State Modulation of Network Hyperexcitability in Alzheimer’s Disease

PubMed Central

Ying, Andrew; Jones, Mary; Chou, Robert Chang-Chih; Jordan, Crispin Y.; Jedrasiak-Cape, Izabela; Abou Jaoude, Maurice; Hashemi, Kevan

2018-01-01

Abstract Network hyperexcitability is a feature of Alzheimer’ disease (AD) as well as numerous transgenic mouse models of AD. While hyperexcitability in AD patients and AD animal models share certain features, the mechanistic overlap remains to be established. We aimed to identify features of network hyperexcitability in AD models that can be related to epileptiform activity signatures in AD patients. We studied network hyperexcitability in mice expressing amyloid precursor protein (APP) with mutations that cause familial AD, and compared a transgenic model that overexpresses human APP (hAPP) (J20), to a knock-in model expressing APP at physiological levels (APPNL/F). We recorded continuous long-term electrocorticogram (ECoG) activity from mice, and studied modulation by circadian cycle, behavioral, and brain state. We report that while J20s exhibit frequent interictal spikes (IISs), APPNL/F mice do not. In J20 mice, IISs were most prevalent during daylight hours and the circadian modulation was associated with sleep. Further analysis of brain state revealed that IIS in J20s are associated with features of rapid eye movement (REM) sleep. We found no evidence of cholinergic changes that may contribute to IIS-circadian coupling in J20s. In contrast to J20s, intracranial recordings capturing IIS in AD patients demonstrated frequent IIS in non-REM (NREM) sleep. The salient differences in sleep-stage coupling of IIS in APP overexpressing mice and AD patients suggests that different mechanisms may underlie network hyperexcitability in mice and humans. We posit that sleep-stage coupling of IIS should be an important consideration in identifying mouse AD models that most closely recapitulate network hyperexcitability in human AD. PMID:29780880

Visual perceptual training reconfigures post-task resting-state functional connectivity with a feature-representation region.

PubMed

Sarabi, Mitra Taghizadeh; Aoki, Ryuta; Tsumura, Kaho; Keerativittayayut, Ruedeerat; Jimura, Koji; Nakahara, Kiyoshi

2018-01-01

The neural mechanisms underlying visual perceptual learning (VPL) have typically been studied by examining changes in task-related brain activation after training. However, the relationship between post-task "offline" processes and VPL remains unclear. The present study examined this question by obtaining resting-state functional magnetic resonance imaging (fMRI) scans of human brains before and after a task-fMRI session involving visual perceptual training. During the task-fMRI session, participants performed a motion coherence discrimination task in which they judged the direction of moving dots with a coherence level that varied between trials (20, 40, and 80%). We found that stimulus-induced activation increased with motion coherence in the middle temporal cortex (MT+), a feature-specific region representing visual motion. On the other hand, stimulus-induced activation decreased with motion coherence in the dorsal anterior cingulate cortex (dACC) and bilateral insula, regions involved in decision making under perceptual ambiguity. Moreover, by comparing pre-task and post-task rest periods, we revealed that resting-state functional connectivity (rs-FC) with the MT+ was significantly increased after training in widespread cortical regions including the bilateral sensorimotor and temporal cortices. In contrast, rs-FC with the MT+ was significantly decreased in subcortical regions including the thalamus and putamen. Importantly, the training-induced change in rs-FC was observed only with the MT+, but not with the dACC or insula. Thus, our findings suggest that perceptual training induces plastic changes in offline functional connectivity specifically in brain regions representing the trained visual feature, emphasising the distinct roles of feature-representation regions and decision-related regions in VPL.

Tips for longwall conveyor chain

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

Courtney, J.C.; Fortune, J.

1977-03-01

Mining chain must be capable of sustaining imposed loads of 80 or 90 percent of its minimum breaking strength, and still return to its original dimensions. This means that the chain should behave like an extremely sophisticated spring up to very high load levels, and return to its unloaded state without any plastic stretch or permanent dimensional deformation having taken place. This feature is absolutely essential, not only to enable repeat loadings to be made, but to ensure that constant, precise engagement of the drive and idler sprockets takes place. The mining chain production process, therefore, is carefully designed somore » that the last stage of manufacture introduces these necessary features.« less

Real-time assessment of mental workload using psychophysiological measures and artificial neural networks.

PubMed

Wilson, Glenn F; Russell, Christopher A

The functional state of the human operator is critical to optimal system performance. Degraded states of operator functioning can lead to errors and overall suboptimal system performance. Accurate assessment of operator functional state is crucial to the successful implementation of an adaptive aiding system. One method of determining operators' functional state is by monitoring their physiology. In the present study, artificial neural networks using physiological signals were used to continuously monitor, in real time, the functional state of 7 participants while they performed the Multi-Attribute Task Battery with two levels of task difficulty. Six channels of brain electrical activity and eye, heart and respiration measures were evaluated on line. The accuracy of the classifier was determined to test its utility as an on-line measure of operator state. The mean classification accuracies were 85%, 82%, and 86% for the baseline, low task difficulty, and high task difficulty conditions, respectively. The high levels of accuracy suggest that these procedures can be used to provide accurate estimates of operator functional state that can be used to provide adaptive aiding. The relative contribution of each of the 43 psychophysiological features was also determined. Actual or potential applications of this research include test and evaluation and adaptive aiding implementation.

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.

PubMed

Awais, Muhammad; Badruddin, Nasreen; Drieberg, Micheal

2017-08-31

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t -tests to select only statistically significant features ( p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

PubMed Central

Badruddin, Nasreen

2017-01-01

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear. PMID:28858220

Isomerization, Perturbations, Calculations and the S_{1} State of C_{2}H_{2}

NASA Astrophysics Data System (ADS)

Baraban, J. H.; Changala, P. B.; Berk, J. R. P.; Field, R. W.; Stanton, J. F.; Merer, A. J.

2013-06-01

Preliminary analysis of the energy region of the cis-trans isomerization transition state on the S_{1} surface of C_{2}H_{2} has revealed novel patterns and surprising perturbations, including unusually large (and high-order) anharmonicities, as well as K-staggerings of several vibrational levels. These effects complicate the analysis considerably, and require new models and calculations to account for and predict features of the observed spectra. The ˜{A}-˜{X} spectrum of acetylene has been studied both experimentally and theoretically for almost a century, and this cycle of unexpected phenomena eliciting innovative responses is found throughout its history. Especially in the last ten years, progress in understanding the S_{1} state rovibrational level structure and cis-trans isomerization has been accelerated by combining the information available from both ab initio computation and spectroscopic observations. The resulting dialogue has then frequently suggested fruitful avenues for further experiments and calculations. Current challenges and recent results in understanding the cis-trans isomerization transition state region will be discussed in this context.

Solid State Television Camera (CID)

NASA Technical Reports Server (NTRS)

Steele, D. W.; Green, W. T.

1976-01-01

The design, development and test are described of a charge injection device (CID) camera using a 244x248 element array. A number of video signal processing functions are included which maximize the output video dynamic range while retaining the inherently good resolution response of the CID. Some of the unique features of the camera are: low light level performance, high S/N ratio, antiblooming, geometric distortion, sequential scanning and AGC.

Parental Intentions to Enroll Children in a Voluntary Expanded Newborn Screening Program

PubMed Central

Paquin, Ryan S.; Peay, Holly L.; Gehtland, Lisa M.; Lewis, Megan A.; Bailey, Donald B.

2016-01-01

Background and Objectives Nearly all babies in the United States are tested at birth for rare, serious, and treatable disorders through mandatory state newborn screening (NBS). Recently, there have been calls for an expanded, voluntary model to facilitate early diagnosis and treatment of a wider range of disorders. We applied the reasoned action framework to examine parental intentions to participate in voluntary expanded screening. Methods We recruited a national cohort of recent and expectant parents living in the U.S. who completed a self-administered online survey (N = 1,001). Using a mixed-level fractional factorial experiment, we studied parental participation intentions and preferences for timing of consent, cost, consent format, and testing options. Results We conducted a hierarchical regression analysis assessing parental intentions to participate in voluntary expanded NBS. Attitudes, perceived normative influence, and perceived behavioral control explained substantial variance in intention, with perceived normative influence emerging as the strongest predictor. We found no evidence that the manipulated program features altered mean levels of intention, but timing of parental permission, cost, and permission format moderated the relative importance of reasoned action constructs on intention. Conclusion Program design features may impact the psychological mechanisms underlying parental decision making for voluntary expanded screening. These results have important implications for parent education, outreach, and informed parental permission procedures. PMID:27526258

State “Technical Assistance Programs” for nursing home quality improvement: variations and potential implications

PubMed Central

Li, Yue; Spector, Williams D.; Glance, Laurent G.; Mukamel, Dana B.

2013-01-01

Context To improve nursing home quality, many states developed “Technical Assistance Programs” that provide on-site consultation and training for nursing facility staff. Methods We conducted a national survey on these state programs to collect data on program design, operations, financing, and perceived effectiveness. Results As of 2010, 17 states have developed such programs. Compared to existing state nursing home quality regulations, these programs represent a collaborative, rather than enforcement-oriented, approach to quality. However, existing programs vary substantially in key structural features such as staffing patterns, funding levels, and relationship with state survey and certification agencies. Perceived effectiveness by program officials on quality was high, although few states have performed formal evaluations. Perceived barriers to program effectiveness included lack of appropriate staff and funding, among others. Conclusion State “Technical Assistance Programs” for nursing homes varies in program design and perceived effectiveness. Future comparative evaluations are needed to inform evidence-based quality initiatives. PMID:23216345

An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition.

PubMed

Luo, Ling; Yang, Zhihao; Yang, Pei; Zhang, Yin; Wang, Lei; Lin, Hongfei; Wang, Jian

2018-04-15

In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction. However, most popular chemical NER methods are based on traditional machine learning and their performances are heavily dependent on the feature engineering. Moreover, these methods are sentence-level ones which have the tagging inconsistency problem. In this paper, we propose a neural network approach, i.e. attention-based bidirectional Long Short-Term Memory with a conditional random field layer (Att-BiLSTM-CRF), to document-level chemical NER. The approach leverages document-level global information obtained by attention mechanism to enforce tagging consistency across multiple instances of the same token in a document. It achieves better performances with little feature engineering than other state-of-the-art methods on the BioCreative IV chemical compound and drug name recognition (CHEMDNER) corpus and the BioCreative V chemical-disease relation (CDR) task corpus (the F-scores of 91.14 and 92.57%, respectively). Data and code are available at https://github.com/lingluodlut/Att-ChemdNER. yangzh@dlut.edu.cn or wangleibihami@gmail.com. Supplementary data are available at Bioinformatics online.

The origin of absorptive features in the two-dimensional electronic spectra of rhodopsin.

PubMed

Farag, Marwa H; Jansen, Thomas L C; Knoester, Jasper

2018-05-09

In rhodopsin, the absorption of a photon causes the isomerization of the 11-cis isomer of the retinal chromophore to its all-trans isomer. This isomerization is known to occur through a conical intersection (CI) and the internal conversion through the CI is known to be vibrationally coherent. Recently measured two-dimensional electronic spectra (2DES) showed dramatic absorptive spectral features at early waiting times associated with the transition through the CI. The common two-state two-mode model Hamiltonian was unable to elucidate the origin of these features. To rationalize the source of these features, we employ a three-state three-mode model Hamiltonian where the hydrogen out-of plane (HOOP) mode and a higher-lying electronic state are included. The 2DES of the retinal chromophore in rhodopsin are calculated and compared with the experiment. Our analysis shows that the source of the observed features in the measured 2DES is the excited state absorption to a higher-lying electronic state and not the HOOP mode.

Machine-learning-based diagnosis of schizophrenia using combined sensor-level and source-level EEG features.

PubMed

Shim, Miseon; Hwang, Han-Jeong; Kim, Do-Won; Lee, Seung-Hwan; Im, Chang-Hwan

2016-10-01

Recently, an increasing number of researchers have endeavored to develop practical tools for diagnosing patients with schizophrenia using machine learning techniques applied to EEG biomarkers. Although a number of studies showed that source-level EEG features can potentially be applied to the differential diagnosis of schizophrenia, most studies have used only sensor-level EEG features such as ERP peak amplitude and power spectrum for machine learning-based diagnosis of schizophrenia. In this study, we used both sensor-level and source-level features extracted from EEG signals recorded during an auditory oddball task for the classification of patients with schizophrenia and healthy controls. EEG signals were recorded from 34 patients with schizophrenia and 34 healthy controls while each subject was asked to attend to oddball tones. Our results demonstrated higher classification accuracy when source-level features were used together with sensor-level features, compared to when only sensor-level features were used. In addition, the selected sensor-level features were mostly found in the frontal area, and the selected source-level features were mostly extracted from the temporal area, which coincide well with the well-known pathological region of cognitive processing in patients with schizophrenia. Our results suggest that our approach would be a promising tool for the computer-aided diagnosis of schizophrenia. Copyright © 2016 Elsevier B.V. All rights reserved.

Feature-selective attention enhances color signals in early visual areas of the human brain.

PubMed

Müller, M M; Andersen, S; Trujillo, N J; Valdés-Sosa, P; Malinowski, P; Hillyard, S A

2006-09-19

We used an electrophysiological measure of selective stimulus processing (the steady-state visual evoked potential, SSVEP) to investigate feature-specific attention to color cues. Subjects viewed a display consisting of spatially intermingled red and blue dots that continually shifted their positions at random. The red and blue dots flickered at different frequencies and thereby elicited distinguishable SSVEP signals in the visual cortex. Paying attention selectively to either the red or blue dot population produced an enhanced amplitude of its frequency-tagged SSVEP, which was localized by source modeling to early levels of the visual cortex. A control experiment showed that this selection was based on color rather than flicker frequency cues. This signal amplification of attended color items provides an empirical basis for the rapid identification of feature conjunctions during visual search, as proposed by "guided search" models.

Learning From Where Students Look While Observing Simulated Physical Phenomena

NASA Astrophysics Data System (ADS)

Demaree, Dedra

2005-04-01

The Physics Education Research (PER) Group at the Ohio State University (OSU) has developed Virtual Reality (VR) programs for teaching introductory physics concepts. Winter 2005, the PER group worked with OSU's cognitive science eye-tracking lab to probe what features students look at while using our VR programs. We see distinct differences in the features students fixate on depending upon whether or not they have formally studied the related physics. Students who first make predictions seem to fixate more on the relevant features of the simulation than those who do not, regardless of their level of education. It is known that students sometimes perform an experiment and report results consistent with their misconceptions but inconsistent with the experimental outcome. We see direct evidence of one student holding onto misconceptions despite fixating frequently on the information needed to understand the correct answer. Future studies using these technologies may prove valuable for tackling difficult questions regarding student learning.

Special object extraction from medieval books using superpixels and bag-of-features

NASA Astrophysics Data System (ADS)

Yang, Ying; Rushmeier, Holly

2017-01-01

We propose a method to extract special objects in images of medieval books, which generally represent, for example, figures and capital letters. Instead of working on the single-pixel level, we consider superpixels as the basic classification units for improved time efficiency. More specifically, we classify superpixels into different categories/objects by using a bag-of-features approach, where a superpixel category classifier is trained with the local features of the superpixels of the training images. With the trained classifier, we are able to assign the category labels to the superpixels of a historical document image under test. Finally, special objects can easily be identified and extracted after analyzing the categorization results. Experimental results demonstrate that, as compared to the state-of-the-art algorithms, our method provides comparable performance for some historical books but greatly outperforms them in terms of generality and computational time.

Gutzwiller wave-function solution for Anderson lattice model: Emerging universal regimes of heavy quasiparticle states

NASA Astrophysics Data System (ADS)

Wysokiński, Marcin M.; Kaczmarczyk, Jan; Spałek, Jozef

2015-09-01

The recently proposed diagrammatic expansion (DE) technique for the full Gutzwiller wave function (GWF) is applied to the Anderson lattice model. This approach allows for a systematic evaluation of the expectation values with full Gutzwiller wave function in finite-dimensional systems. It introduces results extending in an essential manner those obtained by means of the standard Gutzwiller approximation (GA), which is variationally exact only in infinite dimensions. Within the DE-GWF approach we discuss the principal paramagnetic properties and their relevance to heavy-fermion systems. We demonstrate the formation of an effective, narrow f band originating from atomic f -electron states and subsequently interpret this behavior as a direct itineracy of f electrons; it represents a combined effect of both the hybridization and the correlations induced by the Coulomb repulsive interaction. Such a feature is absent on the level of GA, which is equivalent to the zeroth order of our expansion. Formation of the hybridization- and electron-concentration-dependent narrow f band rationalizes the common assumption of such dispersion of f levels in the phenomenological modeling of the band structure of CeCoIn5. Moreover, it is shown that the emerging f -electron direct itineracy leads in a natural manner to three physically distinct regimes within a single model that are frequently discussed for 4 f - or 5 f -electron compounds as separate model situations. We identify these regimes as (i) the mixed-valence regime, (ii) Kondo/almost-Kondo insulating regime, and (iii) the Kondo-lattice limit when the f -electron occupancy is very close to the f -state half filling, →1 . The nonstandard features of the emerging correlated quantum liquid state are stressed.

Assessment of medication adherence app features, functionality, and health literacy level and the creation of a searchable Web-based adherence app resource for health care professionals and patients.

PubMed

Heldenbrand, Seth; Martin, Bradley C; Gubbins, Paul O; Hadden, Kristie; Renna, Catherine; Shilling, Rebecca; Dayer, Lindsey

2016-01-01

To assess the features and level of health literacy (HL) of available medication adherence apps and to create a searchable website to assist health care providers (HCP) and patients identify quality adherence apps. Medication nonadherence continues to be a significant problem and leads to poor health outcomes and avoidable health care expense. The average adherence rate for chronic medications, regardless of disease state, is approximately 50% leaving significant room for improvement. Smartphone adherence apps are a novel resource to address medication nonadherence. With widespread smartphone use and the growing number of adherence apps, both HCP and patients should be able to identify quality adherence apps to maximize potential benefits. Assess the features, functionality and level of HL of available adherence apps and create a searchable website to help both HCP and patients identify quality adherence apps. Online marketplaces (iTunes, Google Play, Blackberry) were searched in June of 2014 to identify available adherence apps. Online descriptions were recorded and scored based on 28 author-identified features across 4 domains. The 100 highest-scoring apps were user-tested with a standardized regimen to evaluate their functionality and level of HL. 461 adherence apps were identified. 367 unique apps were evaluated after removing "Lite/Trial" versions. The median initial score based on descriptions was 15 (max of 68; range: 3 to 47). Only 77 apps of the top 100 highest-scoring apps completed user-testing and HL evaluations. The median overall user-testing score was 30 (max of 73; range: 16 to 55). App design, functionality, and level of HL varies widely among adherence apps. While no app is perfect, several apps scored highly across all domains. The website www.medappfinder.com is a searchable tool that helps HCP and patients identify quality apps in a crowded marketplace. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Attosecond transient absorption of argon atoms in the vacuum ultraviolet region: line energy shifts versus coherent population transfer

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

Cao, Wei; Warrick, Erika R.; Neumark, Daniel M.

Using attosecond transient absorption, the dipole response of an argon atom in the vacuum ultraviolet (VUV) region is studied when an external electromagnetic field is present. An isolated attosecond VUV pulse populates Rydberg states lying 15 eV above the argon ground state. A synchronized few-cycle near infrared (NIR) pulse modifies the oscillating dipoles of argon impulsively, leading to alterations in the VUV absorption spectra. As the NIR pulse is delayed with respect to the VUV pulse, multiple features in the absorption profile emerge simultaneously including line broadening, sideband structure, sub-cycle fast modulations, and 5-10 fs slow modulations. These features indicatemore » the coexistence of two general processes of the light-matter interaction: the energy shift of individual atomic levels and coherent population transfer between atomic eigenstates, revealing coherent superpositions. Finally, an intuitive formula is derived to treat both effects in a unifying framework, allowing one to identify and quantify the two processes in a single absorption spectrogram.« less

Attosecond transient absorption of argon atoms in the vacuum ultraviolet region: line energy shifts versus coherent population transfer

NASA Astrophysics Data System (ADS)

Cao, Wei; Warrick, Erika R.; Neumark, Daniel M.; Leone, Stephen R.

2016-01-01

Using attosecond transient absorption, the dipole response of an argon atom in the vacuum ultraviolet (VUV) region is studied when an external electromagnetic field is present. An isolated attosecond VUV pulse populates Rydberg states lying 15 eV above the argon ground state. A synchronized few-cycle near infrared (NIR) pulse modifies the oscillating dipoles of argon impulsively, leading to alterations in the VUV absorption spectra. As the NIR pulse is delayed with respect to the VUV pulse, multiple features in the absorption profile emerge simultaneously including line broadening, sideband structure, sub-cycle fast modulations, and 5-10 fs slow modulations. These features indicate the coexistence of two general processes of the light-matter interaction: the energy shift of individual atomic levels and coherent population transfer between atomic eigenstates, revealing coherent superpositions. An intuitive formula is derived to treat both effects in a unifying framework, allowing one to identify and quantify the two processes in a single absorption spectrogram.

Attosecond transient absorption of argon atoms in the vacuum ultraviolet region: line energy shifts versus coherent population transfer

DOE PAGES

Cao, Wei; Warrick, Erika R.; Neumark, Daniel M.; ...

2016-01-18

Using attosecond transient absorption, the dipole response of an argon atom in the vacuum ultraviolet (VUV) region is studied when an external electromagnetic field is present. An isolated attosecond VUV pulse populates Rydberg states lying 15 eV above the argon ground state. A synchronized few-cycle near infrared (NIR) pulse modifies the oscillating dipoles of argon impulsively, leading to alterations in the VUV absorption spectra. As the NIR pulse is delayed with respect to the VUV pulse, multiple features in the absorption profile emerge simultaneously including line broadening, sideband structure, sub-cycle fast modulations, and 5-10 fs slow modulations. These features indicatemore » the coexistence of two general processes of the light-matter interaction: the energy shift of individual atomic levels and coherent population transfer between atomic eigenstates, revealing coherent superpositions. Finally, an intuitive formula is derived to treat both effects in a unifying framework, allowing one to identify and quantify the two processes in a single absorption spectrogram.« less

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

DOE PAGES

Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul; ...

2017-12-20

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differentialmore » equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.« less

A Compilation of Spatial Datasets to Support a Preliminary Assessment of Pesticides and Pesticide Use on Tribal Lands in Oklahoma

USGS Publications Warehouse

Mashburn, Shana L.; Winton, Kimberly T.

2010-01-01

This CD-ROM contains spatial datasets that describe natural and anthropogenic features and county-level estimates of agricultural pesticide use and pesticide data for surface-water, groundwater, and biological specimens in the state of Oklahoma. County-level estimates of pesticide use were compiled from the Pesticide National Synthesis Project of the U.S. Geological Survey, National Water-Quality Assessment Program. Pesticide data for surface water, groundwater, and biological specimens were compiled from U.S. Geological Survey National Water Information System database. These spatial datasets that describe natural and manmade features were compiled from several agencies and contain information collected by the U.S. Geological Survey. The U.S. Geological Survey datasets were not collected specifically for this compilation, but were previously collected for projects with various objectives. The spatial datasets were created by different agencies from sources with varied quality. As a result, features common to multiple layers may not overlay exactly. Users should check the metadata to determine proper use of these spatial datasets. These data were not checked for accuracy or completeness. If a question of accuracy or completeness arise, the user should contact the originator cited in the metadata.

pyomo.dae: a modeling and automatic discretization framework for optimization with differential and algebraic equations

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

Nicholson, Bethany; Siirola, John D.; Watson, Jean-Paul

We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differentialmore » equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.« less

Dynamical manifestations of quantum chaos

NASA Astrophysics Data System (ADS)

Torres Herrera, Eduardo Jonathan; Santos, Lea

2017-04-01

A main feature of a chaotic quantum system is a rigid spectrum, where the levels do not cross. Dynamical quantities, such as the von Neumann entanglement entropy, Shannon information entropy, and out-of-time correlators can differentiate the ergodic from the nonergodic phase in disordered interacting systems, but not level repulsion from level crossing in the delocalized phase of disordered and clean models. This is in contrast with the long-time evolution of the survival probability of the initial state. The onset of correlated energy levels is manifested by a drop, referred to as correlation hole, below the asymptotic value of the survival probability. The correlation hole is an unambiguous indicator of the presence of level repulsion. EJTH is grateful to VIEP, BUAP for financial support through the VIEP projects program.

Reduced Perceptual Exclusivity during Object and Grating Rivalry in Autism

PubMed Central

Freyberg, J.; Robertson, C.E.; Baron-Cohen, S.

2015-01-01

Background The dynamics of binocular rivalry may be a behavioural footprint of excitatory and inhibitory neural transmission in visual cortex. Given the presence of atypical visual features in Autism Spectrum Conditions (ASC), and evidence in support of the idea of an imbalance in excitatory/inhibitory neural transmission in ASC, we hypothesized that binocular rivalry might prove a simple behavioural marker of such a transmission imbalance in the autistic brain. In support of this hypothesis, we previously reported a slower rate of rivalry in ASC, driven by reduced perceptual exclusivity. Methods We tested whether atypical dynamics of binocular rivalry in ASC are specific to certain stimulus features. 53 participants (26 with ASC, matched for age, sex and IQ) participated in binocular rivalry experiments in which the dynamics of rivalry were measured at two levels of stimulus complexity, low (grayscale gratings) and high (coloured objects). Results Individuals with ASC experienced a slower rate of rivalry, driven by longer transitional states between dominant percepts. These exaggerated transitional states were present at both low and high levels of stimulus complexity, suggesting that atypical rivalry dynamics in autism are robust with respect to stimulus choice. Interactions between stimulus properties and rivalry dynamics in autism indicate that achromatic grating stimuli produce stronger group differences. Conclusion These results confirm the finding of atypical dynamics of binocular rivalry in ASC. These dynamics were present for stimuli of both low and high levels of visual complexity, suggesting an imbalance in competitive interactions throughout the visual system of individuals with ASC. PMID:26382002

Local quantum uncertainty guarantees the measurement precision for two coupled two-level systems in non-Markovian environment

NASA Astrophysics Data System (ADS)

Wu, Shao-xiong; Zhang, Yang; Yu, Chang-shui

2018-03-01

Quantum Fisher information (QFI) is an important feature for the precision of quantum parameter estimation based on the quantum Cramér-Rao inequality. When the quantum state satisfies the von Neumann-Landau equation, the local quantum uncertainty (LQU), as a kind of quantum correlation, present in a bipartite mixed state guarantees a lower bound on QFI in the optimal phase estimation protocol (Girolami et al., 2013). However, in the open quantum systems, there is not an explicit relation between LQU and QFI generally. In this paper, we study the relation between LQU and QFI in open systems which is composed of two interacting two-level systems coupled to independent non-Markovian environments with the entangled initial state embedded by a phase parameter θ. The analytical calculations show that the QFI does not depend on the phase parameter θ, and its decay can be restrained through enhancing the coupling strength or non-Markovianity. Meanwhile, the LQU is related to the phase parameter θ and shows plentiful phenomena. In particular, we find that the LQU can well bound the QFI when the coupling between the two systems is switched off or the initial state is Bell state.

Electronic and structural properties of vacancies and hydrogen adsorbates on trilayer graphene

NASA Astrophysics Data System (ADS)

Menezes, Marcos G.; Capaz, Rodrigo B.

2015-08-01

Using ab initio calculations, we study the electronic and structural properties of vacancies and hydrogen adsorbates on trilayer graphene. Those defects are found to share similar low-energy electronic features, since they both remove a pz electron from the honeycomb lattice and induce a defect level near the Fermi energy. However, a vacancy also leaves unpaired σ electrons on the lattice, which lead to important structural differences and also contribute to magnetism. We explore both ABA and ABC stackings and compare properties such as formation energies, magnetic moments, spin density and the local density of states (LDOS) of the defect levels. These properties show a strong sensitivity to the layer in which the defect is placed and smaller sensitivities to sublattice placing and stacking type. Finally, for the ABC trilayer, we also study how these states behave in the presence of an external field, which opens a tunable gap in the band structure of the non-defective system. The pz defect states show a strong hybridization with band states as the field increases, with reduction and eventually loss of magnetization, and a non-magnetic, midgap-like state is found when the defect is at the middle layer.

Electronic and Structural Properties of Vacancies and Hydrogen Adsorbates on Trilayer Graphene

NASA Astrophysics Data System (ADS)

Menezes, Marcos; Capaz, Rodrigo

2015-03-01

Using ab initio calculations, we study the electronic and structural properties of vacancies and hydrogen adsorbates on trilayer graphene. Those defects are found to share similar low-energy electronic features, since they both remove a pz electron from the honeycomb lattice and induce a defect level near the Fermi energy. However, a vacancy also leaves unpaired σ electrons on the lattice, which lead to important structural differences and also contribute to magnetism. We explore both ABA and ABC stackings and compare properties such as formation energies, magnetic moments, spin density and the local density of states (LDOS) of the defect levels. These properties show a strong sensitivity to the layer in which the defect is placed and smaller sensitivities to sublattice placing and stacking type. Finally, for the ABC trilayer, we also study how these states behave in the presence of an external electrical field, which opens a tunable gap in the band structure of the non-defective system. The pz defect states show a strong hybridization with band states as the field increases, with reduction and eventually loss of magnetization, and a non-magnetic, midgap-like state is found when the defect is at the middle layer.

Viewing brain processes as Critical State Transitions across levels of organization: Neural events in Cognition and Consciousness, and general principles.

PubMed

Werner, Gerhard

2009-04-01

In this theoretical and speculative essay, I propose that insights into certain aspects of neural system functions can be gained from viewing brain function in terms of the branch of Statistical Mechanics currently referred to as "Modern Critical Theory" [Stanley, H.E., 1987. Introduction to Phase Transitions and Critical Phenomena. Oxford University Press; Marro, J., Dickman, R., 1999. Nonequilibrium Phase Transitions in Lattice Models. Cambridge University Press, Cambridge, UK]. The application of this framework is here explored in two stages: in the first place, its principles are applied to state transitions in global brain dynamics, with benchmarks of Cognitive Neuroscience providing the relevant empirical reference points. The second stage generalizes to suggest in more detail how the same principles could also apply to the relation between other levels of the structural-functional hierarchy of the nervous system and between neural assemblies. In this view, state transitions resulting from the processing at one level are the input to the next, in the image of a 'bucket brigade', with the content of each bucket being passed on along the chain, after having undergone a state transition. The unique features of a process of this kind will be discussed and illustrated.

Systematics of g factors of 2{sub 1}{sup +} states in even-even nuclei from Gd to Pt: A microscopic description by the projected shell model

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

Bian, Bao-An; Institute of Low Energy Nuclear Physics, Beijing Normal University, Beijing 100875; Di, Yao-Min

2007-01-15

The systematics of g factor of the first excited 2{sup +} state vs neutron number N is studied by the projected shell model. The study covers the even-even nuclei of all isotopic chains from Gd to Pt. g factors are calculated by using the many-body wave functions that well reproduce the energy levels and B(E2)s of the ground-state bands. For Gd to W isotopes the characteristic feature of the g factor data along an isotopic chain is described by the present model. Deficiency of the model in the g factor description for the heavier Os and Pt isotopes is discussed.

Low-lying structure and shape evolution in neutron-rich Se isotopes

NASA Astrophysics Data System (ADS)

Chen, S.; Doornenbal, P.; Obertelli, A.; Rodríguez, T. R.; Authelet, G.; Baba, H.; Calvet, D.; Château, F.; Corsi, A.; Delbart, A.; Gheller, J.-M.; Giganon, A.; Gillibert, A.; Lapoux, V.; Motobayashi, T.; Niikura, M.; Paul, N.; Roussé, J.-Y.; Sakurai, H.; Santamaria, C.; Steppenbeck, D.; Taniuchi, R.; Uesaka, T.; Ando, T.; Arici, T.; Blazhev, A.; Browne, F.; Bruce, A. M.; Caroll, R.; Chung, L. X.; Cortés, M. L.; Dewald, M.; Ding, B.; Flavigny, F.; Franchoo, S.; Górska, M.; Gottardo, A.; Jungclaus, A.; Lee, J.; Lettmann, M.; Linh, B. D.; Liu, J.; Liu, Z.; Lizarazo, C.; Momiyama, S.; Moschner, K.; Nagamine, S.; Nakatsuka, N.; Nita, C. R.; Nobs, C.; Olivier, L.; Orlandi, R.; Patel, Z.; Podolyak, Zs.; Rudigier, M.; Saito, T.; Shand, C.; Söderström, P.-A.; Stefan, I.; Vaquero, V.; Werner, V.; Wimmer, K.; Xu, Z.

2017-04-01

Neutron-rich 88,90,92,94Se isotopes were studied via in-beam γ -ray spectroscopy after nucleon removal reactions at intermediate energies at the Radioactive Isotope Beam Factory. Based on γ -γ coincidence analysis, low-lying excitation level schemes are proposed for these nuclei, including the 21+, 41+ states and 22+ states at remarkably low energies. The low-lying 22+ states, along with other features, indicate triaxiality in these nuclei. The experimental results are in good overall agreement with self-consistent beyond-mean-field calculations based on the Gogny D1S interaction, which suggests both triaxial degree of freedom and shape coexistence playing important roles in the description of intrinsic deformations in neutron-rich Se isotopes.

Hospital Quality And Intensity Of Spending: Is There An Association?

PubMed Central

Yasaitis, Laura; Fisher, Elliott S.; Skinner, Jonathan S.

2009-01-01

Numerous studies in the United States have examined the association between quality and spending at the regional level. In this paper we evaluate this relationship at the level of individual hospitals, which are a more natural unit of analysis for reporting on and improving accountability. For all of the quality indicators studied, the association with spending is either nil or negative. The absence of positive correlations suggests that some institutions achieve exemplary performance on quality measures in settings that feature lower intensity of care. This finding highlights the need for reporting information on both quality and spending. PMID:19460774

Collisional quenching at ultralow energies: controlling efficiency with internal state selection.

PubMed

Bovino, S; Bodo, E; Gianturco, F A

2007-12-14

Calculations have been carried out for the vibrational quenching of excited H(2) molecules which collide with Li(+) ions at ultralow energies. The dynamics has been treated exactly using the well-known quantum coupled-channel expansions over different initial vibrational levels. The overall interaction potential has been obtained from the calculations carried out earlier by our group using highly correlated ab initio methods. The results indicate that specific features of the scattering observables, e.g., the appearance of Ramsauer-Townsend minima in elastic channel cross sections and the marked increase of the cooling rates from specific initial states, can be linked to potential properties at vanishing energies (sign and size of scattering lengths) and to the presence of either virtual states or bound states. The suggestion is made such that by selecting the initial state preparation of the molecular partners, the ionic interactions would be amenable to controlling quenching efficiency at ultralow energies.

Core excitations across the neutron shell gap in 207Tl

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

Wilson, E.; Podolyák, Zs.; Grawe, H.

2015-05-05

The single closed-neutron-shell, one proton–hole nucleus 207Tl was populated in deep-inelastic collisions of a 208Pb beam with a 208Pb target. The yrast and near-yrast level scheme has been established up to high excitation energy, comprising an octupole phonon state and a large number of core excited states. Based on shell-model calculations, all observed single core excitations were established to arise from the breaking of the N=126 neutron core. While the shell-model calculations correctly predict the ordering of these states, their energies are compressed at high spins. It is concluded that this compression is an intrinsic feature of shell-model calculations usingmore » two-body matrix elements developed for the description of two-body states, and that multiple core excitations need to be considered in order to accurately calculate the energy spacings of the predominantly three-quasiparticle states.« less

Multi-category micro-milling tool wear monitoring with continuous hidden Markov models

NASA Astrophysics Data System (ADS)

Zhu, Kunpeng; Wong, Yoke San; Hong, Geok Soon

2009-02-01

In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods.

On the Multilevel Solution Algorithm for Markov Chains

NASA Technical Reports Server (NTRS)

Horton, Graham

1997-01-01

We discuss the recently introduced multilevel algorithm for the steady-state solution of Markov chains. The method is based on an aggregation principle which is well established in the literature and features a multiplicative coarse-level correction. Recursive application of the aggregation principle, which uses an operator-dependent coarsening, yields a multi-level method which has been shown experimentally to give results significantly faster than the typical methods currently in use. When cast as a multigrid-like method, the algorithm is seen to be a Galerkin-Full Approximation Scheme with a solution-dependent prolongation operator. Special properties of this prolongation lead to the cancellation of the computationally intensive terms of the coarse-level equations.

Serum tumour necrosis factor-α and interleukin-6 levels in Alzheimer's disease and mild cognitive impairment.

PubMed

Kim, Yo Sup; Lee, Kang Joon; Kim, Hyun

2017-07-01

Neuroinflammation has been recognized as a feature of Alzheimer's disease (AD), and mild cognitive impairment (MCI) is believed to share several pathological features with AD. The aim of the present study was to compare serum cytokine levels between patients with AD, subjects with MCI, and healthy controls, and to assess the correlation between cytokine levels and cognitive performance in these subjects. Participants included 35 patients with AD, 29 subjects with MCI, and 28 healthy controls from the Department of Psychiatry of IIlsan Paik Hospital in South Korea. Demographic and neuropsychological information were obtained, and peripheral cytokine levels, specifically tumour necrosis factor (TNF)-α and interleukin (IL)-6 levels, were measured for all subjects. After adjustment for age, a significant difference in IL-6 levels (P = 0.045), but not in TNF-α (P = 0.082) levels, was observed among the three groups. IL-6 levels were higher in patients with AD than in subjects with MCI and healthy controls. TNF-α and IL-6 levels negatively and positively correlated with Mini-Mental State Examination and Global Deterioration Scale scores, respectively. TNF-α and IL-6 levels were also positively correlated with each other. The present study suggests that serum IL-6 levels of patients with AD might be higher than those of subjects with MCI and healthy controls. Serum TNF-α and IL-6 levels might be negatively correlated with cognitive function, and we suspect that serum IL-6 levels could be biomarkers for AD. © 2017 Japanese Psychogeriatric Society.

Origin of subgap states in amorphous In-Ga-Zn-O

NASA Astrophysics Data System (ADS)

Körner, Wolfgang; Urban, Daniel F.; Elsässer, Christian

2013-10-01

We present a density functional theory analysis of stoichiometric and nonstoichiometric, crystalline and amorphous In-Ga-Zn-O (c-IGZO, a-IGZO), which connects the recently experimentally discovered electronic subgap states to structural features of a-IGZO. In particular, we show that undercoordinated oxygen atoms create electronic defect levels in the lower half of the band gap up to about 1.5 eV above the valence band edge. As a second class of fundamental defects that appear in a-IGZO, we identify mainly pairs of metal atoms which are not separated by oxygen atoms in between. These defects cause electronic defect levels in the upper part of the band gap. Furthermore, we show that hydrogen doping can suppress the deep levels due to undercoordinated oxygen atoms while those of metal defects just undergo a shift within the band gap. Altogether our results provide an explanation for the experimentally observed effect that hydrogen doping increases the transparency and improves the conductivity of a-IGZO.

The 3D Elevation Program: summary for Michigan

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation's natural and constructed features. The Michigan Statewide Authoritative Imagery and Lidar (MiSAIL) program provides statewide lidar coordination with local, State, and national groups in support of 3DEP for Michigan.

Proximity soundings of thundersnow in the central United States

NASA Astrophysics Data System (ADS)

Market, Patrick S.; Oravetz, Angela M.; Gaede, David; Bookbinder, Evan; Lupo, Anthony R.; Melick, Christopher J.; Smith, Larry L.; Thomas, Rashida; Redburn, Rachel; Pettegrew, Brian P.; Becker, Amy E.

2006-10-01

Proximity balloon soundings for snow events with lightning and thunder during the period 1961 through 1990 reveal a less statically stable environment than similar nonthundering snow events. When thundersnow is present, a less stable environment (and in some cases subsequent upright convection) is found aloft in all of the thundering cases examined here; all of the events feature their most unstable parcel originating above a frontal inversion. In fact, only events in the cold air north of an extratropical cyclone are included in this study. Events with a lake effect or orographic enhancement are eliminated from the sample. The basic composite derived by averaging temperatures at an established interval reveals a nearly saturated lower atmosphere, below 0°C throughout its depth, with the frontal inversion present and its most unstable parcel occurring just above the top of the inversion. The feature-preserving composite approach of R. A. Brown (1993) better defines the frontal inversion bottom and top as well as the level and temperature of the most unstable parcel; these are the features in need of preservation, and a less statically stable environment emerges by doing so. Other salient features include the most unstable parcel originating some 30-50 mbar above the top of the frontal inversion and significant drying ˜100 mbar above the level of the most unstable parcel. The bulk sounding characteristics also favor the existence of lightning. The composite temperature at the level of the most unstable parcel is -8.7°C, which allows for enhanced amounts of supercooled water to enter any updraft that may form. The temperature of the most unstable parcel at its origin is also warmer than the charge reversal temperature; therefore convection of any appreciable depth will span that level. Moreover, the height of the composited -10°C level is 2959 m above ground level, which previous investigators have shown is sufficiently high to favor lightning production. Yet no convective available potential energy (CAPE) appears with either composite approach, which concurs with previous studies. While several of the composite members feature CAPE for elevated layers, the majority do not, suggesting that other processes (e.g., the release of symmetric instability), which are difficult to assess from a single sounding, tend to be at work.

Document-level classification of CT pulmonary angiography reports based on an extension of the ConText algorithm.

PubMed

Chapman, Brian E; Lee, Sean; Kang, Hyunseok Peter; Chapman, Wendy W

2011-10-01

In this paper we describe an application called peFinder for document-level classification of CT pulmonary angiography reports. peFinder is based on a generalized version of the ConText algorithm, a simple text processing algorithm for identifying features in clinical report documents. peFinder was used to answer questions about the disease state (pulmonary emboli present or absent), the certainty state of the diagnosis (uncertainty present or absent), the temporal state of an identified pulmonary embolus (acute or chronic), and the technical quality state of the exam (diagnostic or not diagnostic). Gold standard answers for each question were determined from the consensus classifications of three human annotators. peFinder results were compared to naive Bayes' classifiers using unigrams and bigrams. The sensitivities (and positive predictive values) for peFinder were 0.98(0.83), 0.86(0.96), 0.94(0.93), and 0.60(0.90) for disease state, quality state, certainty state, and temporal state respectively, compared to 0.68(0.77), 0.67(0.87), 0.62(0.82), and 0.04(0.25) for the naive Bayes' classifier using unigrams, and 0.75(0.79), 0.52(0.69), 0.59(0.84), and 0.04(0.25) for the naive Bayes' classifier using bigrams. Copyright © 2011 Elsevier Inc. All rights reserved.

Plasma and serum brain-derived neurotrophic factor (BDNF) levels and their association with neurocognition in at-risk mental state, first episode psychosis and chronic schizophrenia patients.

PubMed

Heitz, Ulrike; Papmeyer, Martina; Studerus, Erich; Egloff, Laura; Ittig, Sarah; Andreou, Christina; Vogel, Tobias; Borgwardt, Stefan; Graf, Marc; Eckert, Anne; Riecher-Rössler, Anita

2018-06-25

Brain-derived neurotrophic factor (BDNF) is involved in numerous cognitive processes. Since cognitive deficits are a core feature of psychotic disorders, the investigation of BDNF levels in psychosis and their correlation with cognition has received increased attention. However, there are no studies investigating BDNF levels in individuals with an at-risk mental state (ARMS) for psychosis. Hence, the aims of the present study were: (1) assessing peripheral BDNF levels across different (potential) stages of psychosis; (2) investigating their association with cognition. Plasma and serum BDNF levels and neuropsychological performance were assessed in 16 ARMS, six first-episode psychosis (FEP), and 11 chronic schizophrenia (CS) patients. Neuropsychological assessment covered intelligence, verbal memory, working memory, attention and executive functioning. Both plasma and serum BDNF levels were highest in CS, intermediate in FEP and lowest in ARMS. Multiple regression analysis revealed a significant positive association of plasma BDNF levels with planning ability across all groups. The lower peripheral BDNF levels in ARMS compared to FEP and CS might point towards an important drop of this neurotrophin prior to the onset of frank psychosis. The associations of peripheral BDNF with planning-abilities match previous findings.

Theoretical Discussion of Electron Transport Rate Constant at TCNQ / Ge and TiO2 System

NASA Astrophysics Data System (ADS)

Al-agealy, Hadi J. M.; Alshafaay, B.; Hassooni, Mohsin A.; Ashwiekh, Ahmed M.; Sadoon, Abbas K.; Majeed, Raad H.; Ghadhban, Rawnaq Q.; Mahdi, Shatha H.

2018-05-01

We have been studying and estimation the electronic transport constant at TCNQ / Ge and Tio2 interface by means of tunneling potential (TP), transport energy reorientation (TER), driving transition energy DTE and coupling coefficient constant. A simple quantum model for the transition processes was adapted to estimation and analysis depending on the quantum state for donor state |α D > and acceptor stated |α A > and assuming continuum levels of the system. Evaluation results were performed for the surfaces of Ge and Tio2 as best as for multilayer TCNQ. The results show an electronic transfer feature for electronic TCNQ density of states and a semiconductor behavior. The electronic rate constant result for both systems shows a good tool to election system in applied devices. All these results indicate the

Plasma metallurgical production of nanocrystalline borides and carbides

NASA Astrophysics Data System (ADS)

Galevsky, G. V.; Rudneva, V. V.; Cherepanov, A. N.; Galevsky, S. G.; Efimova, K. A.

2016-09-01

he experience in production and study of properties of nanocrystalline borides and chromium carbides, titanium, silicon was summarized. The design and features of the vertical three-jet once-through reactor with power 150 kW, used in the plasma metallurgical production, was described. The technological, thermotechnical and resource characteristics of the reactor were identified. The parameters of borides and carbides synthesis, their main characteristics in the nanodispersed state and equipment-technological scheme of production were provided. Evaluation of engineering-and-economical performance of the laboratory and industrial levels of borides and carbides production and the state corresponding to the segment of the world market was carried out.

Finger vein recognition based on the hyperinformation feature

NASA Astrophysics Data System (ADS)

Xi, Xiaoming; Yang, Gongping; Yin, Yilong; Yang, Lu

2014-01-01

The finger vein is a promising biometric pattern for personal identification due to its advantages over other existing biometrics. In finger vein recognition, feature extraction is a critical step, and many feature extraction methods have been proposed to extract the gray, texture, or shape of the finger vein. We treat them as low-level features and present a high-level feature extraction framework. Under this framework, base attribute is first defined to represent the characteristics of a certain subcategory of a subject. Then, for an image, the correlation coefficient is used for constructing the high-level feature, which reflects the correlation between this image and all base attributes. Since the high-level feature can reveal characteristics of more subcategories and contain more discriminative information, we call it hyperinformation feature (HIF). Compared with low-level features, which only represent the characteristics of one subcategory, HIF is more powerful and robust. In order to demonstrate the potential of the proposed framework, we provide a case study to extract HIF. We conduct comprehensive experiments to show the generality of the proposed framework and the efficiency of HIF on our databases, respectively. Experimental results show that HIF significantly outperforms the low-level features.

Tabu search and binary particle swarm optimization for feature selection using microarray data.

PubMed

Chuang, Li-Yeh; Yang, Cheng-Huei; Yang, Cheng-Hong

2009-12-01

Gene expression profiles have great potential as a medical diagnosis tool because they represent the state of a cell at the molecular level. In the classification of cancer type research, available training datasets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed to improve the predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this article, we propose to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection. BPSO acts as a local optimizer each time the TS has been run for a single generation. The K-nearest neighbor method with leave-one-out cross-validation and support vector machine with one-versus-rest serve as evaluators of the TS and BPSO. The proposed method is applied and compared to the 11 classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods.

Vibronic spectra of the p-benzoquinone radical anion and cation: a matrix isolation and computational study.

PubMed

Piech, Krzysztof; Bally, Thomas; Ichino, Takatoshi; Stanton, John

2014-02-07

The electronic and vibrational absorption spectra of the radical anion and cation of p-benzoquinone (PBQ) in an Ar matrix between 500 and 40,000 cm(-1) are presented and discussed in detail. Of particular interest is the radical cation, which shows very unusual spectroscopic features that can be understood in terms of vibronic coupling between the ground and a very low-lying excited state. The infrared spectrum of PBQ˙(+) exhibits a very conspicuous and complicated pattern of features above 1900 cm(-1) that is due to this electronic transition, and offers an unusually vivid demonstration of the effects of vibronic coupling in what would usually be a relatively simple region of the electromagnetic spectrum associated only with vibrational transitions. As expected, the intensities of most of the IR transitions leading to levels that couple the ground to the very low-lying first excited state of PBQ˙(+) increase by large factors upon ionization, due to "intensity borrowing" from the D0 → D1 electronic transition. A notable exception is the antisymmetric C=O stretching vibration, which contributes significantly to the vibronic coupling, but has nevertheless quite small intensity in the cation spectrum. This surprising feature is rationalized on the basis of a simple perturbation analysis.

Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction.

PubMed

Batalle, Dafnis; Muñoz-Moreno, Emma; Tornador, Cristian; Bargallo, Nuria; Deco, Gustavo; Eixarch, Elisenda; Gratacos, Eduard

2016-04-01

The feasibility to use functional MRI (fMRI) during natural sleep to assess low-frequency basal brain activity fluctuations in human neonates has been demonstrated, although its potential to characterise pathologies of prenatal origin has not yet been exploited. In the present study, we used intrauterine growth restriction (IUGR) as a model of altered neurodevelopment due to prenatal condition to show the suitability of brain networks to characterise functional brain organisation at neonatal age. Particularly, we analysed resting-state fMRI signal of 20 neonates with IUGR and 13 controls, obtaining whole-brain functional networks based on correlations of blood oxygen level-dependent (BOLD) signal in 90 grey matter regions of an anatomical atlas (AAL). Characterisation of the networks obtained with graph theoretical features showed increased network infrastructure and raw efficiencies but reduced efficiency after normalisation, demonstrating hyper-connected but sub-optimally organised IUGR functional brain networks. Significant association of network features with neurobehavioral scores was also found. Further assessment of spatiotemporal dynamics displayed alterations into features associated to frontal, cingulate and lingual cortices. These findings show the capacity of functional brain networks to characterise brain reorganisation from an early age, and their potential to develop biomarkers of altered neurodevelopment. Copyright © 2016 Elsevier Ltd. All rights reserved.

A structurally oriented simulation system

NASA Technical Reports Server (NTRS)

Aran, Z.

1973-01-01

The computer program SOSS (Structurally Oriented Simulation System) is designed to be used as an experimental aid in the study of reliable systems. Basically, SOSS can simulate the structure and behavior of a discrete-time, finite-state, time-invariant system at various levels of structural definition. A general description of the program is given along with its modes of operation, command language of the basic system, future features to be incorporated in SOSS, and an example of usage.

"Team Leadership Training in Vocational Special Needs." Proceedings: Annual Statewide Conference on Vocational Special Needs (3rd, College Station, Texas, April 16,17, and 18, 1979).

ERIC Educational Resources Information Center

Kok, Marilyn, Ed.; And Others

These proceedings of a statewide Texas conference focus on the need for cooperation between the leadership in the fields of special and vocational education in order to meet the needs of handicapped students. Featured presentations (1) stress the necessity for this team approach at the top levels of the education-state agency bureaucracy as well…

Feedback control of plasma instabilities with charged particle beams and study of plasma turbulence

NASA Technical Reports Server (NTRS)

Tham, Philip Kin-Wah

1994-01-01

A new non-perturbing technique for feedback control of plasma instabilities has been developed in the Columbia Linear Machine (CLM). The feedback control scheme involves the injection of a feedback modulated ion beam as a remote suppressor. The ion beam was obtained from a compact ion beam source which was developed for this purpose. A Langmuir probe was used as the feedback sensor. The feedback controller consisted of a phase-shifter and amplifiers. This technique was demonstrated by stabilizing various plasma instabilities to the background noise level, like the trapped particle instability, the ExB instability and the ion-temperature-gradient (ITG) driven instability. An important feature of this scheme is that the injected ion beam is non-perturbing to the plasma equilibrium parameters. The robustness of this feedback stabilization scheme was also investigated. The principal result is that the scheme is fairly robust, tolerating about 100% variation about the nominal parameter values. Next, this scheme is extended to the unsolved general problem of controlling multimode plasma instabilities simultaneously with a single sensor-suppressor pair. A single sensor-suppressor pair of feedback probes is desirable to reduce the perturbation caused by the probes. Two plasma instabilities the ExB and the ITG modes, were simultaneously stabilized. A simple 'state' feedback type method was used where more state information was generated from the single sensor Langmuir probe by appropriate signal processing, in this case, by differentiation. This proof-of-principle experiment demonstrated for the first time that by designing a more sophisticated electronic feedback controller, many plasma instabilities may be simultaneously controlled. Simple theoretical models showed generally good agreement with the feedback experimental results. On a parallel research front, a better understanding of the saturated state of a plasma instability was sought partly with the help of feedback. A plasma instability is usually observed in its saturated state and appears as a single feature in the frequency spectrum with a single azimuthal and parallel wavenumbers. The physics of the non-zero spectral width was investigated in detail because the finite spectral width can cause "turbulent" transport. One aspect of the "turbulence" was investigated by obtaining the scaling of the linear growth rate of the instabilities with the fluctuation levels. The linear growth rates were measured with the established gated feedback technique. The research showed that the ExB instability evolves into a quasi-coherent state when the fluctuation level is high. The coherent aspects were studied with a bispectral analysis. Moreover, the single spectral feature was discovered to be actually composed of a few radial harmonics. The radial harmonics play a role in the nonlinear saturation of the instability via three-wave coupling.

Exclusion of overlapping symptoms in DSM-5 mixed features specifier: heuristic diagnostic and treatment implications.

PubMed

Malhi, Gin S; Byrow, Yulisha; Outhred, Tim; Fritz, Kristina

2017-04-01

This article focuses on the controversial decision to exclude the overlapping symptoms of distractibility, irritability, and psychomotor agitation (DIP) with the introduction of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) mixed features specifier. In order to understand the placement of mixed states within the current classification system, we first review the evolution of mixed states. Then, using Kraepelin's original classification of mixed states, we compare and contrast his conceptualization with modern day definitions. The DSM-5 workgroup excluded DIP symptoms, arguing that they lack the ability to differentiate between manic and depressive states; however, accumulating evidence suggests that DIP symptoms may be core features of mixed states. We suggest a return to a Kraepelinian approach to classification-with mood, ideation, and activity as key axes-and reintegration of DIP symptoms as features that are expressed across presentations. An inclusive definition of mixed states is urgently needed to resolve confusion in clinical practice and to redirect future research efforts.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

PubMed

Steimer, Andreas; Schindler, Kaspar

2015-01-01

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon's implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike's preceding ISI. As we show, the EIF's exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron's ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.

Identification of subgroups of patients with tension type headache with higher widespread pressure pain hyperalgesia.

PubMed

Fernández-de-Las-Peñas, César; Benito-González, Elena; Palacios-Ceña, María; Wang, Kelun; Castaldo, Matteo; Arendt-Nielsen, Lars

2017-12-01

Identification of subgroups of patients with different levels of sensitization and clinical features can help to identify groups at risk and the development of better therapeutic strategies. The aim of this study was to identify subgroups of patients with tension type headache (TTH) with different levels of sensitization, clinical pain features, and psychological outcomes. A total of 197 individuals with TTH participated. Headache intensity, frequency, and duration and medication intake were collected with a 4-weeks diary. Pressure pain thresholds were assessed bilaterally over the temporalis muscle, C5-C6 joint, second metacarpal and tibialis anterior muscle to determine widespread pressure pain hyperalgesia. The Hospital Anxiety and Depression Scale assessed anxiety and depression. The State-Trait Anxiety Inventory evaluated the state and trait levels of anxiety. The Headache Disability Inventory evaluated the burden of headache. Health-related quality of life was determined with the SF-36 questionnaire. Groups were considered as positive (three or more criteria) or negative (less than three criteria) on a clinical prediction rule: headache duration <8.5 h/day; headache frequency <5.5 days/week; bodily pain <47 and vitality <47.5. The ANCOVA revealed that subjects in group 1 (positive rule, n = 89) exhibited longer headache history, shorter headache duration, lower headache frequency, higher widespread pressure hyperalgesia, higher anxiety trait levels, and lower quality of life (all, P < 0.01) than those subjects within group 2 (negative rule, n = 108). Differences were similar between men and women. This study identified a subgroup of patients with TTH with higher sensitization, higher chronicity of headaches and worse quality of life but lower frequency and duration of headache episodes. This subgroup of individuals with TTH may need particular attention and specific therapeutic programs for avoiding potential chronification.

Metalinguistic Knowledge of Salient vs. Unsalient Features: Evidence from the Arabic Construct State

ERIC Educational Resources Information Center

Azaz, Mahmoud

2017-01-01

This study examined to what extent English-speaking learners of Arabic demonstrated varied metalinguistic knowledge of a salient feature (head-direction) vs. an unsalient feature (definiteness) in the Arabic construct state. In addition, it examined whether this knowledge was utilized in form-focused task performance. In the target construction,…

101 things to do to reduce energy in every home and business. [Advertising supplement to Newsday

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

Not Available

This advertising supplement to Newsday features the following articles: Three Levels of Conservation; Nassau and Energy; Suffolk and Conservation; Energy Conservation and Land Use Planning; The Energy Efficient Community Program; NYSERDA Promotes Energy Efficiency and Conservation; The New York State Energy Conservation Plan; Architecture and Energy Conservation; The Engineer's Role in Energy Conservation; Energy Management Programs; a Model Energy-Efficient Home; and Choosing a Contractor. A feature also is Homeowners Energy Check List: 101 Ways to Save Money by Saving Energy. This checklist is included separately with the news supplement also. Many advertisements provide information on where to obtain energy conservationmore » equipment, specifically, solar energy systems equipment. (MCW)« less

Observation of Phase-Filling Singularities in the Optical Dielectric Function of Highly Doped n-Type Ge.

PubMed

Xu, Chi; Fernando, Nalin S; Zollner, Stefan; Kouvetakis, John; Menéndez, José

2017-06-30

Phase-filling singularities in the optical response function of highly doped (>10^{19}  cm^{-3}) germanium are theoretically predicted and experimentally confirmed using spectroscopic ellipsometry. Contrary to direct-gap semiconductors, which display the well-known Burstein-Moss phenomenology upon doping, the critical point in the joint density of electronic states associated with the partially filled conduction band in n-Ge corresponds to the so-called E_{1} and E_{1}+Δ_{1} transitions, which are two-dimensional in character. As a result of this reduced dimensionality, there is no edge shift induced by Pauli blocking. Instead, one observes the "original" critical point (shifted only by band gap renormalization) and an additional feature associated with the level occupation discontinuity at the Fermi level. The experimental observation of this feature is made possible by the recent development of low-temperature, in situ doping techniques that allow the fabrication of highly doped films with exceptionally flat doping profiles.

Influence of magnetism and correlation on the spectral properties of doped Mott insulators

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

Wang, Yao; Moritz, Brian; Chen, Cheng-Chien

Unraveling the nature of the doping-induced transition between a Mott insulator and a weakly correlated metal is crucial to understanding novel emergent phases in strongly correlated materials. Here, for this purpose, we study the evolution of spectral properties upon doping Mott insulating states by utilizing the cluster perturbation theory on the Hubbard and t – J -like models. Specifically, a quasifree dispersion crossing the Fermi level develops with small doping, and it eventually evolves into the most dominant feature at high doping levels. Although this dispersion is related to the free-electron hopping, our study shows that this spectral feature is,more » in fact, influenced inherently by both electron-electron correlation and spin-exchange interaction: the correlation destroys coherence, while the coupling between spin and mobile charge restores it in the photoemission spectrum. Due to the persistent impact of correlations and spin physics, the onset of gaps or the high-energy anomaly in the spectral functions can be expected in doped Mott insulators.« less

Influence of magnetism and correlation on the spectral properties of doped Mott insulators

DOE PAGES

Wang, Yao; Moritz, Brian; Chen, Cheng-Chien; ...

2018-03-01

Unraveling the nature of the doping-induced transition between a Mott insulator and a weakly correlated metal is crucial to understanding novel emergent phases in strongly correlated materials. Here, for this purpose, we study the evolution of spectral properties upon doping Mott insulating states by utilizing the cluster perturbation theory on the Hubbard and t – J -like models. Specifically, a quasifree dispersion crossing the Fermi level develops with small doping, and it eventually evolves into the most dominant feature at high doping levels. Although this dispersion is related to the free-electron hopping, our study shows that this spectral feature is,more » in fact, influenced inherently by both electron-electron correlation and spin-exchange interaction: the correlation destroys coherence, while the coupling between spin and mobile charge restores it in the photoemission spectrum. Due to the persistent impact of correlations and spin physics, the onset of gaps or the high-energy anomaly in the spectral functions can be expected in doped Mott insulators.« less

The NaK 1 1,3delta states: theoretical and experimental studies of fine and hyperfine structure of rovibrational levels near the dissociation limit.

PubMed

Wilkins, A D; Morgus, L; Hernandez-Guzman, J; Huennekens, J; Hickman, A P

2005-09-22

Earlier high-resolution spectroscopic studies of the fine and hyperfine structure of rovibrational levels of the 1 3delta state of NaK have been extended to include high lying rovibrational levels with v < or = 59, of which the highest levels lie within approximately 4 cm(-1) of the dissociation limit. A potential curve is determined using the inverted perturbation approximation method that reproduces these levels to an accuracy of approximately 0.026 cm(-1). For the largest values of v, the outer turning points occur near R approximately 12.7 angstroms, which is sufficiently large to permit the estimation of the C6 coefficient for this state. The fine and hyperfine structure of the 1 3delta rovibrational levels has been fit using the matrix diagonalization method that has been applied to other states of NaK, leading to values of the spin-orbit coupling constant A(v) and the Fermi contact constant b(F). New values determined for v < or = 33 are consistent with values determined by a simpler method and reported earlier. The measured fine and hyperfine structure for v in the range 44 < or = v < or = 49 exhibits anomalous behavior whose origin is believed to be the mixing between the 1 3delta and 1 1delta states. The matrix diagonalization method has been extended to treat this interaction, and the results provide an accurate representation of the complicated patterns that arise. The analysis leads to accurate values for A(v) and b(F) for all values of v < or = 49. For higher v (50 < or = v < or = 59), several rovibrational levels have been assigned, but the pattern of fine and hyperfine structure is difficult to interpret. Some of the observed features may arise from effects not included in the current model.

Using different classification models in wheat grading utilizing visual features

NASA Astrophysics Data System (ADS)

Basati, Zahra; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-04-01

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively

Physical Activity Level of Ambulatory Stroke Patients: Is it Related to Neuropsychological Factors?

PubMed

Ersöz Hüseyinsinoğlu, Burcu; Kuran Aslan, Gökşen; Tarakci, Devrim; Razak Özdinçler, Arzu; Küçükoğlu, Hayriye; Baybaş, Sevim

2017-06-01

Physical inactivity is an important risk factor for stroke and stroke recurrence. There is insufficient knowledge about the physical activity (PA) level in stroke patients who are ambulatory in the subacute phase. Our aim was to compare the PA level between ambulatory stroke patients and a population of the same age and to investigate neuropsychological factors that could affect the PA level in the same stroke group. Eighty-five subacute stroke patients and 58 healthy subjects were included. Patients' demographic features, disease-related features, and comorbidities were recorded. The PA level was assessed by the International Physical Activity Questionnaire-Short Version and a pedometer (OMRON Walking style II). The Apathy Rating Scale was applied to determine the apathy level. Depression level was investigated by the Geriatric Depression Scale. The standardized Mini-mental State Examination was performed to assess the cognitive status. The PA level was significantly higher in the healthy group than in the stroke group (p<0.001). Step count and walking distance were significantly higher in healthy group (p=0.001 and p=0.04, respectively). The PA level of men was significantly higher than that of women (p=0.03). Participants who were classified as level 4 had a lower PA level than those who were classified as level 5 according to the Functional Ambulation Category. There was no relationship between the PA level and the apathy, cognitive, and depression levels in the stroke patients (p>0.05). Subacute stroke patients have a lower PA level than healthy subjects. This is not related to neuropsychological factors. The reasons for minor deficits related to ambulation should be researched further while developing strategies for increasing the PA level of subacute stroke patients.

Excited states of aniline by photoabsorption spectroscopy in the 30,000-90,000 cm(-1) region using synchrotron radiation.

PubMed

Rajasekhar, B N; Veeraiah, A; Sunanda, K; Jagatap, B N

2013-08-14

The photoabsorption spectrum of aniline (C6H5NH2) in gas phase in the 30,000-90,000 cm(-1) (3.7-11.2 eV) region is recorded at resolution limit of 0.008 eV using synchrotron radiation source for the first time to comprehend the nature of the excited valence and Rydberg states. The first half of the energy interval constitutes the richly structured valence transitions from the ground to excited states up to the first ionization potential (IP) at 8.02 eV. The spectrum in the second half consists of vibrational features up to second IP (9.12 eV) and structureless broad continuum up to the third IP (10.78 eV). The electronic states are assigned mainly to the singlets belonging to π → π* transitions. A few weak initial members of Rydberg states arising from π → 4s, np or nd transitions are also identified. Observed vibrational features are assigned to transitions from the ground state A' to the excited states 1A", 3A', 5A", 6A', and 10A" in C(s) symmetry. Time dependent density functional theory (TDDFT) calculations at B3LYP level of theory are employed to obtain the vertical excitation energies and the symmetries of the excited states in equilibrium configuration. The computed values of the transition energies agree fairly well with the experimental data. Further the calculated oscillator strengths are used to substantiate the assignments of the bands. The work provides a comprehensive picture of the vacuum ultraviolet photoabsorption spectrum of aniline up to its third ionization limit.

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

PubMed

Katzman, Jared L; Shaham, Uri; Cloninger, Alexander; Bates, Jonathan; Jiang, Tingting; Kluger, Yuval

2018-02-26

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

How to Link Brain and Experience? Spatiotemporal Psychopathology of the Lived Body

PubMed Central

Northoff, Georg; Stanghellini, Giovanni

2016-01-01

The focus of the present article is on sketching a psychopathology of the body in schizophrenia and linking it to brain activity. This is done providing converging data from psychopathological evidence (phenomenal), phenomenological contructs (trans-phenomenal) and neuroscientific measures (pre-phenomenal). The phenomenal level is the detailed documentation of the patients’ subjective anomalous experiences. These phenomena are explicit contents in the patients’ field of consciousness. The trans-phenomenal level targets the implicit yet operative matrix that underlies these anomalous subjective experiences. Abnormal phenomena are viewed as expressions of a modification of trans-phenomenal matrix, that is, in terms of an abnormal synthesis or integration through time of intero-, proprio- and extero-ceptive stimuli. Finally, we link the abnormalities of the trans-phenomenal matrix to pre-phenomenal alterations of the brain resting state and of its spatio-temporal organization, as documented by neurobiological methods providing spatial and temporal resolution of intrinsic brain activity (with many features of the resting state remaining yet unclear though). Based on phenomenological research, the body in schizophrenia is typically experienced in an itemized way as an object external to one’s self and unrelated to events in the external world. Based on neurobiological data, we tentatively hypothesize that such anomalies of the lived body are related to decreased integration between intero-, extero- and proprioceptive experiences by the brain’s spontaneous activity and its temporal structure. Taken all together, this suggests that we view abnormalities of bodily experience in terms of their underlying abnormal spatiotemporal features which, as we suppose, can be traced back to the spatiotemporal features of the brain’s spontaneous activity. PMID:27199695

Density functional theory for prediction of far-infrared vibrational frequencies: molecular crystals of astrophysical interest

NASA Astrophysics Data System (ADS)

Ennis, C.; Auchettl, R.; Appadoo, D. R. T.; Robertson, E. G.

2017-11-01

Solid-state density functional theory code has been implemented for the structure optimization of crystalline methanol, acetaldehyde and acetic acid and for the calculation of infrared frequencies. The results are compared to thin film spectra obtained from low-temperature experiments performed at the Australian Synchrotron. Harmonic frequency calculations of the internal modes calculated at the B3LYP-D3/m-6-311G(d) level shows higher deviation from infrared experiment than more advanced theory applied to the gas phase. Importantly for the solid-state, the simulation of low-frequency molecular lattice modes closely resembles the observed far-infrared features after application of a 0.92 scaling factor. This allowed experimental peaks to be assigned to specific translation and libration modes, including acetaldehyde and acetic acid lattice features for the first time. These frequency calculations have been performed without the need for supercomputing resources that are required for large molecular clusters using comparable levels of theory. This new theoretical approach will find use for the rapid characterization of intermolecular interactions and bonding in crystals, and the assignment of far-infrared spectra for crystalline samples such as pharmaceuticals and molecular ices. One interesting application may be for the detection of species of prebiotic interest on the surfaces of Kuiper-Belt and Trans-Neptunian Objects. At such locations, the three small organic molecules studied here could reside in their crystalline phase. The far-infrared spectra for their low-temperature solid phases are collected under planetary conditions, allowing us to compile and assign their most intense spectral features to assist future far-infrared surveys of icy Solar system surfaces.

Evaluation of maternal attachment, self-efficacy, levels of depression, and anxiety in mothers who have babies diagnosed with retinopathy of prematurity.

PubMed

Özyurt, Gonca; Özyurt, Ayhan; Ozturk, Taylan; Yaman, Aylin; Berk, A Tulin

2018-04-01

The aim of this study is to evaluate the emotional stress and its effects on parental self-efficacy and mother-infant attachment in mothers whose babies were diagnosed with retinopathy of prematurity (ROP). Study sample was consisted of voluntarily participating 82 mothers whose babies were first diagnosed with ROP, 83 mothers of preterm babies without ROP, and 85 mothers of term babies admitting for their routine visits. Sociodemographic data form maternal attachment scale, state-trait anxiety inventory, Edinburgh postnatal depression scale, and parental self-efficacy scale were applied to study participants, and the overall results of three groups were statistically compared. The sociodemographic features of three study groups were similar. Statistical significant differences were found in depression and state anxiety levels among study groups, while maternal attachment scale and trait anxiety level scores and parental self-efficacy scale total score were similar in study groups. Maternal depression and state-anxiety levels were tend to be higher in mother of children diagnosed with ROP and prematurity; however, there were no statistically significant differences between levels of mothers' of premature children with or without ROP. This is the first study in literature assessing the additional effect of ROP on the anxiety and depression levels of recent mothers, as well as mother-infant attachment and parental self-efficacy. Supporting of mothers having an infant with diagnosed ROP is crucial because of feeling themselves inefficient and responsible for all interventions applied to their babies.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

PubMed Central

Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

2015-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. PMID:25987366

Neuron’s eye view: Inferring features of complex stimuli from neural responses

PubMed Central

Chen, Xin; Beck, Jeffrey M.

2017-01-01

Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world—contrast and luminance for vision, pitch and intensity for sound—and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov) dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set. PMID:28827790

Detecting Pilot's Engagement Using fNIRS Connectivity Features in an Automated vs. Manual Landing Scenario

PubMed Central

Verdière, Kevin J.; Roy, Raphaëlle N.; Dehais, Frédéric

2018-01-01

Monitoring pilot's mental states is a relevant approach to mitigate human error and enhance human machine interaction. A promising brain imaging technique to perform such a continuous measure of human mental state under ecological settings is Functional Near-InfraRed Spectroscopy (fNIRS). However, to our knowledge no study has yet assessed the potential of fNIRS connectivity metrics as long as passive Brain Computer Interfaces (BCI) are concerned. Therefore, we designed an experimental scenario in a realistic simulator in which 12 pilots had to perform landings under two contrasted levels of engagement (manual vs. automated). The collected data were used to benchmark the performance of classical oxygenation features (i.e., Average, Peak, Variance, Skewness, Kurtosis, Area Under the Curve, and Slope) and connectivity features (i.e., Covariance, Pearson's, and Spearman's Correlation, Spectral Coherence, and Wavelet Coherence) to discriminate these two landing conditions. Classification performance was obtained by using a shrinkage Linear Discriminant Analysis (sLDA) and a stratified cross validation using each feature alone or by combining them. Our findings disclosed that the connectivity features performed significantly better than the classical concentration metrics with a higher accuracy for the wavelet coherence (average: 65.3/59.9 %, min: 45.3/45.0, max: 80.5/74.7 computed for HbO/HbR signals respectively). A maximum classification performance was obtained by combining the area under the curve with the wavelet coherence (average: 66.9/61.6 %, min: 57.3/44.8, max: 80.0/81.3 computed for HbO/HbR signals respectively). In a general manner all connectivity measures allowed an efficient classification when computed over HbO signals. Those promising results provide methodological cues for further implementation of fNIRS-based passive BCIs. PMID:29422841

Effects of non-neuronal components for functional connectivity analysis from resting-state functional MRI toward automated diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Kim, Junghoe; Lee, Jong-Hwan

2014-03-01

A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.

Low-level awareness accompanies "unconscious" high-level processing during continuous flash suppression.

PubMed

Gelbard-Sagiv, Hagar; Faivre, Nathan; Mudrik, Liad; Koch, Christof

2016-01-01

The scope and limits of unconscious processing are a matter of ongoing debate. Lately, continuous flash suppression (CFS), a technique for suppressing visual stimuli, has been widely used to demonstrate surprisingly high-level processing of invisible stimuli. Yet, recent studies showed that CFS might actually allow low-level features of the stimulus to escape suppression and be consciously perceived. The influence of such low-level awareness on high-level processing might easily go unnoticed, as studies usually only probe the visibility of the feature of interest, and not that of lower-level features. For instance, face identity is held to be processed unconsciously since subjects who fail to judge the identity of suppressed faces still show identity priming effects. Here we challenge these results, showing that such high-level priming effects are indeed induced by faces whose identity is invisible, but critically, only when a lower-level feature, such as color or location, is visible. No evidence for identity processing was found when subjects had no conscious access to any feature of the suppressed face. These results suggest that high-level processing of an image might be enabled by-or co-occur with-conscious access to some of its low-level features, even when these features are not relevant to the processed dimension. Accordingly, they call for further investigation of lower-level awareness during CFS, and reevaluation of other unconscious high-level processing findings.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

Rethinking the REAL ID Act and National Identification Cards as a Counterterrorism Tool

DTIC Science & Technology

2009-12-01

federal government imposing national identification standards on states are also actively engaged in the debate. Michael Boldin , a 36-year-old Web...on the RIA.94 Boldin states, “Maine resisted, the government backed off, and soon all of these other states were doing the same thing.”95 Since...that acquires biometric data from an individual, extracts a feature set from the data, compares this feature set against the feature set stored in a

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Monte Carlo calculations of diatomic molecule gas flows including rotational mode excitation

NASA Technical Reports Server (NTRS)

Yoshikawa, K. K.; Itikawa, Y.

1976-01-01

The direct simulation Monte Carlo method was used to solve the Boltzmann equation for flows of an internally excited nonequilibrium gas, namely, of rotationally excited homonuclear diatomic nitrogen. The semi-classical transition probability model of Itikawa was investigated for its ability to simulate flow fields far from equilibrium. The behavior of diatomic nitrogen was examined for several different nonequilibrium initial states that are subjected to uniform mean flow without boundary interactions. A sample of 1000 model molecules was observed as the gas relaxed to a steady state starting from three specified initial states. The initial states considered are: (1) complete equilibrium, (2) nonequilibrium, equipartition (all rotational energy states are assigned the mean energy level obtained at equilibrium with a Boltzmann distribution at the translational temperature), and (3) nonequipartition (the mean rotational energy is different from the equilibrium mean value with respect to the translational energy states). In all cases investigated the present model satisfactorily simulated the principal features of the relaxation effects in nonequilibrium flow of diatomic molecules.

Smoothed quantum-classical states in time-irreversible hybrid dynamics

NASA Astrophysics Data System (ADS)

Budini, Adrián A.

2017-09-01

We consider a quantum system continuously monitored in time which in turn is coupled to an arbitrary dissipative classical system (diagonal reduced density matrix). The quantum and classical dynamics can modify each other, being described by an arbitrary time-irreversible hybrid Lindblad equation. Given a measurement trajectory, a conditional bipartite stochastic state can be inferred by taking into account all previous recording information (filtering). Here, we demonstrate that the joint quantum-classical state can also be inferred by taking into account both past and future measurement results (smoothing). The smoothed hybrid state is estimated without involving information from unobserved measurement channels. Its average over recording realizations recovers the joint time-irreversible behavior. As an application we consider a fluorescent system monitored by an inefficient photon detector. This feature is taken into account through a fictitious classical two-level system. The average purity of the smoothed quantum state increases over that of the (mixed) state obtained from the standard quantum jump approach.

Single cell analysis in native tissue: Quantification of the retinoid content of hepatic stellate cells

NASA Astrophysics Data System (ADS)

Galler, Kerstin; Requardt, Robert Pascal; Glaser, Uwe; Markwart, Robby; Bocklitz, Thomas; Bauer, Michael; Popp, Jürgen; Neugebauer, Ute

2016-04-01

Hepatic stellate cells (HSCs) are retinoid storing cells in the liver: The retinoid content of those cells changes depending on nutrition and stress level. There are also differences with regard to a HSC’s anatomical position in the liver. Up to now, retinoid levels were only accessible from bulk measurements of tissue homogenates or cell extracts. Unfortunately, they do not account for the intercellular variability. Herein, Raman spectroscopy relying on excitation by the minimally destructive wavelength 785 nm is introduced for the assessment of the retinoid state of single HSCs in freshly isolated, unprocessed murine liver lobes. A quantitative estimation of the cellular retinoid content is derived. Implications of the retinoid content on hepatic health state are reported. The Raman-based results are integrated with histological assessments of the tissue samples. This spectroscopic approach enables single cell analysis regarding an important cellular feature in unharmed tissue.

Single cell analysis in native tissue: Quantification of the retinoid content of hepatic stellate cells

PubMed Central

Galler, Kerstin; Requardt, Robert Pascal; Glaser, Uwe; Markwart, Robby; Bocklitz, Thomas; Bauer, Michael; Popp, Jürgen; Neugebauer, Ute

2016-01-01

Hepatic stellate cells (HSCs) are retinoid storing cells in the liver: The retinoid content of those cells changes depending on nutrition and stress level. There are also differences with regard to a HSC’s anatomical position in the liver. Up to now, retinoid levels were only accessible from bulk measurements of tissue homogenates or cell extracts. Unfortunately, they do not account for the intercellular variability. Herein, Raman spectroscopy relying on excitation by the minimally destructive wavelength 785 nm is introduced for the assessment of the retinoid state of single HSCs in freshly isolated, unprocessed murine liver lobes. A quantitative estimation of the cellular retinoid content is derived. Implications of the retinoid content on hepatic health state are reported. The Raman-based results are integrated with histological assessments of the tissue samples. This spectroscopic approach enables single cell analysis regarding an important cellular feature in unharmed tissue. PMID:27063397

Outreach at Washington State University: a case study in costs and attendance

NASA Astrophysics Data System (ADS)

Bernhardt, Elizabeth A.; Bollen, Viktor; Bersano, Thomas M.; Mossman, Sean M.

2016-09-01

Making effective and efficient use of outreach resources can be difficult for student groups in smaller rural communities. Washington State University's OSA/SPIE student chapter desires well attended yet cost-effective ways to educate and inform the public. We designed outreach activities focused on three different funding levels: low upfront cost, moderate continuing costs, and high upfront cost with low continuing costs. By featuring our activities at well attended events, such as a pre-football game event, or by advertising a headlining activity, such as a laser maze, we take advantage of large crowds to create a relaxed learning atmosphere. Moreover, participants enjoy casual learning while waiting for a main event. Choosing a particular funding level and associating with well-attended events makes outreach easier. While there are still many challenges to outreach, such as motivating volunteers or designing outreach programs, we hope overcoming two large obstacles will lead to future outreach success.

A glimpse behind the mask: facets of narcissism and feelings of self-worth.

PubMed

Zeigler-Hill, Virgil; Besser, Avi

2013-01-01

This study examined the connections that the facets of narcissism captured by the Narcissistic Personality Inventory (NPI; Raskin & Hall, 1979) and the Pathological Narcissism Inventory (PNI; Pincus et al., 2009) have with self-esteem. This was accomplished by asking 372 participants to complete measures of narcissism and self-esteem level as well as daily diary measures concerning their state self-esteem and daily experiences. Our analyses found that the facets of narcissism differed in their associations with average level of self-esteem, fluctuations in state self-esteem over time, and self-esteem reactions following daily events. These results suggest that it is important to consider specific facets of narcissism when examining feelings of self-worth rather than relying on broader composite measures of narcissistic personality features. Implications of these results for our understanding of the dynamics of the narcissistic personality are discussed.

Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

NASA Astrophysics Data System (ADS)

Yang, G.; Lin, Y.; Bhattacharya, P.

2007-12-01

To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i) casual or contextual feature, (ii) contact feature, (iii) contactless feature, and (iv) performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK) model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA), is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue). We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches.

PubMed

Chen, Xi; Kopsaftopoulos, Fotis; Wu, Qi; Ren, He; Chang, Fu-Kuo

2018-04-29

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles.

An integrated condition-monitoring method for a milling process using reduced decomposition features

NASA Astrophysics Data System (ADS)

Liu, Jie; Wu, Bo; Wang, Yan; Hu, Youmin

2017-08-01

Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification.

Coupled binary embedding for large-scale image retrieval.

PubMed

Zheng, Liang; Wang, Shengjin; Tian, Qi

2014-08-01

Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.

27 CFR 9.3 - Relation to parts 4 and 70 of this chapter.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) Evidence relating to the geographical features (climate, soil, elevation, physical features, etc.) which... of the viticultural area, based on features which can be found on United States Geological Survey (U... index by State.) [T.D. ATF-60, 44 FR 56692, Oct. 2, 1979, as amended by T.D. ATF-92, 46 FR 46913, Sept...

A comprehensive sensitivity analysis of microarray breast cancer classification under feature variability

PubMed Central

2009-01-01

Background Large discrepancies in signature composition and outcome concordance have been observed between different microarray breast cancer expression profiling studies. This is often ascribed to differences in array platform as well as biological variability. We conjecture that other reasons for the observed discrepancies are the measurement error associated with each feature and the choice of preprocessing method. Microarray data are known to be subject to technical variation and the confidence intervals around individual point estimates of expression levels can be wide. Furthermore, the estimated expression values also vary depending on the selected preprocessing scheme. In microarray breast cancer classification studies, however, these two forms of feature variability are almost always ignored and hence their exact role is unclear. Results We have performed a comprehensive sensitivity analysis of microarray breast cancer classification under the two types of feature variability mentioned above. We used data from six state of the art preprocessing methods, using a compendium consisting of eight diferent datasets, involving 1131 hybridizations, containing data from both one and two-color array technology. For a wide range of classifiers, we performed a joint study on performance, concordance and stability. In the stability analysis we explicitly tested classifiers for their noise tolerance by using perturbed expression profiles that are based on uncertainty information directly related to the preprocessing methods. Our results indicate that signature composition is strongly influenced by feature variability, even if the array platform and the stratification of patient samples are identical. In addition, we show that there is often a high level of discordance between individual class assignments for signatures constructed on data coming from different preprocessing schemes, even if the actual signature composition is identical. Conclusion Feature variability can have a strong impact on breast cancer signature composition, as well as the classification of individual patient samples. We therefore strongly recommend that feature variability is considered in analyzing data from microarray breast cancer expression profiling experiments. PMID:19941644

An Early Look At SHOP Marketplaces: Low Premiums, Adequate Plan Choice In Many, But Not All, States.

PubMed

Gabel, Jon R; Stromberg, Sam T; Green, Matthew; Lischko, Amy; Whitmore, Heidi

2015-05-01

The Affordable Care Act created the Small Business Health Options Program (SHOP) Marketplaces to help small businesses provide health insurance to their employees. To attract the participation of substantial numbers of small employers, SHOP Marketplaces must demonstrate value-added features unavailable in the traditional small-group market. Such features could include lower premiums than those for plans offered outside the Marketplace and more extensive choices of carriers and plans. More choices are necessary for SHOP Marketplaces to offer the "employee choice model," in which employees may choose from many carriers and plans. This study compared the numbers of carriers and plans and premium levels in 2014 for plans offered through SHOP Marketplaces with those of plans offered only outside of the Marketplaces. An average of 4.3 carriers participated in each state's Marketplace, offering a total of forty-seven plans. Premiums for plans offered through SHOP Marketplaces were, on average, 7 percent less than those in the same metal tier offered only outside of the Marketplaces. Lower premiums and the participation of multiple carriers in most states are a source of optimism for future enrollment growth in SHOP Marketplaces. Lack of broker buy-in in many states and burdensome enrollment processes are major impediments to success. Project HOPE—The People-to-People Health Foundation, Inc.

Terrain-driven unstructured mesh development through semi-automatic vertical feature extraction

NASA Astrophysics Data System (ADS)

Bilskie, Matthew V.; Coggin, David; Hagen, Scott C.; Medeiros, Stephen C.

2015-12-01

A semi-automated vertical feature terrain extraction algorithm is described and applied to a two-dimensional, depth-integrated, shallow water equation inundation model. The extracted features describe what are commonly sub-mesh scale elevation details (ridge and valleys), which may be ignored in standard practice because adequate mesh resolution cannot be afforded. The extraction algorithm is semi-automated, requires minimal human intervention, and is reproducible. A lidar-derived digital elevation model (DEM) of coastal Mississippi and Alabama serves as the source data for the vertical feature extraction. Unstructured mesh nodes and element edges are aligned to the vertical features and an interpolation algorithm aimed at minimizing topographic elevation error assigns elevations to mesh nodes via the DEM. The end result is a mesh that accurately represents the bare earth surface as derived from lidar with element resolution in the floodplain ranging from 15 m to 200 m. To examine the influence of the inclusion of vertical features on overland flooding, two additional meshes were developed, one without crest elevations of the features and another with vertical features withheld. All three meshes were incorporated into a SWAN+ADCIRC model simulation of Hurricane Katrina. Each of the three models resulted in similar validation statistics when compared to observed time-series water levels at gages and post-storm collected high water marks. Simulated water level peaks yielded an R2 of 0.97 and upper and lower 95% confidence interval of ∼ ± 0.60 m. From the validation at the gages and HWM locations, it was not clear which of the three model experiments performed best in terms of accuracy. Examination of inundation extent among the three model results were compared to debris lines derived from NOAA post-event aerial imagery, and the mesh including vertical features showed higher accuracy. The comparison of model results to debris lines demonstrates that additional validation techniques are necessary for state-of-the-art flood inundation models. In addition, the semi-automated, unstructured mesh generation process presented herein increases the overall accuracy of simulated storm surge across the floodplain without reliance on hand digitization or sacrificing computational cost.

High-level intuitive features (HLIFs) for intuitive skin lesion description.

PubMed

Amelard, Robert; Glaister, Jeffrey; Wong, Alexander; Clausi, David A

2015-03-01

A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.

Solid-state lighting life prediction using extended Kalman filter

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

Lall, Pradeep; Wei, Junchao; Davis, Lynn

2013-07-16

Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. The U.S. Department of Energy has made a long term commitment to advance the efficiency, understandingmore » and development of solid-state lighting (SSL) and is making a strong push for the acceptance and use of SSL products to reduce overall energy consumption attributable to lighting. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of SSL Luminaires from LM-80 test data. The TM-21 model uses an Arrhenius Equation with an Activation Energy, Pre-decay factor and Decay Rates. Several failure mechanisms may be active in a luminaire at a single time causing lumen depreciation. The underlying TM-21 Arrhenius Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, a Kalman Filter and Extended Kalman Filters have been used to develop a 70% Lumen Maintenance Life Prediction Model for a LEDs used in SSL luminaires. This model can be used to calculate acceleration factors, evaluate failure-probability and identify ALT methodologies for reducing test time. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state has been described in state space form using the measurement of the feature vector, velocity of feature vector change and the acceleration of the feature vector change. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying damage using physics-based models. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have been compared with the TM-21 model predictions and experimental data.« less

Ecosystems and the Biosphere as Complex Adaptive Systems

NASA Technical Reports Server (NTRS)

Levin, Simon A.

1998-01-01

Ecosystems are prototypical examples of complex adaptive systems, in which patterns at higher levels emerge from localized interactions and selection processes acting at lower levels. An essential aspect of such systems is nonlinearity, leading to historical dependency and multiple possible outcomes of dynamics. Given this, it is essential to determine the degree to which system features are determined by environmental conditions, and the degree to which they are the result of self-organization. Furthermore, given the multiple levels at which dynamics become apparent and at which selection can act, central issues relate to how evolution shapes ecosystems properties, and whether ecosystems become buffered to changes (more resilient) over their ecological and evolutionary development or proceed to critical states and the edge of chaos.

Spin-related origin of the magnetotransport feature at filling factor 7/11

NASA Astrophysics Data System (ADS)

Gamez, Gerardo; Muraki, Koji

2010-03-01

Experiments by Pan et al. disclosed quantum Hall (QH) effect-like features at unconventional filling fractions, such as 4/11 and 7/11, not included in the Jain sequence [1]. These features were considered as evidence for a new class of fractional quantum Hall (FQH) states whose origin, unlike ordinary FQH states, is linked to interactions between composite fermions (CFs). However, the exact origin of these features is not well established yet. Here we focus on 7/11, where a minimum in the longitudinal resistance and a plateau-like structure in the Hall resistance are observed at a much higher field, 11.4 T, in a 30-nm quantum well (QW). Our density-dependent studies show that at this field, the FQH states flanking 7/11, viz. the 2/3 and 3/5 states, are both fully spin polarized. Despite of this fact, tilted-field experiments reveal that the 7/11 feature weakens and then disappears upon tilting. Using a CF model, we show that the spin degree of freedom may not be completely frozen in the region between the 2/3 and 3/5 states even when both states are fully polarized. Systematic studies unveil that the exact location of the 7/11 feature depends on the electron density and the QW width, in accordance with the model. Our model can also account for the reported contrasting behavior upon tilting of 7/11 and its electron-hole counterpart 4/11. [1] Pan et al., Phys. Rev. Lett. 90, 016801 (2003).

Seeds of Their Struggle: The Features of Under- and Overage Enrolment among Grade 4 Learners in South Africa. CREATE Pathways to Access. Research Monograph No. 47

ERIC Educational Resources Information Center

Taylor, Nick; Mabogoane, Thabo; Shindler, Jennifer; Akoobhai, Bina

2010-01-01

Using data drawn from the National School Effectiveness Study, this paper reveals that by Grade 4 35% of South African learners are overage. The study confirms what every other testing programme tells us about the poor state of learning which characterizes the system: the seeds of low pass rates at Grade 12 level, and of low participation and…

Composition and formation of heterochromatin in Arabidopsis thaliana.

PubMed

Fransz, P; ten Hoopen, R; Tessadori, F

2006-01-01

The term heterochromatin has been applied to both large-scale, microscopically visible chromocentres and small-scale, silent genes located outside chromocentres. This may cause confusion in the interpretation of epigenetic marks for both features. The model plant Arabidopsis thaliana provides an excellent system to investigate composition and function of chromatin states at different levels of organization. In this review we will discuss recent developments in molecular networks underlying gene silencing and the relationship with visible heterochromatin in Arabidopsis.

Exploring the Temperature Dependent Solid-State ALC Spectrum of the C6H6Mu• Radical with Ab-Initio Simulation Techniques

NASA Astrophysics Data System (ADS)

Sturniolo, S.; Liborio, L.; Pratt, F. L.; Cottrell, S. P.; Jochym, D. B.; Montanari, B.

The longitudinal field Avoided Level Crossing (ALC) muon spectrum of crystalline benzene is found to exhibit multiple complex features that strongly depend on temperature. In this work, a number of different computational techniques were used in conjunction to explain this behavior. An overall picture emerges in which the spectrum appears to be the result of an interplay of multiple dynamical effects, both classical and quantum mechanical.

Software engineering project management - A state-of-the-art report

NASA Technical Reports Server (NTRS)

Thayer, R. H.; Lehman, J. H.

1977-01-01

The management of software engineering projects in the aerospace industry was investigated. The survey assessed such features as contract type, specification preparation techniques, software documentation required by customers, planning and cost-estimating, quality control, the use of advanced program practices, software tools and test procedures, the education levels of project managers, programmers and analysts, work assignment, automatic software monitoring capabilities, design and coding reviews, production times, success rates, and organizational structure of the projects.

Direct observation of interlocked domain walls and topological four-state vortex-like domain patterns in multiferroic YMnO{sub 3} single crystal

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

Tian, Lei; School of Materials Science and Engineering, Dalian Jiaotong University, Dalian, Liaoning 116028; Wang, Yumei, E-mail: wangym@iphy.ac.cn

2015-03-16

Using the advanced spherical aberration-corrected high angle annular dark field scanning transmission electron microscope imaging techniques, we investigated atomic-scale structural features of domain walls and domain patterns in YMnO{sub 3} single crystal. Three different types of interlocked ferroelectric-antiphase domain walls and two abnormal topological four-state vortex-like domain patterns are identified. Each ferroelectric domain wall is accompanied by a translation vector, i.e., 1/6[210] or −1/6[210], demonstrating its interlocked nature. Different from the four-state vortex domain patterns caused by a partial edge dislocation, two four-state vortex-like domain configurations have been obtained at atomic level. These observed phenomena can further extend our understandingmore » of the fascinating vortex domain patterns in multiferroic hexagonal rare-earth manganites.« less

Multiscale pore structure and its effect on gas transport in organic-rich shale

NASA Astrophysics Data System (ADS)

Wu, Tianhao; Li, Xiang; Zhao, Junliang; Zhang, Dongxiao

2017-07-01

A systematic investigation of multiscale pore structure in organic-rich shale by means of the combination of various imaging techniques is presented, including the state-of-the-art Helium-Ion-Microscope (HIM). The study achieves insight into the major features at each scale and suggests the affordable techniques for specific objectives from the aspects of resolution, dimension, and cost. The pores, which appear to be isolated, are connected by smaller pores resolved by higher-resolution imaging. This observation provides valuable information, from the microscopic perspective of pore structure, for understanding how gas accumulates and transports from where it is generated. A comprehensive workflow is proposed based on the characteristics acquired from the multiscale pore structure analysis to simulate the gas transport process. The simulations are completed with three levels: the microscopic mechanisms should be taken into consideration at level I; the spatial distribution features of organic matter, inorganic matter, and macropores constitute the major issue at level II; and the microfracture orientation and topological structure are dominant factors at level III. The results of apparent permeability from simulations agree well with the values acquired from experiments. By means of the workflow, the impact of various gas transport mechanisms at different scales can be investigated more individually and precisely than conventional experiments.

Transgenic Mice Overexpressing Amyloid Precursor Protein Exhibit Early Metabolic Deficits and a Pathologically Low Leptin State Associated with Hypothalamic Dysfunction in Arcuate Neuropeptide Y Neurons

PubMed Central

Ishii, Makoto; Wang, Gang; Racchumi, Gianfranco; Dyke, Jonathan P.

2014-01-01

Weight loss is a prominent early feature of Alzheimer's disease (AD) that often precedes the cognitive decline and clinical diagnosis. While the exact pathogenesis of AD remains unclear, accumulation of amyloid-β (Aβ) derived from the amyloid precursor protein (APP) in the brain is thought to lead to the neuronal dysfunction and death underlying the dementia. In this study, we examined whether transgenic mice overexpressing the Swedish mutation of APP (Tg2576), recapitulating selected features of AD, have hypothalamic leptin signaling dysfunction leading to early body weight deficits. We found that 3-month-old Tg2576 mice, before amyloid plaque formation, exhibit decreased weight with markedly decreased adiposity, low plasma leptin levels, and increased energy expenditure without alterations in feeding behavior. The expression of the orexigenic neuropeptide Y (NPY) in the hypothalamus to the low leptin state was abnormal at basal and fasting conditions. In addition, arcuate NPY neurons exhibited abnormal electrophysiological responses to leptin in Tg2576 hypothalamic slices or wild-type slices treated with Aβ. Finally, the metabolic deficits worsened as Tg2576 mice aged and amyloid burden increased in the brain. These results indicate that excess Aβ can potentially disrupt hypothalamic arcuate NPY neurons leading to weight loss and a pathologically low leptin state early in the disease process that progressively worsens as the amyloid burden increases. Collectively, these findings suggest that weight loss is an intrinsic pathological feature of Aβ accumulation and identify hypothalamic leptin signaling as a previously unrecognized pathogenic site of action for Aβ. PMID:24990930

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

Cortical evoked responses associated with arousal from sleep.

PubMed

Phillips, Derrick J; Schei, Jennifer L; Meighan, Peter C; Rector, David M

2011-01-01

To determine if low-level intermittent auditory stimuli have the potential to disrupt sleep during 24-h recordings, we assessed arousal occurrence to varying stimulus intensities. Additionally, if stimulus-generated evoked response potential (ERP) components provide a metric of underlying cortical state, then a particular ERP structure may precede an arousal. Physiological electrodes measuring EEG, EKG, and EMG were implanted into 5 adult female Sprague-Dawley rats. We delivered auditory stimuli of varying intensities (50-75 dBa sound pressure level SPL) at random intervals of 6-12 s over a 24-hour period. Recordings were divided into 2-s epochs and scored for sleep/wake state. Following each stimulus, we identified whether the animal stayed asleep or woke. We then sorted the stimuli depending on prior and post-stimulus state, and measured ERP components. Auditory stimuli did not produce a significant increase in the number of arousals compared to silent control periods. Overall, arousal from REM sleep occurred more often compared to quiet sleep. ERPs preceding an arousal had decreased mean area and shorter N1 latency. Low level auditory stimuli did not fragment animal sleep since we observed no significant change in arousal occurrence. Arousals that occurred within 4 s of a stimulus exhibited an ERP mean area and latency had features similar to ERPs generated during wake, indicating that the underlying cortical tissue state may contribute to physiological conditions required for arousal.

Salient Feature of Haptic-Based Guidance of People in Low Visibility Environments Using Hard Reins.

PubMed

Ranasinghe, Anuradha; Sornkarn, Nantachai; Dasgupta, Prokar; Althoefer, Kaspar; Penders, Jacques; Nanayakkara, Thrishantha

2016-02-01

This paper presents salient features of human-human interaction where one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several salient features of the interaction between the guider and follower such as: 1) the order of an autoregressive (AR) control policy that maps states of the follower to actions of the guider; 2) how the guider may modulate the pulling force in response to the trust level of the follower; and 3) how learning may successively apportion the responsibility of control across different muscles of the guider. Based on experimental systems identification on human demonstrations from ten pairs of naive subjects, we show that guiders tend to adopt a third-order AR predictive control policy and followers tend to adopt second-order reactive control policy. Moreover, the extracted guider's control policy was implemented and validated by human-robot interaction experiments. By modeling the follower's dynamics with a time varying virtual damped inertial system, we found that it is the coefficient of virtual damping which is most sensitive to the trust level of the follower. We used these experimental insights to derive a novel controller that integrates an optimal order control policy with a push/pull force modulator in response to the trust level of the follower monitored using a time varying virtual damped inertial model.

Phenotypic feature quantification of patient derived 3D cancer spheroids in fluorescence microscopy image

NASA Astrophysics Data System (ADS)

Kang, Mi-Sun; Rhee, Seon-Min; Seo, Ji-Hyun; Kim, Myoung-Hee

2017-03-01

Patients' responses to a drug differ at the cellular level. Here, we present an image-based cell phenotypic feature quantification method for predicting the responses of patient-derived glioblastoma cells to a particular drug. We used high-content imaging to understand the features of patient-derived cancer cells. A 3D spheroid culture formation resembles the in vivo environment more closely than 2D adherent cultures do, and it allows for the observation of cellular aggregate characteristics. However, cell analysis at the individual level is more challenging. In this paper, we demonstrate image-based phenotypic screening of the nuclei of patient-derived cancer cells. We first stitched the images of each well of the 384-well plate with the same state. We then used intensity information to detect the colonies. The nuclear intensity and morphological characteristics were used for the segmentation of individual nuclei. Next, we calculated the position of each nucleus that is appeal of the spatial pattern of cells in the well environment. Finally, we compared the results obtained using 3D spheroid culture cells with those obtained using 2D adherent culture cells from the same patient being treated with the same drugs. This technique could be applied for image-based phenotypic screening of cells to determine the patient's response to the drug.

The Effects of State Policy Design Features on Take-Up and Crowd-Out Rates for the Sate Children's Health Insurance Program

ERIC Educational Resources Information Center

Bansak, Cynthia; Raphael, Steven

2007-01-01

We evaluate the effects of state policy design features on SCHIP take-up rates and on the degree to which SCHIP benefits crowd out private benefits. The results indicate overall program take-up rates of approximately 10 percent. However, there is considerable heterogeneity across states, suggesting a potential role of inter-state variation in…

Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta

NASA Astrophysics Data System (ADS)

Zhang, Hongsheng; Xu, Ru

2018-02-01

Integrating synthetic aperture radar (SAR) and optical data to improve urban land cover classification has been identified as a promising approach. However, which integration level is the most suitable remains unclear but important to many researchers and engineers. This study aimed to compare different integration levels for providing a scientific reference for a wide range of studies using optical and SAR data. SAR data from TerraSAR-X and ENVISAT ASAR in both WSM and IMP modes were used to be combined with optical data at pixel level, feature level and decision levels using four typical machine learning methods. The experimental results indicated that: 1) feature level that used both the original images and extracted features achieved a significant improvement of up to 10% compared to that using optical data alone; 2) different levels of fusion required different suitable methods depending on the data distribution and data resolution. For instance, support vector machine was the most stable at both the feature and decision levels, while random forest was suitable at the pixel level but not suitable at the decision level. 3) By examining the distribution of SAR features, some features (e.g., homogeneity) exhibited a close-to-normal distribution, explaining the improvement from the maximum likelihood method at the feature and decision levels. This indicated the benefits of using texture features from SAR data when being combined with optical data for land cover classification. Additionally, the research also shown that combining optical and SAR data does not guarantee improvement compared with using single data source for urban land cover classification, depending on the selection of appropriate fusion levels and fusion methods.

Understanding the relationship between Kano model's customer satisfaction scores and self-stated requirements importance.

PubMed

Mkpojiogu, Emmanuel O C; Hashim, Nor Laily

2016-01-01

Customer satisfaction is the result of product quality and viability. The place of the perceived satisfaction of users/customers for a software product cannot be neglected especially in today competitive market environment as it drives the loyalty of customers and promotes high profitability and return on investment. Therefore understanding the importance of requirements as it is associated with the satisfaction of users/customers when their requirements are met is worth the pain considering. It is necessary to know the relationship between customer satisfactions when their requirements are met (or their dissatisfaction when their requirements are unmet) and the importance of such requirement. So many works have been carried out on customer satisfaction in connection with the importance of requirements but the relationship between customer satisfaction scores (coefficients) of the Kano model and users/customers self-stated requirements importance have not been sufficiently explored. In this study, an attempt is made to unravel the underlying relationship existing between Kano model's customer satisfaction indexes and users/customers self reported requirements importance. The results of the study indicate some interesting associations between these considered variables. These bivariate associations reveal that customer satisfaction index (SI), and average satisfaction coefficient (ASC) and customer dissatisfaction index (DI) and average satisfaction coefficient (ASC) are highly correlated (r = 96 %) and thus ASC can be used in place of either SI or DI in representing customer satisfaction scores. Also, these Kano model's customer satisfaction variables (SI, DI, and ASC) are each associated with self-stated requirements importance (IMP). Further analysis indicates that the value customers or users place on requirements that are met or on features that are incorporated into a product influences the level of satisfaction such customers derive from the product. The worth of a product feature is indicated by the perceived satisfaction customers get from the inclusion of such feature in the product design and development. The satisfaction users/customers derive when a requirement is fulfilled or when a feature is placed in the product (SI or ASC) is strongly influenced by the value the users/customers place on such requirements/features when met (IMP). However, the dissatisfaction users/customers received when a requirement is not met or when a feature is not incorporated into the product (DI), even though related to self-stated requirements importance (IMP), does not have a strong effect on the importance/worth (IMP) of that given requirement/feature as perceived by the users or customers. Therefore, since customer satisfaction is proportionally related to the perceived requirements importance (worth), it is then necessary to give adequate attention to user/customer satisfying requirements (features) from elicitation to design and to the final implementation of the design. Incorporating user or customer satisfying requirements in product design is of great worth or value to the future users or customers of the product.

Comparative Photoemission Study of Actinide (Am, Pu, Np and U) Metals, Nitrides, and Hydrides

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

Gouder, Thomas; Seibert, Alice; Rebizant, Jean

2007-07-01

Core-level and valence-band spectra of Pu and the other early actinide compounds show remarkable systematics, which can be understood in the framework of final state screening. We compare the early actinide (U, Np, Pu and Am) metals, nitrides and hydrides and a few other specific compounds (PuSe, PuS, PuCx, PuSix) prepared as thin films by sputter deposition. In choosing these systems, we combine inherent 5f band narrowing, due to 5f orbital contraction throughout the actinide series, with variations of the chemical environment in the compounds. Goal of this work was to learn more on the electronic structure of the earlymore » actinide systems and to achieve the correct interpretation of their photoemission spectra. The highly correlated nature of the 5f states in systems, which are on the verge to localization, makes this a challenging task, because of the peculiar interplay between ground state DOS and final-state effects. Their influence can be estimated by doing systematic studies on systems with different (5f) bandwidths. We conclude on the basis of such systematic experiments that final-state effects due to strong e-e correlations in narrow 5f-band systems lead to multiplet like structures, analogous to those observed in the case of systems with localized electron states. Such observations in essentially band-like 5f-systems was first surprising, but the astonishing similarity of photoemission spectra of very different chemical systems (e.g. PuSe, Pu{sub 2}C{sub 3}..) points to a common origin, relating them to atomic features rather than material dependent density of states (DOS) features. (authors)« less

Topological edge states and impurities: Manifestation in the local static and dynamical characteristics of dimerized quantum chains

NASA Astrophysics Data System (ADS)

Zvyagin, A. A.

2018-04-01

Based on the results of exact analytic calculations, we show that topological edge states and impurities in quantum dimerized chains manifest themselves in various local static and dynamical characteristics, which can be measured in experiments. In particular, topological edge states can be observed in the magnetic field behavior of the local magnetization or magnetic susceptibility of dimerized spin chains as jumps (for the magnetization) and features (for the static susceptibility) at zero field. In contrast, impurities reveal themselves in similar jumps and features, however, at nonzero values of the critical field. We also show that dynamical characteristics of dimerized quantum chains also manifest the features, related to the topological edge states and impurities. Those features, as a rule, can be seen more sharply than the manifestation of bulk extended states in, e.g., the dynamical local susceptibility. Such peculiarities can be observed in one-dimensional dimerized spin chains, e.g., in NMR experiments, or in various realizations of quantum dimerized chains in optical experiments.

Segmenting the human genome based on states of neutral genetic divergence.

PubMed

Kuruppumullage Don, Prabhani; Ananda, Guruprasad; Chiaromonte, Francesca; Makova, Kateryna D

2013-09-03

Many studies have demonstrated that divergence levels generated by different mutation types vary and covary across the human genome. To improve our still-incomplete understanding of the mechanistic basis of this phenomenon, we analyze several mutation types simultaneously, anchoring their variation to specific regions of the genome. Using hidden Markov models on insertion, deletion, nucleotide substitution, and microsatellite divergence estimates inferred from human-orangutan alignments of neutrally evolving genomic sequences, we segment the human genome into regions corresponding to different divergence states--each uniquely characterized by specific combinations of divergence levels. We then parsed the mutagenic contributions of various biochemical processes associating divergence states with a broad range of genomic landscape features. We find that high divergence states inhabit guanine- and cytosine (GC)-rich, highly recombining subtelomeric regions; low divergence states cover inner parts of autosomes; chromosome X forms its own state with lowest divergence; and a state of elevated microsatellite mutability is interspersed across the genome. These general trends are mirrored in human diversity data from the 1000 Genomes Project, and departures from them highlight the evolutionary history of primate chromosomes. We also find that genes and noncoding functional marks [annotations from the Encyclopedia of DNA Elements (ENCODE)] are concentrated in high divergence states. Our results provide a powerful tool for biomedical data analysis: segmentations can be used to screen personal genome variants--including those associated with cancer and other diseases--and to improve computational predictions of noncoding functional elements.

Gamow-Teller transitions between proton h11/2 and neutron h9/2 partner orbitals in 140I

NASA Astrophysics Data System (ADS)

Moon, B.; Moon, C.-B.; Odahara, A.; Lozeva, R.; Söderström, P.-A.; Nishimura, S.; Yuan, C.; Hong, B.; for theNP1112-RIBF87 Collaboration

2018-04-01

The excited states of the neutron-rich nucleus 140I were, for the first time, investigated by a β-delayed γ-ray spectroscopy. The parent nuclide 140Te was produced through the in-flight fission of the 238U beam at 345 MeV per nucleon on a 9Be target at the Radioactive Isotope Beam Factory (RIBF), RIKEN in Japan. The half-life of 140Te was measured to be 350(5) ms and the spin-parity of ground state of 140I was found to be 2-. The spin-parities of three levels at 926, 1188, and 1787 keV were assigned as 1+ based on log f t values. These allowed Gamow-Teller (G-T) transition-states could be interpreted as the transformation of a neutron in the h9/2 orbital into a proton in the h11/2 orbital. Systematic features of level structures and G-T transitions are discussed in the frameworks of the large-scale shell model and deformed shell model.

GEOGRAPHIC NAMES INFORMATION SYSTEM (GNIS) ...

EPA Pesticide Factsheets

The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey in cooperation with the U.S. Board on Geographic Names (BGN), contains information about physical and cultural geographic features in the United States and associated areas, both current and historical, but not including roads and highways. The database also contains geographic names in Antarctica. The database holds the Federally recognized name of each feature and defines the location of the feature by state, county, USGS topographic map, and geographic coordinates. Other feature attributes include names or spellings other than the official name, feature designations, feature class, historical and descriptive information, and for some categories of features the geometric boundaries. The database assigns a unique feature identifier, a random number, that is a key for accessing, integrating, or reconciling GNIS data with other data sets. The GNIS is our Nation's official repository of domestic geographic feature names information.

Village Building Identification Based on Ensemble Convolutional Neural Networks

PubMed Central

Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

2017-01-01

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

The European space exploration programme: current status of ESA's plans for Moon and Mars exploration.

PubMed

Messina, Piero; Vennemann, Dietrich

2005-01-01

After a large consultation with the scientific and industrial communities in Europe, the Aurora Space Exploration Programme was unanimously approved at the European Space Agency (ESA) Council at ministerial level in Edinburgh in 2001. This marked the start of the programme's preparation phase that was due to finish by the end of 2004. Aurora features technology development robotic and crewed rehearsal missions aimed at preparing a human mission to Mars by 2033. Due to the evolving context, both international and European, ESA has undertaken a review of the goals and approach of its exploration programme. While maintaining the main robotic missions that had been conceived during Aurora, the European Space Exploration Programme that is currently being proposed to the Aurora participating states and other ESA Member States has a reviewed approach and will feature a greater synergy with other ESA programmes. The paper will present the process that led to the revision of ESA's plans in the field of exploration and will give the current status of the programme. c2005 Published by Elsevier Ltd.

Social anxiety and the severity and typography of stuttering in adolescents.

PubMed

Mulcahy, Kylie; Hennessey, Neville; Beilby, Janet; Byrnes, Michelle

2008-12-01

The present study examined the relationship between anxiety, attitude toward daily communication, and stuttering symptomatology in adolescent stuttering. Adolescents who stuttered (n=19) showed significantly higher levels of trait, state and social anxiety than fluent speaking controls (n=18). Trait and state anxiety was significantly associated with difficulty with communication in daily situations for adolescents who stutter, but not for controls. No statistically significant associations were found between anxiety and measures of communication difficulty, and the severity or typography of stuttering surface behaviours. These results highlight some of the psychosocial concomitants of chronic stuttering in adolescence, but challenge the notion that anxiety plays a direct mediating role in stuttering surface behaviours. Rather, the results suggest stuttering is a disorder that features psychosocial conflict regardless of its surface features. The reader will be able to: (1) summarise findings from previous studies with regards to stuttering and anxiety; (2) identify the sub-types of anxiety that may impact on the individual who stutters; and (3) discuss the clinical implications of the results with regards to working with adolescents who stutter.

Vibrational Mode-Specific Autodetachment and Coupling of CH2CN-

NASA Astrophysics Data System (ADS)

Lyle, Justin; Mabbs, Richard

2017-06-01

The Cyanomethyl Anion, CH_{2}CN-, and neutral radical have been studied extensively, with several findings of autodetachment about the totally symmetric transition, as well as high resolution experiments revealing symmetrically forbidden and weak vibrational features. We report photoelectron spectra using the Velocity-Mapped Imaging Technique in 1-2 \\wn increments over a range of 13460 to 15384 \\wn that has not been previously examined. These spectra include excitation of the ground state cyanomethyl anion into the direct detachment thresholds of previously reported vibrational modes for the neutral radical. Significant variations from Franck-Condon behavior were observed in the branching ratios for resolved vibrational features for excitation in the vicinity of the thresholds involving the νb{3} and νb{5} modes. These are consistent with autodetachment from rovibrational levels of a dipole bound state acting as a resonance in the detachment continuum. The autodetachment channels involve single changes in vibrational quantum number, consistent with the vibrational propensity rule but in some cases reveal relaxation to a different vibrational mode indicating coupling between the modes and/or a breakdown of the normal mode approximation.

Towards discrete wavelet transform-based human activity recognition

NASA Astrophysics Data System (ADS)

Khare, Manish; Jeon, Moongu

2017-06-01

Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

Coherent population trapping with a controlled dissipation: applications in optical metrology

NASA Astrophysics Data System (ADS)

Nicolas, L.; Delord, T.; Jamonneau, P.; Coto, R.; Maze, J.; Jacques, V.; Hétet, G.

2018-03-01

We analyze the properties of a pulsed coherent population trapping protocol that uses a controlled decay from the excited state in a Λ-level scheme. We study this problem analytically and numerically and find regimes where narrow transmission, absorption, or fluorescence spectral lines occur. We then look for optimal frequency measurements using these spectral features by computing the Allan deviation in the presence of ground state decoherence and show that the protocol is on a par with Ramsey-CPT. We discuss possible implementations with ensembles of alkali atoms and single ions and demonstrate that typical pulsed-CPT experiments that are realized on femto-second timescales can be implemented on micro-seconds timescales using this scheme.

Nonergodicity in binary alloys

NASA Astrophysics Data System (ADS)

Son, Leonid; Sidorov, Valery; Popel, Pjotr; Shulgin, Dmitry

2015-09-01

For binary liquids with limited miscibility of the components, we provide the corrections to the equation of state which arise from the nonergogic diffusivity. It is shown that these corrections result in lowering of critical miscibility point. In some cases, it may result in a bifurcation of miscibility curve: the mixtures near 50% concentration which are homogeneous at the microscopic level, occur to be too stable to provide a quasi - eutectic triple point. These features provide a new look on the phase diagrams of some binary systems. In present work, we discuss Ga-Pb, Fe-Cu, and Cu-Zr alloys. Our investigation corresponds their complex behavior in liquid state to the shapes of their phase diagrams.

The Impact of Health Care Reform on Hospital and Preventive Care: Evidence from Massachusetts☆

PubMed Central

Kolstad, Jonathan T.; Kowalski, Amanda E.

2012-01-01

In April 2006, Massachusetts passed legislation aimed at achieving near-universal health insurance coverage. The key features of this legislation were a model for national health reform, passed in March 2010. The reform gives us a novel opportunity to examine the impact of expansion to near-universal coverage state-wide. Among hospital discharges in Massachusetts, we find that the reform decreased uninsurance by 36% relative to its initial level and to other states. Reform affected utilization by decreasing length of stay, the number of inpatient admissions originating from the emergency room, and preventable admissions. At the same time, hospital cost growth did not increase. PMID:23180894

Should I Stay or Should I Go? A Habitat-Dependent Dispersal Kernel Improves Prediction of Movement

PubMed Central

Vinatier, Fabrice; Lescourret, Françoise; Duyck, Pierre-François; Martin, Olivier; Senoussi, Rachid; Tixier, Philippe

2011-01-01

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes. PMID:21765890

Should I stay or should I go? A habitat-dependent dispersal kernel improves prediction of movement.

PubMed

Vinatier, Fabrice; Lescourret, Françoise; Duyck, Pierre-François; Martin, Olivier; Senoussi, Rachid; Tixier, Philippe

2011-01-01

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes.

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

Affective Computing and the Impact of Gender and Age

PubMed Central

Rukavina, Stefanie; Gruss, Sascha; Hoffmann, Holger; Tan, Jun-Wen; Walter, Steffen; Traue, Harald C.

2016-01-01

Affective computing aims at the detection of users’ mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods. Although this is of high priority, other subject-specific variables should not be neglected. In our study, we analyzed the effect of gender, age, personality and gender roles on the extracted psychobiological features (derived from skin conductance level, facial electromyography and heart rate variability) as well as the influence on the classification results. In an experimental human-computer interaction, five different affective states with picture material from the International Affective Picture System and ULM pictures were induced. A total of 127 subjects participated in the study. Among all potentially influencing variables (gender has been reported to be influential), age was the only variable that correlated significantly with psychobiological responses. In summary, the conducted classification processes resulted in 20% classification accuracy differences according to age and gender, especially when comparing the neutral condition with four other affective states. We suggest taking age and gender specifically into account for future studies in affective computing, as these may lead to an improvement of emotion recognition accuracy. PMID:26939129

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Luminescence Mechanism of Carbon Dots by Tailoring Functional Groups for Sensing Fe3+ Ions

PubMed Central

Yu, Jingjing; Liu, Chang; Yuan, Kang; Lu, Zunming; Cheng, Yahui; Li, Lanlan; Jin, Peng; Meng, Fanbin; Liu, Hui

2018-01-01

In this paper, spherical carbon dots (CDs) with distinct compositions and surface states have been successfully synthesized by a facile microwave method. From the fluorescence spectra, several characteristic luminescence features have been observed: surface amino groups are dominant in the whole emission spectra centering at 445 nm, and the fingerprint emissions relevant to the impurity levels formed by some groups related to C and N elements, including C-C/C=C (intrinsic C), C-N (graphitic N), N-containing heterocycles (pyridine N) and C=O groups, are located around 305 nm, 355 nm, 410 nm, and 500 nm, respectively. Those fine luminescence features could be ascribed to the electron transition among various trapping states within the band structure caused by different chemical bonds in carbon cores, or functional groups attached to the CDs’ surfaces. According to the theoretical calculations and experimental results, a scheme of the band structure has been proposed to describe the positions of those trapping states within the band gap. Additionally, it has also been observed that the emission of CDs is sensitive to the concentration of Fe3+ ions with a linear relation in the range of Fe3+ concentration from 12.5 to 250 μM. PMID:29649110

Analysis of MTMR1 expression and correlation with muscle pathological features in juvenile/adult onset myotonic dystrophy type 1 (DM1) and in myotonic dystrophy type 2 (DM2).

PubMed

Santoro, Massimo; Modoni, Anna; Masciullo, Marcella; Gidaro, Teresa; Broccolini, Aldobrando; Ricci, Enzo; Tonali, Pietro Attilio; Silvestri, Gabriella

2010-10-01

Among genes abnormally expressed in myotonic dystrophy type1 (DM1), the myotubularin-related 1 gene (MTMR1) was related to impaired muscle differentiation. Therefore, we analyzed MTMR1 expression in correlation with CUG-binding protein1 (CUG-BP1) and muscleblind-like1 protein (MBNL1) steady-state levels and with morphological features in muscle tissues from DM1 and myotonic dystrophy type 2 (DM2) patients. Semi-quantitative RT-PCR for MTMR1 was done on muscle biopsies and primary muscle cultures. The presence of impaired muscle fiber maturation was evaluated using immunochemistry for neural cell adhesion molecule (NCAM), Vimentin and neonatal myosin heavy chain. CUG-BP1 and MBNL1 steady-state levels were estimated by Western blot. RNA-fluorescence in situ hybridization combined with immunochemistry for CUG-BP1, MBNL1 and NCAM were performed on serial muscle sections. An aberrant splicing of MTMR1 and a significant amount of NCAM-positive myofibers were detected in DM1 and DM2 muscle biopsies; these alterations correlated with DNA repeat expansion size only in DM1. CUG-BP1 levels were increased only in DM1 muscles, while MBNL1 levels were similar among DM1, DM2 and controls. Normal and NCAM-positive myofibers displayed no differences either in the amount of ribonuclear foci and the intracellular distribution of MBNL1 and CUG-BP1. In conclusion, an aberrant MTMR1 expression and signs of altered myofiber maturation were documented in both DM1 and in DM2 muscle tissues. The more severe dysregulation of MTMR1 expression in DM1 versus DM2, along with increased CUG-BP1 levels only in DM1 tissues, suggests that the mutual antagonism between MBNL1 and CUG-BP1 on alternative splicing is more unbalanced in DM1. Copyright © 2010 Elsevier Inc. All rights reserved.

The Built Environment and Cognitive Disorders: Results From the Cognitive Function and Ageing Study II.

PubMed

Wu, Yu-Tzu; Prina, A Matthew; Jones, Andy; Matthews, Fiona E; Brayne, Carol

2017-07-01

Built environment features have been related to behavior modification and might stimulate cognitive activity with a potential impact on cognitive health in later life. This study investigated cross-sectional associations between features of land use and cognitive impairment and dementia, and also explored urban and rural differences in these associations. Postcodes of the 7,505 community-based participants (aged ≥65 years) in the Cognitive Function and Ageing Study II (collected in 2008-2011) were linked to environmental data from government statistics. Multilevel logistic regression investigated associations between cognitive impairment (defined as Mini-Mental State Examination score ≤25) and dementia (Geriatric Mental Status and Automatic Geriatric Examination for Computer-Assisted Taxonomy organicity level ≥3) and land use features, including natural environment availability and land use mix, fitting interaction terms with three rural/urban categories. Data were analyzed in 2015. Associations between features of land use and cognitive impairment were not linear. After adjusting for individual-level factors and area deprivation, living in areas with high land use mix was associated with a nearly 30% decreased odds of cognitive impairment (OR=0.72, 95% CI=0.58, 0.89). This was similar, yet non-significant, for dementia (OR=0.70, 95% CI=0.46, 1.06). In conurbations, living in areas with high natural environment availability was associated with 30% reduced odds of cognitive impairment (OR=0.70, 95% CI=0.50, 0.97). Non-linear associations between features of land use and cognitive impairment were confirmed in this new cohort of older people in England. Both lack of and overload of environmental stimulation may be detrimental to cognition in later life. Copyright © 2017 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

PubMed

Li, Man; Ling, Cheng; Xu, Qi; Gao, Jingyang

2018-02-01

Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithms. To improve prediction accuracy, these algorithms must confront the key challenge of extracting valuable features. In this work, we propose a feature-enhanced protein classification approach, considering the rich generation of multiple sequence alignment algorithms, N-gram probabilistic language model and the deep learning technique. The essence behind the proposed method is that if each group of sequences can be represented by one feature sequence, composed of homologous sites, there should be less loss when the sequence is rebuilt, when a more relevant sequence is added to the group. On the basis of this consideration, the prediction becomes whether a query sequence belonging to a group of sequences can be transferred to calculate the probability that the new feature sequence evolves from the original one. The proposed work focuses on the hierarchical classification of G-protein Coupled Receptors (GPCRs), which begins by extracting the feature sequences from the multiple sequence alignment results of the GPCRs sub-subfamilies. The N-gram model is then applied to construct the input vectors. Finally, these vectors are imported into a convolutional neural network to make a prediction. The experimental results elucidate that the proposed method provides significant performance improvements. The classification error rate of the proposed method is reduced by at least 4.67% (family level I) and 5.75% (family Level II), in comparison with the current state-of-the-art methods. The implementation program of the proposed work is freely available at: https://github.com/alanFchina/CNN .

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification.

PubMed

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

PubMed Central

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. PMID:26640813

The relationship between visual working memory and attention: retention of precise colour information in the absence of effects on perceptual selection.

PubMed

Hollingworth, Andrew; Hwang, Seongmin

2013-10-19

We examined the conditions under which a feature value in visual working memory (VWM) recruits visual attention to matching stimuli. Previous work has suggested that VWM supports two qualitatively different states of representation: an active state that interacts with perceptual selection and a passive (or accessory) state that does not. An alternative hypothesis is that VWM supports a single form of representation, with the precision of feature memory controlling whether or not the representation interacts with perceptual selection. The results of three experiments supported the dual-state hypothesis. We established conditions under which participants retained a relatively precise representation of a parcticular colour. If the colour was immediately task relevant, it reliably recruited attention to matching stimuli. However, if the colour was not immediately task relevant, it failed to interact with perceptual selection. Feature maintenance in VWM is not necessarily equivalent with feature-based attentional selection.

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments

PubMed Central

Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke

2017-01-01

Recent advances in Deep convolutional Neural Networks (DNNs) have enabled unprecedentedly accurate computational models of brain representations, and present an exciting opportunity to model diverse cognitive functions. State-of-the-art DNNs achieve human-level performance on object categorisation, but it is unclear how well they capture human behavior on complex cognitive tasks. Recent reports suggest that DNNs can explain significant variance in one such task, judging object similarity. Here, we extend these findings by replicating them for a rich set of object images, comparing performance across layers within two DNNs of different depths, and examining how the DNNs’ performance compares to that of non-computational “conceptual” models. Human observers performed similarity judgments for a set of 92 images of real-world objects. Representations of the same images were obtained in each of the layers of two DNNs of different depths (8-layer AlexNet and 16-layer VGG-16). To create conceptual models, other human observers generated visual-feature labels (e.g., “eye”) and category labels (e.g., “animal”) for the same image set. Feature labels were divided into parts, colors, textures and contours, while category labels were divided into subordinate, basic, and superordinate categories. We fitted models derived from the features, categories, and from each layer of each DNN to the similarity judgments, using representational similarity analysis to evaluate model performance. In both DNNs, similarity within the last layer explains most of the explainable variance in human similarity judgments. The last layer outperforms almost all feature-based models. Late and mid-level layers outperform some but not all feature-based models. Importantly, categorical models predict similarity judgments significantly better than any DNN layer. Our results provide further evidence for commonalities between DNNs and brain representations. Models derived from visual features other than object parts perform relatively poorly, perhaps because DNNs more comprehensively capture the colors, textures and contours which matter to human object perception. However, categorical models outperform DNNs, suggesting that further work may be needed to bring high-level semantic representations in DNNs closer to those extracted by humans. Modern DNNs explain similarity judgments remarkably well considering they were not trained on this task, and are promising models for many aspects of human cognition. PMID:29062291

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

PubMed

Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t -test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

Analysis of within Subjects Variability in Mouse Ultrasonic Vocalization: Pups Exhibit Inconsistent, State-Like Patterns of Call Production

PubMed Central

Rieger, Michael A.; Dougherty, Joseph D.

2016-01-01

Mice produce ultrasonic vocalizations (USV) in multiple communicative contexts, including adult social interaction (e.g., male to female courtship), as well as pup calls when separated from the dam. Assessment of pup USV has been widely applied in models of social and communicative disorders, dozens of which have shown alterations to this conserved behavior. However, features such as call production rate can vary substantially even within experimental groups and it is unclear to what extent aspects of USV represent stable trait-like influences or are vulnerable to an animal's state. To address this question, we have employed a mixed modeling approach to describe consistency in USV features across time, leveraging multiple large cohorts recorded from two strains, and across ages/times. We find that most features of pup USV show consistent patterns within a recording session, but inconsistent patterns across postnatal development. This supports the conclusion that pup USV is most strongly influenced by “state”-like variables. In contrast, adult USV call rate and call duration show higher consistency across sessions and may reflect a stable “trait.” However, spectral features of adult song such as the presence of pitch jumps do not show this level of consistency, suggesting that pitch modulation is more susceptible to factors affecting the animal's state at the time of recording. Overall, the utility of this work is three-fold. First, as variability necessarily affects the sensitivity of the assay to detect experimental perturbation, we hope the information provided here will be used to help researchers plan sufficiently powered experiments, as well as prioritize specific ages to study USV behavior and to decide which features to consider most strongly in analysis. Second, via the mouseTube platform, we have provided these hundreds of recordings and associated data to serve as a shared resource for other researchers interested in either benchmark data for these strains or in developing algorithms for studying features of mouse song. Finally, we hope that this work informs both interpretation of USV studies in models of developmental disorder, and helps to further research into understanding the neural processes that contribute to the production and predictability of USV behavior. PMID:27733819

Distant supervision for neural relation extraction integrated with word attention and property features.

PubMed

Qu, Jianfeng; Ouyang, Dantong; Hua, Wen; Ye, Yuxin; Li, Ximing

2018-04-01

Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack useful sentence information for some positive training instances. To address the above issues, we propose a novel neural relation extraction model. First, we develop a word-level attention mechanism to distinguish the importance of each individual word in a sentence, increasing the attention weights for those critical words. Second, we investigate the semantic information from word embeddings of target entities, which can be developed as a supplementary feature for the extractor. Experimental results show that our model outperforms previous state-of-the-art baselines. Copyright © 2018 Elsevier Ltd. All rights reserved.

The National Map - geographic names

USGS Publications Warehouse

Yost, Lou; Carswell, William J.

2009-01-01

The Geographic Names Information System (GNIS), developed by the U.S. Geological Survey (USGS) in cooperation with the U.S. Board on Geographic Names (BGN), contains information about the official names for places, features, and areas in the 50 States, the District of Columbia, the territories and outlying areas of the United States, including Antarctica. It is the geographic names component of The National Map. The BGN maintains working relationships with State names authorities to cooperate in achieving the standardization of geographic names. The GNIS contains records on more than 2 million geographic names in the United States - from populated places, schools, reservoirs, and parks to streams, valleys, springs, ridges, and every feature type except roads and highways. Entries include information such as the federally-recognized name and variant names and spellings for the feature; former names; the status of the name as determined by the BGN; county or counties in which each named feature is located; geographic coordinates that locate the approximate center of an aerial feature or the mouth and source of a linear feature, such as a stream; name of the cell of the USGS topographic map or maps on which the feature may appear; elevation figures derived from the National Elevation Dataset; bibliographic code for the source of the name; BGN decision dates and historical information are available for some features. Data from the GNIS are used for emergency preparedness, mapmaking, local and regional planning, service delivery routing, marketing, site selection, environmental analysis, genealogical research, and other applications.

Context-Aware Local Binary Feature Learning for Face Recognition.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2018-05-01

In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.

Ab initio multiple spawning dynamics study of dimethylnitramine and dimethylnitramine-Fe complex to model their ultrafast nonadiabatic chemistry

NASA Astrophysics Data System (ADS)

Bera, Anupam; Ghosh, Jayanta; Bhattacharya, Atanu

2017-07-01

Conical intersections are now firmly established to be the key features in the excited electronic state processes of polyatomic energetic molecules. In the present work, we have explored conical intersection-mediated nonadiabatic chemical dynamics of a simple analogue nitramine molecule, dimethylnitramine (DMNA, containing one N-NO2 energetic group), and its complex with an iron atom (DMNA-Fe). For this task, we have used the ab initio multiple spawning (AIMS) dynamics simulation at the state averaged-complete active space self-consistent field(8,5)/6-31G(d) level of theory. We have found that DMNA relaxes back to the ground (S0) state following electronic excitation to the S1 excited state [which is an (n,π*) excited state] with a time constant of approximately 40 fs. This AIMS result is in very good agreement with the previous surface hopping-result and femtosecond laser spectroscopy result. DMNA does not dissociate during this fast internal conversion from the S1 to the S0 state. DMNA-Fe also undergoes extremely fast relaxation from the upper S1 state to the S0 state; however, this relaxation pathway is dissociative in nature. DMNA-Fe undergoes initial Fe-O, N-O, and N-N bond dissociations during relaxation from the upper S1 state to the ground S0 state through the respective conical intersection. The AIMS simulation reveals the branching ratio of these three channels as N-N:Fe-O:N-O = 6:3:1 (based on 100 independent simulations). Furthermore, the AIMS simulation reveals that the Fe-O bond dissociation channel exhibits the fastest (time constant 24 fs) relaxation, while the N-N bond dissociation pathway features the slowest (time constant 128 fs) relaxation. An intermediate time constant (30 fs) is found for the N-O bond dissociation channel. This is the first nonadiabatic chemical dynamics study of metal-contained energetic molecules through conical intersections.

Histogram-based adaptive gray level scaling for texture feature classification of colorectal polyps

NASA Astrophysics Data System (ADS)

Pomeroy, Marc; Lu, Hongbing; Pickhardt, Perry J.; Liang, Zhengrong

2018-02-01

Texture features have played an ever increasing role in computer aided detection (CADe) and diagnosis (CADx) methods since their inception. Texture features are often used as a method of false positive reduction for CADe packages, especially for detecting colorectal polyps and distinguishing them from falsely tagged residual stool and healthy colon wall folds. While texture features have shown great success there, the performance of texture features for CADx have lagged behind primarily because of the more similar features among different polyps types. In this paper, we present an adaptive gray level scaling and compare it to the conventional equal-spacing of gray level bins. We use a dataset taken from computed tomography colonography patients, with 392 polyp regions of interest (ROIs) identified and have a confirmed diagnosis through pathology. Using the histogram information from the entire ROI dataset, we generate the gray level bins such that each bin contains roughly the same number of voxels Each image ROI is the scaled down to two different numbers of gray levels, using both an equal spacing of Hounsfield units for each bin, and our adaptive method. We compute a set of texture features from the scaled images including 30 gray level co-occurrence matrix (GLCM) features and 11 gray level run length matrix (GLRLM) features. Using a random forest classifier to distinguish between hyperplastic polyps and all others (adenomas and adenocarcinomas), we find that the adaptive gray level scaling can improve performance based on the area under the receiver operating characteristic curve by up to 4.6%.

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study.

PubMed

A Santos, Jose C; Nassif, Houssam; Page, David; Muggleton, Stephen H; E Sternberg, Michael J

2012-07-11

There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. In addition to confirming literature results, ProGolem's model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners.

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.

PubMed

Galavis, Paulina E; Hollensen, Christian; Jallow, Ngoneh; Paliwal, Bhudatt; Jeraj, Robert

2010-10-01

Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [(18)F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.

Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters

PubMed Central

GALAVIS, PAULINA E.; HOLLENSEN, CHRISTIAN; JALLOW, NGONEH; PALIWAL, BHUDATT; JERAJ, ROBERT

2014-01-01

Background Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters. Material and methods Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45–60 minutes post-injection of 10 mCi of [18F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value. Results Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%). Conclusion Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation. PMID:20831489

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches

PubMed Central

Chen, Xi; Wu, Qi; Ren, He; Chang, Fu-Kuo

2018-01-01

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles. PMID:29710832

Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks

NASA Astrophysics Data System (ADS)

Yang, Xue; Sun, Hao; Fu, Kun; Yang, Jirui; Sun, Xian; Yan, Menglong; Guo, Zhi

2018-01-01

Ship detection has been playing a significant role in the field of remote sensing for a long time but it is still full of challenges. The main limitations of traditional ship detection methods usually lie in the complexity of application scenarios, the difficulty of intensive object detection and the redundancy of detection region. In order to solve such problems above, we propose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ship in different scenes including ocean and port. Specifically, we put forward the Dense Feature Pyramid Network (DFPN), which is aimed at solving the problem resulted from the narrow width of the ship. Compared with previous multi-scale detectors such as Feature Pyramid Network (FPN), DFPN builds the high-level semantic feature-maps for all scales by means of dense connections, through which enhances the feature propagation and encourages the feature reuse. Additionally, in the case of ship rotation and dense arrangement, we design a rotation anchor strategy to predict the minimum circumscribed rectangle of the object so as to reduce the redundant detection region and improve the recall. Furthermore, we also propose multi-scale ROI Align for the purpose of maintaining the completeness of semantic and spatial information. Experiments based on remote sensing images from Google Earth for ship detection show that our detection method based on R-DFPN representation has a state-of-the-art performance.

Global-Local Interactions Modulate Tropical Moisture Exports to the Ohio River Basin

NASA Astrophysics Data System (ADS)

Doss-Gollin, J.; Farnham, D. J.; Lall, U.

2016-12-01

Regional-scale extreme rainfall and flooding are temporally and spatially associated with the occurrence of tropical moisture exports (TMEs) in the Ohio River Basin (ORB). TMEs are related to but not synonymous with atmospheric rivers, which refer to specific filiamentary organizational processes. TMEs to the ORB may be driven by strong, persistent ridging over the Eastern United States and troughing over the Central United States, creating favorable conditions for southerly flow and moisture transport from the Gulf of Mexico and Caribbean Sea. However, the strong inter-annual variation in TME activity over the ORB suggests dependence on global-scale features of the atmospheric circulation. We suggest that this synoptic dipole pattern may be viewed as the passage of one or more high-wavenumber, transient Rossby waves. We build a multi-level hierarchical Bayesian model in which the probability distribution of TME entering the ORB is a function of the phase and amplitude of the traveling waves. In turn, the joint distribution of the phase and amplitude of this wave is modulated by hemispheric-scale features of the atmospheric and oceanic circulation, and the amplitude and synchronization of quasi-stationary Rossby waves with wavenumber 1-4. Our approach bridges information about different features of the atmospheric circulation which inform the predictability of TME at multiple time scales and develops existing understanding of the atmospheric drivers of TMEs beyond existing composite and EOF studies.

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

Yan, Yiying, E-mail: yiyingyan@sjtu.edu.cn; Lü, Zhiguo, E-mail: zglv@sjtu.edu.cn; Zheng, Hang, E-mail: hzheng@sjtu.edu.cn

We present a theoretical formalism for resonance fluorescence radiating from a two-level system (TLS) driven by any periodic driving and coupled to multiple reservoirs. The formalism is derived analytically based on the combination of Floquet theory and Born–Markov master equation. The formalism allows us to calculate the spectrum when the Floquet states and quasienergies are analytically or numerically solved for simple or complicated driving fields. We can systematically explore the spectral features by implementing the present formalism. To exemplify this theory, we apply the unified formalism to comprehensively study a generic model that a harmonically driven TLS is simultaneously coupledmore » to a radiative reservoir and a dephasing reservoir. We demonstrate that the significant features of the fluorescence spectra, the driving-induced asymmetry and the dephasing-induced asymmetry, can be attributed to the violation of detailed balance condition, and explained in terms of the driving-related transition quantities between Floquet-states and their steady populations. In addition, we find the distinguished features of the fluorescence spectra under the biharmonic and multiharmonic driving fields in contrast with that of the harmonic driving case. In the case of the biharmonic driving, we find that the spectra are significantly different from the result of the RWA under the multiple resonance conditions. By the three concrete applications, we illustrate that the present formalism provides a routine tool for comprehensively exploring the fluorescence spectrum of periodically strongly driven TLSs.« less

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.

PubMed

Altazi, Baderaldeen A; Zhang, Geoffrey G; Fernandez, Daniel C; Montejo, Michael E; Hunt, Dylan; Werner, Joan; Biagioli, Matthew C; Moros, Eduardo G

2017-11-01

Site-specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from 18 Flourine-fluorodeoxyglucose ( 18 F-FDG) PET images for three parameters: manual versus computer-aided segmentation methods, gray-level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board-certified radiation oncologists manually segmented the metabolic tumor volume (MTV 1 and MTV 2 ) for each patient. For comparison, we used a graphical-based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down-sampled the tumor volumes into three gray-levels: 32, 64, and 128 from the original gray-level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D-reconstruction algorithms: maximum likelihood-ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning-ML-OSEM (FOREIR), FORE-filtered back projection (FOREFBP), and 3D-Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray-levels of down-sampled volumes, and PET reconstruction algorithms. The features were extracted using gray-level co-occurrence matrices (GLCM), gray-level size zone matrices (GLSZM), gray-level run-length matrices (GLRLM), neighborhood gray-tone difference matrices (NGTDM), shape-based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV 1 -MTV 2 , MTV 1 -GBSV, MTV 2 -GBSV; gray-levels: 64-32, 64-128, and 64-256; reconstruction algorithms: OSEM-FORE-OSEM, OSEM-FOREFBP, and OSEM-3DRP). We used |d¯| as a measure of radiomic feature reproducibility level, where any feature scored |d¯| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland-Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High- ±1% ≤ U/LRL ≤ ±30%; Intermediate- ±30% < U/LRL ≤ ±45%; Low- ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic feature reproducibility among segmentation pair MTV 1 -GBSV than MTV 2 -GBSV, gray-level pairs of 64-32 and 64-128 than 64-256, and reconstruction algorithm pairs of OSEM-FOREIR and OSEM-FOREFBP than OSEM-3DRP. Although the choice of cervical tumor segmentation method, gray-level value, and reconstruction algorithm may affect radiomic features, some features were characterized by high reproducibility through all testing parameters. The number of radiomic features that showed insensitivity to variations in segmentation methods, gray-level discretization, and reconstruction algorithms was 10 (13%), 4 (5%), and 1 (1%), respectively. These results suggest that a careful analysis of the effects of these parameters is essential prior to any radiomics clinical application. © 2017 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

A geospatial model of ambient sound pressure levels in the contiguous United States.

PubMed

Mennitt, Daniel; Sherrill, Kirk; Fristrup, Kurt

2014-05-01

This paper presents a model that predicts measured sound pressure levels using geospatial features such as topography, climate, hydrology, and anthropogenic activity. The model utilizes random forest, a tree-based machine learning algorithm, which does not incorporate a priori knowledge of source characteristics or propagation mechanics. The response data encompasses 270 000 h of acoustical measurements from 190 sites located in National Parks across the contiguous United States. The explanatory variables were derived from national geospatial data layers and cross validation procedures were used to evaluate model performance and identify variables with predictive power. Using the model, the effects of individual explanatory variables on sound pressure level were isolated and quantified to reveal systematic trends across environmental gradients. Model performance varies by the acoustical metric of interest; the seasonal L50 can be predicted with a median absolute deviation of approximately 3 dB. The primary application for this model is to generalize point measurements to maps expressing spatial variation in ambient sound levels. An example of this mapping capability is presented for Zion National Park and Cedar Breaks National Monument in southwestern Utah.

Splitting of the zero-energy Landau level and universal dissipative conductivity at critical points in disordered graphene.

PubMed

Ortmann, Frank; Roche, Stephan

2013-02-22

We report on robust features of the longitudinal conductivity (σ(xx)) of the graphene zero-energy Landau level in the presence of disorder and varying magnetic fields. By mixing an Anderson disorder potential with a low density of sublattice impurities, the transition from metallic to insulating states is theoretically explored as a function of Landau-level splitting, using highly efficient real-space methods to compute the Kubo conductivities (both σ(xx) and Hall σ(xy)). As long as valley degeneracy is maintained, the obtained critical conductivity σ(xx) =/~ 1.4e(2)/h is robust upon an increase in disorder (by almost 1 order of magnitude) and magnetic fields ranging from about 2 to 200 T. When the sublattice symmetry is broken, σ(xx) eventually vanishes at the Dirac point owing to localization effects, whereas the critical conductivities of pseudospin-split states (dictating the width of a σ(xy) = 0 plateau) change to σ(xx) =/~ e(2)/h, regardless of the splitting strength, superimposed disorder, or magnetic strength. These findings point towards the nondissipative nature of the quantum Hall effect in disordered graphene in the presence of Landau level splitting.

A ganglion-cell-based primary image representation method and its contribution to object recognition

NASA Astrophysics Data System (ADS)

Wei, Hui; Dai, Zhi-Long; Zuo, Qing-Song

2016-10-01

A visual stimulus is represented by the biological visual system at several levels: in the order from low to high levels, they are: photoreceptor cells, ganglion cells (GCs), lateral geniculate nucleus cells and visual cortical neurons. Retinal GCs at the early level need to represent raw data only once, but meet a wide number of diverse requests from different vision-based tasks. This means the information representation at this level is general and not task-specific. Neurobiological findings have attributed this universal adaptation to GCs' receptive field (RF) mechanisms. For the purposes of developing a highly efficient image representation method that can facilitate information processing and interpretation at later stages, here we design a computational model to simulate the GC's non-classical RF. This new image presentation method can extract major structural features from raw data, and is consistent with other statistical measures of the image. Based on the new representation, the performances of other state-of-the-art algorithms in contour detection and segmentation can be upgraded remarkably. This work concludes that applying sophisticated representation schema at early state is an efficient and promising strategy in visual information processing.

Earth Observations taken by the STS-109 crew

NASA Image and Video Library

2002-03-05

STS109-719-076 (1-12 March 2002) --- The astronauts on board the Space Shuttle Columbia took this 70mm picture featuring part of the eastern sea board. The oblique view looks northward from South Florida to the southern Appalachians. Most of the southeastern United States appears in crisp, clear air in the wake of a cold front that has pushed well off the mainland. Only a few jet stream and low-level clouds remain over South Florida and Gulf Stream.

[Food status peculiarities, anthropometric, clinica and biochemical indices at professional sportsmen].

PubMed

Gapparova, K M; Nikitiuk, D B; Zaĭnudinov, Z M; Tserekh, A A; Chekhonina, Iu G; Golubeva, A A; Sil'vestrova, G A; Rusakova, D S; Grigor'ian, O N

2011-01-01

Under steady state conditions in 66 athletes involved in weightlifting, bodybuilding, judo and taekwondo have studied features of the metabolic status. Data on matter-of-fact nutrition, body weight content within the inter-competition period, energy exchange, clinical and biochemical indices and physical acceptability indices were analyzed. As a result, the decrease indexes of metabolism at all the sportsmen and high-level caloric value at sportsmen who are engaged in weightlifting, which corresponds their energy expenditures, was revealed.

Buddy Tag CONOPS and Requirements.

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

Brotz, Jay Kristoffer; Deland, Sharon M.

2015-12-01

This document defines the concept of operations (CONOPS) and the requirements for the Buddy Tag, which is conceived and designed in collaboration between Sandia National Laboratories and Princeton University under the Department of State Key VerificationAssets Fund. The CONOPS describe how the tags are used to support verification of treaty limitations and is only defined to the extent necessary to support a tag design. The requirements define the necessary functions and desired non-functional features of the Buddy Tag at a high level

A study of renal blood flow regulation using the discrete wavelet transform

NASA Astrophysics Data System (ADS)

Pavlov, Alexey N.; Pavlova, Olga N.; Mosekilde, Erik; Sosnovtseva, Olga V.

2010-02-01

In this paper we provide a way to distinguish features of renal blood flow autoregulation mechanisms in normotensive and hypertensive rats based on the discrete wavelet transform. Using the variability of the wavelet coefficients we show distinctions that occur between the normal and pathological states. A reduction of this variability in hypertension is observed on the microscopic level of the blood flow in efferent arteriole of single nephrons. This reduction is probably associated with higher flexibility of healthy cardiovascular system.

Study on Properties of Energy Spectra of the Molecular Crystals

NASA Astrophysics Data System (ADS)

Pang, Xiao-Feng; Chen, Xiang-Rong

The energy-spectra of nonlinear vibration of molecular crystals such as acetanilide have been calculated by using discrete nonlinear Schrödinger equation appropriate to the systems, containing various interactions. The energy levels including higher excited states are basically consistent with experimental values obtained by infrared absorption and Raman scattering in acetanilide. We further give the features of distribution of the energy-spectra for the acetanilide. Using the energy spectra we also explained well experimental results obtained by Careri et al..

Inherent Structure versus Geometric Metric for State Space Discretization

PubMed Central

Liu, Hanzhong; Li, Minghai; Fan, Jue; Huo, Shuanghong

2016-01-01

Inherent structure (IS) and geometry-based clustering methods are commonly used for analyzing molecular dynamics trajectories. ISs are obtained by minimizing the sampled conformations into local minima on potential/effective energy surface. The conformations that are minimized into the same energy basin belong to one cluster. We investigate the influence of the applications of these two methods of trajectory decomposition on our understanding of the thermodynamics and kinetics of alanine tetrapeptide. We find that at the micro cluster level, the IS approach and root-mean-square deviation (RMSD) based clustering method give totally different results. Depending on the local features of energy landscape, the conformations with close RMSDs can be minimized into different minima, while the conformations with large RMSDs could be minimized into the same basin. However, the relaxation timescales calculated based on the transition matrices built from the micro clusters are similar. The discrepancy at the micro cluster level leads to different macro clusters. Although the dynamic models established through both clustering methods are validated approximately Markovian, the IS approach seems to give a meaningful state space discretization at the macro cluster level. PMID:26915811

Automatic classification of tissue malignancy for breast carcinoma diagnosis.

PubMed

Fondón, Irene; Sarmiento, Auxiliadora; García, Ana Isabel; Silvestre, María; Eloy, Catarina; Polónia, António; Aguiar, Paulo

2018-05-01

Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images. We provide four malignancy levels as the output of the system: normal, benign, in situ and invasive. The method is based on the calculation of three sets of features related to nuclei, colour regions and textures considering local characteristics and global image properties. By taking advantage of well-established image processing techniques, we build a feature vector for each image that serves as an input to an SVM (Support Vector Machine) classifier with a quadratic kernel. The method has been rigorously evaluated, first with a 5-fold cross-validation within an initial set of 120 images, second with an external set of 30 different images and third with images with artefacts included. Accuracy levels range from 75.8% when the 5-fold cross-validation was performed to 75% with the external set of new images and 61.11% when the extremely difficult images were added to the classification experiment. The experimental results indicate that the proposed method is capable of distinguishing between four malignancy levels with high accuracy. Our results are close to those obtained with recent deep learning-based methods. Moreover, it performs better than other state-of-the-art methods based on feature extraction, and it can help improve the CAD of breast cancer. Copyright © 2018 Elsevier Ltd. All rights reserved.

Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data.

PubMed

Li, Yue; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Yue Li; Jha, Devesh K; Ray, Asok; Wettergren, Thomas A; Wettergren, Thomas A; Li, Yue; Ray, Asok; Jha, Devesh K

2018-06-01

This paper presents information-theoretic performance analysis of passive sensor networks for detection of moving targets. The proposed method falls largely under the category of data-level information fusion in sensor networks. To this end, a measure of information contribution for sensors is formulated in a symbolic dynamics framework. The network information state is approximately represented as the largest principal component of the time series collected across the network. To quantify each sensor's contribution for generation of the information content, Markov machine models as well as x-Markov (pronounced as cross-Markov) machine models, conditioned on the network information state, are constructed; the difference between the conditional entropies of these machines is then treated as an approximate measure of information contribution by the respective sensors. The x-Markov models represent the conditional temporal statistics given the network information state. The proposed method has been validated on experimental data collected from a local area network of passive sensors for target detection, where the statistical characteristics of environmental disturbances are similar to those of the target signal in the sense of time scale and texture. A distinctive feature of the proposed algorithm is that the network decisions are independent of the behavior and identity of the individual sensors, which is desirable from computational perspectives. Results are presented to demonstrate the proposed method's efficacy to correctly identify the presence of a target with very low false-alarm rates. The performance of the underlying algorithm is compared with that of a recent data-driven, feature-level information fusion algorithm. It is shown that the proposed algorithm outperforms the other algorithm.

Inheritance rules for Hierarchical Metadata Based on ISO 19115

NASA Astrophysics Data System (ADS)

Zabala, A.; Masó, J.; Pons, X.

2012-04-01

Mainly, ISO19115 has been used to describe metadata for datasets and services. Furthermore, ISO19115 standard (as well as the new draft ISO19115-1) includes a conceptual model that allows to describe metadata at different levels of granularity structured in hierarchical levels, both in aggregated resources such as particularly series, datasets, and also in more disaggregated resources such as types of entities (feature type), types of attributes (attribute type), entities (feature instances) and attributes (attribute instances). In theory, to apply a complete metadata structure to all hierarchical levels of metadata, from the whole series to an individual feature attributes, is possible, but to store all metadata at all levels is completely impractical. An inheritance mechanism is needed to store each metadata and quality information at the optimum hierarchical level and to allow an ease and efficient documentation of metadata in both an Earth observation scenario such as a multi-satellite mission multiband imagery, as well as in a complex vector topographical map that includes several feature types separated in layers (e.g. administrative limits, contour lines, edification polygons, road lines, etc). Moreover, and due to the traditional split of maps in tiles due to map handling at detailed scales or due to the satellite characteristics, each of the previous thematic layers (e.g. 1:5000 roads for a country) or band (Landsat-5 TM cover of the Earth) are tiled on several parts (sheets or scenes respectively). According to hierarchy in ISO 19115, the definition of general metadata can be supplemented by spatially specific metadata that, when required, either inherits or overrides the general case (G.1.3). Annex H of this standard states that only metadata exceptions are defined at lower levels, so it is not necessary to generate the full registry of metadata for each level but to link particular values to the general value that they inherit. Conceptually the metadata registry is complete for each metadata hierarchical level, but at the implementation level most of the metadata elements are not stored at both levels but only at more generic one. This communication defines a metadata system that covers 4 levels, describes which metadata has to support series-layer inheritance and in which way, and how hierarchical levels are defined and stored. Metadata elements are classified according to the type of inheritance between products, series, tiles and the datasets. It explains the metadata elements classification and exemplifies it using core metadata elements. The communication also presents a metadata viewer and edition tool that uses the described model to propagate metadata elements and to show to the user a complete set of metadata for each level in a transparent way. This tool is integrated in the MiraMon GIS software.

Optimized unconventional superconductivity in a molecular Jahn-Teller metal

PubMed Central

Zadik, Ruth H.; Takabayashi, Yasuhiro; Klupp, Gyöngyi; Colman, Ross H.; Ganin, Alexey Y.; Potočnik, Anton; Jeglič, Peter; Arčon, Denis; Matus, Péter; Kamarás, Katalin; Kasahara, Yuichi; Iwasa, Yoshihiro; Fitch, Andrew N.; Ohishi, Yasuo; Garbarino, Gaston; Kato, Kenichi; Rosseinsky, Matthew J.; Prassides, Kosmas

2015-01-01

Understanding the relationship between the superconducting, the neighboring insulating, and the normal metallic state above Tc is a major challenge for all unconventional superconductors. The molecular A3C60 fulleride superconductors have a parent antiferromagnetic insulator in common with the atom-based cuprates, but here, the C603– electronic structure controls the geometry and spin state of the structural building unit via the on-molecule Jahn-Teller effect. We identify the Jahn-Teller metal as a fluctuating microscopically heterogeneous coexistence of both localized Jahn-Teller–active and itinerant electrons that connects the insulating and superconducting states of fullerides. The balance between these molecular and extended lattice features of the electrons at the Fermi level gives a dome-shaped variation of Tc with interfulleride separation, demonstrating molecular electronic structure control of superconductivity. PMID:26601168

Dynamics and Hall-edge-state mixing of localized electrons in a two-channel Mach-Zehnder interferometer

NASA Astrophysics Data System (ADS)

Bellentani, Laura; Beggi, Andrea; Bordone, Paolo; Bertoni, Andrea

2018-05-01

We present a numerical study of a multichannel electronic Mach-Zehnder interferometer, based on magnetically driven noninteracting edge states. The electron path is defined by a full-scale potential landscape on the two-dimensional electron gas at filling factor 2, assuming initially only the first Landau level as filled. We tailor the two beamsplitters with 50 % interchannel mixing and measure Aharonov-Bohm oscillations in the transmission probability of the second channel. We perform time-dependent simulations by solving the electron Schrödinger equation through a parallel implementation of the split-step Fourier method, and we describe the charge-carrier wave function as a Gaussian wave packet of edge states. We finally develop a simplified theoretical model to explain the features observed in the transmission probability, and we propose possible strategies to optimize gate performances.

Direct Electron Impact Excitation of Rydberg-Valence States of Molecular Nitrogen

NASA Astrophysics Data System (ADS)

Malone, C. P.; Johnson, P. V.; Liu, X.; Ajdari, B.; Muleady, S.; Kanik, I.; Khakoo, M. A.

2012-12-01

Collisions between electrons and neutral N2 molecules result in emissions that provide an important diagnostic probe for understanding the ionospheric energy balance and the effects of space weather in upper atmospheres. Also, transitions to singlet ungerade states cause N2 to be a strong absorber of solar radiation in the EUV spectral range where many ro-vibrational levels of these Rydberg-valence (RV) states are predissociative. Thus, their respective excitation and emission cross sections are important parameters for understanding the [N]/[N2] ratio in the thermosphere of nitrogen dominated atmospheres. The following work provides improved constraints on absolute and relative excitation cross sections of numerous RV states of N2, enabling more physically accurate atmospheric modeling. Here, we present recent integral cross sections (ICSs) for electron impact excitation of RV states of N2 [6], which were based on the differential cross sections (DCSs) derived from electron energy-loss (EEL) spectra of [5]. This work resulted in electronic excitation cross sections over the following measured vibrational levels: b 1Πu (v‧=0-14), c3 1Πu (v‧=0-3), o3 1Πu (v‧=0-3), b‧ 1Σu+ (v‧=0-10), c‧4 1Σu+ (v‧=0-3), G 3Πu (v‧=0-3), and F 3Πu (v‧=0-3). We further adjusted the cross sections of the RV states by extending the vibronic contributions to unmeasured v‧-levels via the relative excitation probabilities (REPs) as discussed in [6]. This resulted in REP-scaled ICSs over the following vibrational levels for the singlet ungerade states: b(0-19), c3(0-4), o3(0-4), b‧(0-16), and c‧4(0-8). Comparison of the ICSs of [6] with available EEL based measurements, theoretical calculations, and emission based work generally shows good agreement within error estimations, except with the recent reevaluation provided by [1]. Further, we have extended these results, using the recent EEL data of [3], to include the unfolding of better resolved features above ~13.82eV. This effort is to provide improved cross sections for these RV states, in particular for the b‧ 1Σu+ and c‧4 1Σu+ states, with inclusion of more upper vibrational levels. Future optical emission work should include re-measurements of excitation shape functions of the singlet ungerade states utilizing better spectral resolution than past determinations (e.g., [2,4]) to avoid uncertainties associated with unresolved and/or blended spectral features as well as J-dependent predissociation. Further development of theoretical treatments of N2 excitation is also in need. We will also present analysis of our new low-energy, near-threshold excitation cross sections for the valence states of N2, including a 1Πg (v‧) levels. Acknowledgement: This work was performed at CSUF and JPL, Caltech, under contract with NASA. We gratefully acknowledge financial support through NASA's OPR and PATM programs and NSF-PHY-RUI-0096808 & -0965793 and NSF-AGS-0938223. References: [1] Ajello, J. M., M. H. Stevens, I. Stewart, et al. (2007), GRL, 34, L24204 [2] Ajello, J. M., G. K. James, and B. O. Franklin (1989), PRA, 40, 3524-56 [3] Heays, A. N., B. R. Lewis, S. T. Gibson, et al. (2012), PRA, 85, 012705 [4] James, G. K., J. M. Ajello, B. Franklin, and D. E. Shemansky (1990), JPB, 23, 2055-81 [5] Khakoo, M. A., C. P. Malone, P. V. Johnson, et al. (2008), PRA, 77, 012704 [6] Malone, C. P., P. V. Johnson, X. Liu, et al. (2012), PRA, 85, 062704

The Cladistic Basis for the Phylogenetic Diversity (PD) Measure Links Evolutionary Features to Environmental Gradients and Supports Broad Applications of Microbial Ecology’s “Phylogenetic Beta Diversity” Framework

PubMed Central

Faith, Daniel P.; Lozupone, Catherine A.; Nipperess, David; Knight, Rob

2009-01-01

The PD measure of phylogenetic diversity interprets branch lengths cladistically to make inferences about feature diversity. PD calculations extend conventional species-level ecological indices to the features level. The “phylogenetic beta diversity” framework developed by microbial ecologists calculates PD-dissimilarities between community localities. Interpretation of these PD-dissimilarities at the feature level explains the framework’s success in producing ordinations revealing environmental gradients. An example gradients space using PD-dissimilarities illustrates how evolutionary features form unimodal response patterns to gradients. This features model supports new application of existing species-level methods that are robust to unimodal responses, plus novel applications relating to climate change, commercial products discovery, and community assembly. PMID:20087461

Experiences and meanings of integration of TCAM (Traditional, Complementary and Alternative Medical) providers in three Indian states: results from a cross-sectional, qualitative implementation research study.

PubMed

Nambiar, D; Narayan, V V; Josyula, L K; Porter, J D H; Sathyanarayana, T N; Sheikh, K

2014-11-25

Efforts to engage Traditional, Complementary and Alternative Medical (TCAM) practitioners in the public health workforce have growing relevance for India's path to universal health coverage. We used an action-centred framework to understand how policy prescriptions related to integration were being implemented in three distinct Indian states. Health departments and district-level primary care facilities in the states of Kerala, Meghalaya and Delhi. In each state, two or three districts were chosen that represented a variation in accessibility and distribution across TCAM providers (eg, small or large proportions of local health practitioners, Homoeopaths, Ayurvedic and/or Unani practitioners). Per district, two blocks or geographical units were selected. TCAM and allopathic practitioners, administrators and representatives of the community at the district and state levels were chosen based on publicly available records from state and municipal authorities. A total of 196 interviews were carried out: 74 in Kerala, and 61 each in Delhi and Meghalaya. We sought to understand experiences and meanings associated with integration across stakeholders, as well as barriers and facilitators to implementing policies related to integration of Traditional, Complementary and Alternative (TCA) providers at the systems level. We found that individual and interpersonal attributes tended to facilitate integration, while system features and processes tended to hinder it. Collegiality, recognition of stature, as well as exercise of individual personal initiative among TCA practitioners and of personal experience of TCAM among allopaths enabled integration. The system, on the other hand, was characterised by the fragmentation of jurisdiction and facilities, intersystem isolation, lack of trust in and awareness of TCA systems, and inadequate infrastructure and resources for TCA service delivery. State-tailored strategies that routinise interaction, reward individual and system-level individual integrative efforts, and are fostered by high-level political will are recommended. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Quantum correlation exists in any non-product state

PubMed Central

Guo, Yu; Wu, Shengjun

2014-01-01

Simultaneous existence of correlation in complementary bases is a fundamental feature of quantum correlation, and we show that this characteristic is present in any non-product bipartite state. We propose a measure via mutually unbiased bases to study this feature of quantum correlation, and compare it with other measures of quantum correlation for several families of bipartite states. PMID:25434458

New insights into turbulent pedestrian movement pattern in crowd-quakes

NASA Astrophysics Data System (ADS)

Ma, J.; Song, W. G.; Lo, S. M.; Fang, Z. M.

2013-02-01

Video recordings right before the Love Parade disaster have been quantitatively analyzed to explore the bursts of unusual crowd movement patterns, crowd-quakes. The pedestrian movement pattern in this incident was special for the reason that it happened in a congested counter flow scenario, where stopped pedestrians were involved. No one was believed to have pushed others intentionally at the beginning, however, under this situation, the body contacts among the pedestrians still induced a force spread, which then led to velocity fluctuation. As indicated by the individual velocity-related features, the densely crowded pedestrian movement displayed turbulent flow features. Further analyzing the overall flow field, we also found that the pedestrian flow field shared typical patterns with turbulent fluid flow. As a result of the turbulent state, different clusters of pedestrians displayed different velocity features. Thus crowd pressure which took into account the velocity and density information was proved to be a good indicator of crowd disasters. Based on these essential features of pedestrian crowd-quakes, a minimal model, i.e., a pedestrian crowd-quake model, was established. Effects including pedestrian gait, stress conservation level and personal intention to escape were explored.

Intralines of quasi-conical intersections on torsion planes: methylamine as a case study.

PubMed

Levi, C; Halász, G J; Vibók, A; Bar, I; Zeiri, Y; Kosloff, R; Baer, M

2009-06-18

Recently we reported on a novel feature associated with the intersection of the two lowest states (1)A' and (1)A'' of the methylamine (J. Chem. Phys. 2008, 128, 244302). We established the existence of a finite (closed) line of conical intersections (ci), namely, a finite seam, located in the HC-NHH symmetry plane, a line that is formed by moving a single hydrogen on that plane while locking the positions of the (six) other atoms. In the present article, this study is extended to the corresponding torsion planes formed by rotating the methyl group around the CN axis. The torsion planes, in contrast with the HC-NHH symmetry plane, do not satisfy the symmetry feature that enables the seam just mentioned. Nevertheless, the calculated nonadiabatic coupling terms (NACTs) resemble features similar to those encountered in the HC-NHH symmetry plane. Following a tedious numerical study supported by a theoretical model (Section III), it was verified that these NACTs may become similar to those on the symmetry plane, sometimes even to the level of almost no distinction, but lack one basic feature; namely, they are not singular and therefore do not form topological effects.

A novel probabilistic framework for event-based speech recognition

NASA Astrophysics Data System (ADS)

Juneja, Amit; Espy-Wilson, Carol

2003-10-01

One of the reasons for unsatisfactory performance of the state-of-the-art automatic speech recognition (ASR) systems is the inferior acoustic modeling of low-level acoustic-phonetic information in the speech signal. An acoustic-phonetic approach to ASR, on the other hand, explicitly targets linguistic information in the speech signal, but such a system for continuous speech recognition (CSR) is not known to exist. A probabilistic and statistical framework for CSR based on the idea of the representation of speech sounds by bundles of binary valued articulatory phonetic features is proposed. Multiple probabilistic sequences of linguistically motivated landmarks are obtained using binary classifiers of manner phonetic features-syllabic, sonorant and continuant-and the knowledge-based acoustic parameters (APs) that are acoustic correlates of those features. The landmarks are then used for the extraction of knowledge-based APs for source and place phonetic features and their binary classification. Probabilistic landmark sequences are constrained using manner class language models for isolated or connected word recognition. The proposed method could overcome the disadvantages encountered by the early acoustic-phonetic knowledge-based systems that led the ASR community to switch to systems highly dependent on statistical pattern analysis methods and probabilistic language or grammar models.

Vision-based localization of the center of mass of large space debris via statistical shape analysis

NASA Astrophysics Data System (ADS)

Biondi, G.; Mauro, S.; Pastorelli, S.

2017-08-01

The current overpopulation of artificial objects orbiting the Earth has increased the interest of the space agencies on planning missions for de-orbiting the largest inoperative satellites. Since this kind of operations involves the capture of the debris, the accurate knowledge of the position of their center of mass is a fundamental safety requirement. As ground observations are not sufficient to reach the required accuracy level, this information should be acquired in situ just before any contact between the chaser and the target. Some estimation methods in the literature rely on the usage of stereo cameras for tracking several features of the target surface. The actual positions of these features are estimated together with the location of the center of mass by state observers. The principal drawback of these methods is related to possible sudden disappearances of one or more features from the field of view of the cameras. An alternative method based on 3D Kinematic registration is presented in this paper. The method, which does not suffer of the mentioned drawback, considers a preliminary reduction of the inaccuracies in detecting features by the usage of statistical shape analysis.

Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric

NASA Astrophysics Data System (ADS)

He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Chen, Ying

2014-05-01

A multimodal biometric system has been considered a promising technique to overcome the defects of unimodal biometric systems. We have introduced a fusion scheme to gain a better understanding and fusion method for a face-iris-fingerprint multimodal biometric system. In our case, we use particle swarm optimization to train a set of adaptive Gabor filters in order to achieve the proper Gabor basic functions for each modality. For a closer analysis of texture information, two different local Gabor features for each modality are produced by the corresponding Gabor coefficients. Next, all matching scores of the two Gabor features for each modality are projected to a single-scalar score via a trained, supported, vector regression model for a final decision. A large-scale dataset is formed to validate the proposed scheme using the Facial Recognition Technology database-fafb and CASIA-V3-Interval together with FVC2004-DB2a datasets. The experimental results demonstrate that as well as achieving further powerful local Gabor features of multimodalities and obtaining better recognition performance by their fusion strategy, our architecture also outperforms some state-of-the-art individual methods and other fusion approaches for face-iris-fingerprint multimodal biometric systems.

Filter bank common spatial patterns in mental workload estimation.

PubMed

Arvaneh, Mahnaz; Umilta, Alberto; Robertson, Ian H

2015-01-01

EEG-based workload estimation technology provides a real time means of assessing mental workload. Such technology can effectively enhance the performance of the human-machine interaction and the learning process. When designing workload estimation algorithms, a crucial signal processing component is the feature extraction step. Despite several studies on this field, the spatial properties of the EEG signals were mostly neglected. Since EEG inherently has a poor spacial resolution, features extracted individually from each EEG channel may not be sufficiently efficient. This problem becomes more pronounced when we use low-cost but convenient EEG sensors with limited stability which is the case in practical scenarios. To address this issue, in this paper, we introduce a filter bank common spatial patterns algorithm combined with a feature selection method to extract spatio-spectral features discriminating different mental workload levels. To evaluate the proposed algorithm, we carry out a comparative analysis between two representative types of working memory tasks using data recorded from an Emotiv EPOC headset which is a mobile low-cost EEG recording device. The experimental results showed that the proposed spatial filtering algorithm outperformed the state-of-the algorithms in terms of the classification accuracy.

Inward open characterization of EmrD transporter with molecular dynamics simulation

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

Tan, Xianwei; Wang, Boxiong, E-mail: boxiong_wang@yahoo.com

EmrD is a member of the multidrug resistance exporter family. Up to now, little is known about the structural dynamics that underline the function of the EmrD protein in inward-facing open state and how the EmrD transits from an occluded state to an inward open state. For the first time the article applied the AT simulation to investigate the membrane transporter protein EmrD, and described the dynamic features of the whole protein, the domain, the helices, and the amino acid residues during an inward-open process from its occluded state. The gradual inward-open process is different from the current model ofmore » rigid-body domain motion in alternating-access mechanism. Simulation results show that the EmrD inward-open conformational fluctuation propagates from a C-terminal domain to an N-terminal domain via the linker region during the transition from its occluded state. The conformational fluctuation of the C-terminal domain is larger than that of the N-terminal domain. In addition, it is observed that the helices exposed to the surrounding membrane show a higher level of flexibility than the other regions, and the protonated E227 plays a key role in the transition from the occluded to the open state. -- Highlights: •This study described the dynamic features of the whole EmrD protein, during an inward-open process from its occluded state. •The EmrD inward-open conformational fluctuation propagates from a C-terminal domain to an N-terminal domain via the linker region during the transition from its occluded state. •The conformational fluctuation of the C-terminal domain is larger than that of the N-terminal domain. •The protonated E227 plays a key role in the transition from the occluded to the open state.« less

"Retweet to Pass the Blunt": Analyzing Geographic and Content Features of Cannabis-Related Tweeting Across the United States.

PubMed

Daniulaityte, Raminta; Lamy, Francois R; Smith, G Alan; Nahhas, Ramzi W; Carlson, Robert G; Thirunarayan, Krishnaprasad; Martins, Silvia S; Boyer, Edward W; Sheth, Amit

2017-11-01

Twitter data offer new possibilities for tracking health-related communications. This study is among the first to apply advanced information processing to identify geographic and content features of cannabis-related tweeting in the United States. Tweets were collected using streaming Application Programming Interface (March-May 2016) and were processed by eDrugTrends to identify geolocation and classify content by source (personal communication, media, retail) and sentiment (positive, negative, neutral). States were grouped by cannabis legalization policies into "recreational," "medical, less restrictive," "medical, more restrictive," and "illegal." Permutation tests were performed to analyze differences among four groups in adjusted percentages of all tweets, unique users, personal communications only, and positive-to-negative sentiment ratios. About 30% of all 13,233,837 cannabis-related tweets had identifiable state-level geo-information. Among geolocated tweets, 76.2% were personal communications, 21.1% media, and 2.7% retail. About 71% of personal communication tweets expressed positive sentiment toward cannabis; 16% expressed negative sentiment. States in the recreational group had significantly greater average adjusted percentage of cannabis tweets (3.01%) compared with other groups. For personal communication tweets only, the recreational group (2.47%) was significantly greater than the medical, more restrictive (1.84%) and illegal (1.85%) groups. Similarly, the recreational group had significantly greater average positive-to-negative sentiment ratio (4.64) compared with the medical, more restrictive (4.15) and illegal (4.19) groups. Average adjusted percentages of unique users showed similar differences between recreational and other groups. States with less restrictive policies displayed greater cannabis-related tweeting and conveyed more positive sentiment. The study demonstrates the potential of Twitter data to become a valuable indicator of drug-related communications in the context of varying policy environments.

Generalized global symmetries in states with dynamical defects: The case of the transverse sound in field theory and holography

NASA Astrophysics Data System (ADS)

Grozdanov, Sašo; Poovuttikul, Napat

2018-05-01

In this work, we show how states with conserved numbers of dynamical defects (strings, domain walls, etc.) can be understood as possessing generalized global symmetries even when the microscopic origins of these symmetries are unknown. Using this philosophy, we build an effective theory of a 2 +1 -dimensional fluid state with two perpendicular sets of immersed elastic line defects. When the number of defects is independently conserved in each set, then the state possesses two one-form symmetries. Normally, such viscoelastic states are described as fluids coupled to Goldstone bosons associated with spontaneous breaking of translational symmetry caused by the underlying microscopic structure—the principle feature of which is a transverse sound mode. At the linear, nondissipative level, we verify that our theory, based entirely on symmetry principles, is equivalent to a viscoelastic theory. We then build a simple holographic dual of such a state containing dynamical gravity and two two-form gauge fields, and use it to study its hydrodynamic and higher-energy spectral properties characterized by nonhydrodynamic, gapped modes. Based on the holographic analysis of transverse two-point functions, we study consistency between low-energy predictions of the bulk theory and the effective boundary theory. Various new features of the holographic dictionary are explained in theories with higher-form symmetries, such as the mixed-boundary-condition modification of the quasinormal mode prescription that depends on the running coupling of the boundary double-trace deformations. Furthermore, we examine details of low- and high-energy parts of the spectrum that depend on temperature, line defect densities and the renormalization group scale.

Appearance-based human gesture recognition using multimodal features for human computer interaction

NASA Astrophysics Data System (ADS)

Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

2011-03-01

The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

New analytical model for the ozone electronic ground state potential surface and accurate ab initio vibrational predictions at high energy range.

PubMed

Tyuterev, Vladimir G; Kochanov, Roman V; Tashkun, Sergey A; Holka, Filip; Szalay, Péter G

2013-10-07

An accurate description of the complicated shape of the potential energy surface (PES) and that of the highly excited vibration states is of crucial importance for various unsolved issues in the spectroscopy and dynamics of ozone and remains a challenge for the theory. In this work a new analytical representation is proposed for the PES of the ground electronic state of the ozone molecule in the range covering the main potential well and the transition state towards the dissociation. This model accounts for particular features specific to the ozone PES for large variations of nuclear displacements along the minimum energy path. The impact of the shape of the PES near the transition state (existence of the "reef structure") on vibration energy levels was studied for the first time. The major purpose of this work was to provide accurate theoretical predictions for ozone vibrational band centres at the energy range near the dissociation threshold, which would be helpful for understanding the very complicated high-resolution spectra and its analyses currently in progress. Extended ab initio electronic structure calculations were carried out enabling the determination of the parameters of a minimum energy path PES model resulting in a new set of theoretical vibrational levels of ozone. A comparison with recent high-resolution spectroscopic data on the vibrational levels gives the root-mean-square deviations below 1 cm(-1) for ozone band centres up to 90% of the dissociation energy. New ab initio vibrational predictions represent a significant improvement with respect to all previously available calculations.

Developing a Novel Measure of Body Satisfaction Using Virtual Reality

PubMed Central

Purvis, Clare K.; Jones, Megan; Bailey, Jakki O.; Bailenson, Jeremy; Taylor, C. Barr

2015-01-01

Body image disturbance (BID), considered a key feature in eating disorders, is a pervasive issue among young women. Accurate assessment of BID is critical, but the field is currently limited to self-report assessment methods. In the present study, we build upon existing research, and explore the utility of virtual reality (VR) to elicit and detect changes in BID across various immersive virtual environments. College-aged women with elevated weight and shape concerns (n = 38) and a non-weight and shape concerned control group (n = 40) were randomly exposed to four distinct virtual environments with high or low levels of body salience and social presence (i.e., presence of virtual others). Participants interacted with avatars of thin, normal weight, and overweight body size (BMI of approximately 18, 22, and 27 respectively) in virtual social settings (i.e., beach, party). We measured state-level body satisfaction (state BD) immediately after exposure to each environment. In addition, we measured participants’ minimum interpersonal distance, visual attention, and approach preference toward avatars of each size. Women with higher baseline BID reported significantly higher state BD in all settings compared to controls. Both groups reported significantly higher state BD in a beach with avatars as compared to other environments. In addition, women with elevated BID approached closer to normal weight avatars and looked longer at thin avatars compared to women in the control group. Our findings indicate that VR may serve as a novel tool for measuring state-level BID, with applications for measuring treatment outcomes. Implications for future research and clinical interventions are discussed. PMID:26469860

Affective State Level Recognition in Naturalistic Facial and Vocal Expressions.

PubMed

Meng, Hongying; Bianchi-Berthouze, Nadia

2014-03-01

Naturalistic affective expressions change at a rate much slower than the typical rate at which video or audio is recorded. This increases the probability that consecutive recorded instants of expressions represent the same affective content. In this paper, we exploit such a relationship to improve the recognition performance of continuous naturalistic affective expressions. Using datasets of naturalistic affective expressions (AVEC 2011 audio and video dataset, PAINFUL video dataset) continuously labeled over time and over different dimensions, we analyze the transitions between levels of those dimensions (e.g., transitions in pain intensity level). We use an information theory approach to show that the transitions occur very slowly and hence suggest modeling them as first-order Markov models. The dimension levels are considered to be the hidden states in the Hidden Markov Model (HMM) framework. Their discrete transition and emission matrices are trained by using the labels provided with the training set. The recognition problem is converted into a best path-finding problem to obtain the best hidden states sequence in HMMs. This is a key difference from previous use of HMMs as classifiers. Modeling of the transitions between dimension levels is integrated in a multistage approach, where the first level performs a mapping between the affective expression features and a soft decision value (e.g., an affective dimension level), and further classification stages are modeled as HMMs that refine that mapping by taking into account the temporal relationships between the output decision labels. The experimental results for each of the unimodal datasets show overall performance to be significantly above that of a standard classification system that does not take into account temporal relationships. In particular, the results on the AVEC 2011 audio dataset outperform all other systems presented at the international competition.

Tuned, driven, and active soft matter

NASA Astrophysics Data System (ADS)

Menzel, Andreas M.

2015-02-01

One characteristic feature of soft matter systems is their strong response to external stimuli. As a consequence they are comparatively easily driven out of their ground state and out of equilibrium, which leads to many of their fascinating properties. Here, we review illustrative examples. This review is structured by an increasing distance from the equilibrium ground state. On each level, examples of increasing degree of complexity are considered. In detail, we first consider systems that are quasi-statically tuned or switched to a new state by applying external fields. These are common liquid crystals, liquid crystalline elastomers, or ferrogels and magnetic elastomers. Next, we concentrate on systems steadily driven from outside e.g. by an imposed flow field. In our case, we review the reaction of nematic liquid crystals, of bulk-filling periodically modulated structures such as block copolymers, and of localized vesicular objects to an imposed shear flow. Finally, we focus on systems that are "active" and "self-driven". Here our range spans from idealized self-propelled point particles, via sterically interacting particles like granular hoppers, via microswimmers such as self-phoretically driven artificial Janus particles or biological microorganisms, via deformable self-propelled particles like droplets, up to the collective behavior of insects, fish, and birds. As we emphasize, similarities emerge in the features and behavior of systems that at first glance may not necessarily appear related. We thus hope that our overview will further stimulate the search for basic unifying principles underlying the physics of these soft materials out of their equilibrium ground state.

Ambulatory activity classification with dendogram-based support vector machine: Application in lower-limb active exoskeleton.

PubMed

Mazumder, Oishee; Kundu, Ananda Sankar; Lenka, Prasanna Kumar; Bhaumik, Subhasis

2016-10-01

Ambulatory activity classification is an active area of research for controlling and monitoring state initiation, termination, and transition in mobility assistive devices such as lower-limb exoskeletons. State transition of lower-limb exoskeletons reported thus far are achieved mostly through the use of manual switches or state machine-based logic. In this paper, we propose a postural activity classifier using a 'dendogram-based support vector machine' (DSVM) which can be used to control a lower-limb exoskeleton. A pressure sensor-based wearable insole and two six-axis inertial measurement units (IMU) have been used for recognising two static and seven dynamic postural activities: sit, stand, and sit-to-stand, stand-to-sit, level walk, fast walk, slope walk, stair ascent and stair descent. Most of the ambulatory activities are periodic in nature and have unique patterns of response. The proposed classification algorithm involves the recognition of activity patterns on the basis of the periodic shape of trajectories. Polynomial coefficients extracted from the hip angle trajectory and the centre-of-pressure (CoP) trajectory during an activity cycle are used as features to classify dynamic activities. The novelty of this paper lies in finding suitable instrumentation, developing post-processing techniques, and selecting shape-based features for ambulatory activity classification. The proposed activity classifier is used to identify the activity states of a lower-limb exoskeleton. The DSVM classifier algorithm achieved an overall classification accuracy of 95.2%. Copyright © 2016 Elsevier B.V. All rights reserved.

Pronounced and sustained central hypernoradrenergic function in major depression with melancholic features: relation to hypercortisolism and corticotropin-releasing hormone.

PubMed

Wong, M L; Kling, M A; Munson, P J; Listwak, S; Licinio, J; Prolo, P; Karp, B; McCutcheon, I E; Geracioti, T D; DeBellis, M D; Rice, K C; Goldstein, D S; Veldhuis, J D; Chrousos, G P; Oldfield, E H; McCann, S M; Gold, P W

2000-01-04

Both stress-system activation and melancholic depression are characterized by fear, constricted affect, stereotyped thinking, and similar changes in autonomic and neuroendocrine function. Because norepinephrine (NE) and corticotropin-releasing hormone (CRH) can produce these physiological and behavioral changes, we measured the cerebrospinal fluid (CSF) levels each hour for 30 consecutive hours in controls and in patients with melancholic depression. Plasma adrenocorticotropic hormone (ACTH) and cortisol levels were obtained every 30 min. Depressed patients had significantly higher CSF NE and plasma cortisol levels that were increased around the clock. Diurnal variations in CSF NE and plasma cortisol levels were virtually superimposable and positively correlated with each other in both patients and controls. Despite their hypercortisolism, depressed patients had normal levels of plasma ACTH and CSF CRH. However, plasma ACTH and CSF CRH levels in depressed patients were inappropriately high, considering the degree of their hypercortisolism. In contrast to the significant negative correlation between plasma cortisol and CSF CRH levels seen in controls, patients with depression showed no statistical relationship between these parameters. These data indicate that persistent stress-system dysfunction in melancholic depression is independent of the conscious stress of the disorder. These data also suggest mutually reinforcing bidirectional links between a central hypernoradrenergic state and the hyperfunctioning of specific central CRH pathways that each are driven and sustained by hypercortisolism. We postulate that alpha-noradrenergic blockade, CRH antagonists, and treatment with antiglucocorticoids may act at different loci, alone or in combination, in the treatment of major depression with melancholic features.

Remote sensing image denoising application by generalized morphological component analysis

NASA Astrophysics Data System (ADS)

Yu, Chong; Chen, Xiong

2014-12-01

In this paper, we introduced a remote sensing image denoising method based on generalized morphological component analysis (GMCA). This novel algorithm is the further extension of morphological component analysis (MCA) algorithm to the blind source separation framework. The iterative thresholding strategy adopted by GMCA algorithm firstly works on the most significant features in the image, and then progressively incorporates smaller features to finely tune the parameters of whole model. Mathematical analysis of the computational complexity of GMCA algorithm is provided. Several comparison experiments with state-of-the-art denoising algorithms are reported. In order to make quantitative assessment of algorithms in experiments, Peak Signal to Noise Ratio (PSNR) index and Structural Similarity (SSIM) index are calculated to assess the denoising effect from the gray-level fidelity aspect and the structure-level fidelity aspect, respectively. Quantitative analysis on experiment results, which is consistent with the visual effect illustrated by denoised images, has proven that the introduced GMCA algorithm possesses a marvelous remote sensing image denoising effectiveness and ability. It is even hard to distinguish the original noiseless image from the recovered image by adopting GMCA algorithm through visual effect.

A Collaborative Web-Based Approach to Planning Research, Integration, and Testing Using a Wiki

NASA Technical Reports Server (NTRS)

Delaney, Michael M.; Koshimoto, Edwin T.; Noble, Deleena; Duggan, Christopher

2010-01-01

The National Aeronautics and Space Administration Integrated Vehicle Health Management program touches on many different research areas while striving to enable the automated detection, diagnosis, prognosis, and mitigation of adverse events at the aircraft and system level. At the system level, the research focus is on the evaluation of multidisciplinary integrated methods, tools, and technologies for achieving the program goal. The participating program members form a diverse group of government, industry, and academic researchers. The program team developed the Research and Test Integration Plan in order to track significant test and evaluation activities, which are important for understanding, demonstrating, and communicating the overall project state and project direction. The Plan is a living document, which allows the project team the flexibility to construct conceptual test scenarios and to track project resources. The Plan also incorporates several desirable feature requirements for Plan users and maintainers. A wiki has proven to be the most efficient and effective means of implementing the feature requirements for the Plan. The wiki has proven very valuable as a research project management tool, and there are plans to expand its scope.

Using High-Dimensional Image Models to Perform Highly Undetectable Steganography

NASA Astrophysics Data System (ADS)

Pevný, Tomáš; Filler, Tomáš; Bas, Patrick

This paper presents a complete methodology for designing practical and highly-undetectable stegosystems for real digital media. The main design principle is to minimize a suitably-defined distortion by means of efficient coding algorithm. The distortion is defined as a weighted difference of extended state-of-the-art feature vectors already used in steganalysis. This allows us to "preserve" the model used by steganalyst and thus be undetectable even for large payloads. This framework can be efficiently implemented even when the dimensionality of the feature set used by the embedder is larger than 107. The high dimensional model is necessary to avoid known security weaknesses. Although high-dimensional models might be problem in steganalysis, we explain, why they are acceptable in steganography. As an example, we introduce HUGO, a new embedding algorithm for spatial-domain digital images and we contrast its performance with LSB matching. On the BOWS2 image database and in contrast with LSB matching, HUGO allows the embedder to hide 7× longer message with the same level of security level.

Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging : A spatial filtering approach.

PubMed

Subbaraju, Vigneshwaran; Suresh, Mahanand Belathur; Sundaram, Suresh; Narasimhan, Sundararajan

2017-01-01

This paper presents a new approach for detecting major differences in brain activities between Autism Spectrum Disorder (ASD) patients and neurotypical subjects using the resting state fMRI. Further the method also extracts discriminative features for an accurate diagnosis of ASD. The proposed approach determines a spatial filter that projects the covariance matrices of the Blood Oxygen Level Dependent (BOLD) time-series signals from both the ASD patients and neurotypical subjects in orthogonal directions such that they are highly separable. The inverse of this filter also provides a spatial pattern map within the brain that highlights those regions responsible for the distinguishable activities between the ASD patients and neurotypical subjects. For a better classification, highly discriminative log-variance features providing the maximum separation between the two classes are extracted from the projected BOLD time-series data. A detailed study has been carried out using the publicly available data from the Autism Brain Imaging Data Exchange (ABIDE) consortium for the different gender and age-groups. The study results indicate that for all the above categories, the regional differences in resting state activities are more commonly found in the right hemisphere compared to the left hemisphere of the brain. Among males, a clear shift in activities to the prefrontal cortex is observed for ASD patients while other parts of the brain show diminished activities compared to neurotypical subjects. Among females, such a clear shift is not evident; however, several regions, especially in the posterior and medial portions of the brain show diminished activities due to ASD. Finally, the classification performance obtained using the log-variance features is found to be better when compared to earlier studies in the literature. Copyright © 2016 Elsevier B.V. All rights reserved.

A Cortical Network for the Encoding of Object Change

PubMed Central

Hindy, Nicholas C.; Solomon, Sarah H.; Altmann, Gerry T.M.; Thompson-Schill, Sharon L.

2015-01-01

Understanding events often requires recognizing unique stimuli as alternative, mutually exclusive states of the same persisting object. Using fMRI, we examined the neural mechanisms underlying the representation of object states and object-state changes. We found that subjective ratings of visual dissimilarity between a depicted object and an unseen alternative state of that object predicted the corresponding multivoxel pattern dissimilarity in early visual cortex during an imagery task, while late visual cortex patterns tracked dissimilarity among distinct objects. Early visual cortex pattern dissimilarity for object states in turn predicted the level of activation in an area of left posterior ventrolateral prefrontal cortex (pVLPFC) most responsive to conflict in a separate Stroop color-word interference task, and an area of left ventral posterior parietal cortex (vPPC) implicated in the relational binding of semantic features. We suggest that when visualizing object states, representational content instantiated across early and late visual cortex is modulated by processes in left pVLPFC and left vPPC that support selection and binding, and ultimately event comprehension. PMID:24127425

Comparison of the epidemiologic features and patterns of initial care for prostate cancer between public and private institutions: a survey by the Brazilian Society of Urology.

PubMed

Nardi, Aguinaldo Cesar; Reis, Rodolfo Borges dos; Zequi, Stenio de Cassio; Nardozza, Archimedes

2012-01-01

To describe the epidemiological features and patterns of initial care for prostate cancer at public and private institutions in the State of Sao Paulo, Brazil. A total of 1,082 physicians affiliated to the Sao Paulo Section of the Brazilian Society of Urology were invited to participate in this cross-sectional, web-based survey. Between September 2004 and September 2005, participating urologists entered data on demographic, clinical and pathological characteristics of patients diagnosed with prostate cancer in their practice. Data on patients attended at public institutions were analyzed and compared with those patients attended at private practice. One hundred and ten society members contributed with data from 1915 patients, 1026 (53.6%) of whom from public institutions. When compared with patients attended at private institutions, those attended at public institutions were older and more likely to be black, had higher serum prostate specific antigen (PSA) levels, had a higher probability of being diagnosed with metastatic disease, but were less likely to undergo prostatectomy (all P < 0.001). In multivariate analysis, age, biopsy Gleason score, and being attended at a public institution were independently associated with metastatic disease upon diagnosis. The significant predictors of nonsurgical treatment were age, black race, and higher serum levels of PSA. A statewide registry provides valuable information regarding patient demographics, clinical features, and patterns of care. The results of this study suggest that significant disparities exist for patients with prostate cancer attended at different health-care systems. The relative contribution of biological versus socioeconomic features remains uncertain.

Interactive classification and content-based retrieval of tissue images

NASA Astrophysics Data System (ADS)

Aksoy, Selim; Marchisio, Giovanni B.; Tusk, Carsten; Koperski, Krzysztof

2002-11-01

We describe a system for interactive classification and retrieval of microscopic tissue images. Our system models tissues in pixel, region and image levels. Pixel level features are generated using unsupervised clustering of color and texture values. Region level features include shape information and statistics of pixel level feature values. Image level features include statistics and spatial relationships of regions. To reduce the gap between low-level features and high-level expert knowledge, we define the concept of prototype regions. The system learns the prototype regions in an image collection using model-based clustering and density estimation. Different tissue types are modeled using spatial relationships of these regions. Spatial relationships are represented by fuzzy membership functions. The system automatically selects significant relationships from training data and builds models which can also be updated using user relevance feedback. A Bayesian framework is used to classify tissues based on these models. Preliminary experiments show that the spatial relationship models we developed provide a flexible and powerful framework for classification and retrieval of tissue images.

Learning discriminative functional network features of schizophrenia

NASA Astrophysics Data System (ADS)

Gheiratmand, Mina; Rish, Irina; Cecchi, Guillermo; Brown, Matthew; Greiner, Russell; Bashivan, Pouya; Polosecki, Pablo; Dursun, Serdar

2017-03-01

Associating schizophrenia with disrupted functional connectivity is a central idea in schizophrenia research. However, identifying neuroimaging-based features that can serve as reliable "statistical biomarkers" of the disease remains a challenging open problem. We argue that generalization accuracy and stability of candidate features ("biomarkers") must be used as additional criteria on top of standard significance tests in order to discover more robust biomarkers. Generalization accuracy refers to the utility of biomarkers for making predictions about individuals, for example discriminating between patients and controls, in novel datasets. Feature stability refers to the reproducibility of the candidate features across different datasets. Here, we extracted functional connectivity network features from fMRI data at both high-resolution (voxel-level) and a spatially down-sampled lower-resolution ("supervoxel" level). At the supervoxel level, we used whole-brain network links, while at the voxel level, due to the intractably large number of features, we sampled a subset of them. We compared statistical significance, stability and discriminative utility of both feature types in a multi-site fMRI dataset, composed of schizophrenia patients and healthy controls. For both feature types, a considerable fraction of features showed significant differences between the two groups. Also, both feature types were similarly stable across multiple data subsets. However, the whole-brain supervoxel functional connectivity features showed a higher cross-validation classification accuracy of 78.7% vs. 72.4% for the voxel-level features. Cross-site variability and heterogeneity in the patient samples in the multi-site FBIRN dataset made the task more challenging compared to single-site studies. The use of the above methodology in combination with the fully data-driven approach using the whole brain information have the potential to shed light on "biomarker discovery" in schizophrenia.

Comparative analyses of quaternary arrangements in homo-oligomeric proteins in superfamilies: Functional implications.

PubMed

Sudha, Govindarajan; Srinivasan, Narayanaswamy

2016-09-01

A comprehensive analysis of the quaternary features of distantly related homo-oligomeric proteins is the focus of the current study. This study has been performed at the levels of quaternary state, symmetry, and quaternary structure. Quaternary state and quaternary structure refers to the number of subunits and spatial arrangements of subunits, respectively. Using a large dataset of available 3D structures of biologically relevant assemblies, we show that only 53% of the distantly related homo-oligomeric proteins have the same quaternary state. Considering these homologous homo-oligomers with the same quaternary state, conservation of quaternary structures is observed only in 38% of the pairs. In 36% of the pairs of distantly related homo-oligomers with different quaternary states the larger assembly in a pair shows high structural similarity with the entire quaternary structure of the related protein with lower quaternary state and it is referred as "Russian doll effect." The differences in quaternary state and structure have been suggested to contribute to the functional diversity. Detailed investigations show that even though the gross functions of many distantly related homo-oligomers are the same, finer level differences in molecular functions are manifested by differences in quaternary states and structures. Comparison of structures of biological assemblies in distantly and closely related homo-oligomeric proteins throughout the study differentiates the effects of sequence divergence on the quaternary structures and function. Knowledge inferred from this study can provide insights for improved protein structure classification and function prediction of homo-oligomers. Proteins 2016; 84:1190-1202. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Non-Adiabatic Effects on Excited States of Vinylidene Observed with Slow Photoelectron Velocity-Map Imaging.

PubMed

DeVine, Jessalyn A; Weichman, Marissa L; Zhou, Xueyao; Ma, Jianyi; Jiang, Bin; Guo, Hua; Neumark, Daniel M

2016-12-21

High-resolution slow photoelectron velocity-map imaging spectra of cryogenically cooled X̃ 2 B 2 H 2 CC - and D 2 CC - in the region of the vinylidene triplet excited states are reported. Three electronic bands are observed and, with the assistance of electronic structure calculations and quantum dynamics on ab initio-based near-equilibrium potential energy surfaces, are assigned as detachment to the [Formula: see text] 3 B 2 (T 1 ), b̃ 3 A 2 (T 2 ), and à 1 A 2 (S 1 ) excited states of neutral vinylidene. This work provides the first experimental observation of the à singlet excited state of H 2 CC. While regular vibrational structure is observed for the ã and à electronic bands, a number of irregular features are resolved in the vicinity of the b̃ band vibrational origin. High-level ab initio calculations suggest that this anomalous structure arises from a conical intersection between the ã and b̃ triplet states near the b̃ state minimum, which strongly perturbs the vibrational levels in the two electronic states through nonadiabatic coupling. Using the adiabatic electron affinity of H 2 CC previously measured to be 0.490(6) eV by Ervin and co-workers [J. Chem. Phys. 1989, 91, 5974], term energies for the excited neutral states of H 2 CC are found to be T 0 (ã 3 B 2 ) = 2.064(6), T 0 (b̃ 3 A 2 ) = 2.738(6), and T 0 (à 1 A 2 ) = 2.991(6) eV.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

PubMed

Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

2016-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. Copyright © 2015 Elsevier Inc. All rights reserved.

Taming The Polar Bear

DTIC Science & Technology

2014-04-01

Kennedy, “2012 State of the Climate : Arctiv Sea Ice” NOAA Climate.gov, 30 July 2013, http://www.climate.gov/news-features/understanding-climate/2012...Kennedy, Caitlin, “2012 State of the Climate : Arctiv Sea Ice” NOAA Climate.gov, 30 July 2013, http://www.climate.gov/news-features/understanding

Work stress of primary care physicians in the US, UK and German health care systems.

PubMed

Siegrist, Johannes; Shackelton, Rebecca; Link, Carol; Marceau, Lisa; von dem Knesebeck, Olaf; McKinlay, John

2010-07-01

Work-related stress among physicians has been an issue of growing concern in recent years. How and why this may vary between different health care systems remains poorly understood. Using an established theoretical model (effort-reward imbalance), this study analyses levels of work stress among primary care physicians (PCPs) in three different health care systems, the United States, the United Kingdom and Germany. Whether professional autonomy and specific features of the work environment are associated with work stress and account for possible country differences are examined. Data are derived from self-administered questionnaires obtained from 640 randomly sampled physicians recruited for an international comparative study of medical decision making conducted from 2005 to 2007. Results demonstrate country-specific differences in work stress with the highest level in Germany, intermediate level in the US and lowest level among UK physicians. A negative correlation between professional autonomy and work stress is observed in all three countries, but neither this association nor features of the work environment account for the observed country differences. Whether there will be adequate numbers of PCPs, or even a field of primary care in the future, is of increasing concern in several countries. To the extent that work-related stress contributes to this, identification of its organizational correlates in different health care systems may offer opportunities for remedial interventions. Copyright 2010 Elsevier Ltd. All rights reserved.

Work stress of primary care physicians in the US, UK and German health care systems

PubMed Central

Siegrist, Johannes; Link, Carol; Marceau, Lisa; von dem Knesebeck, Olaf; McKinlay, John

2010-01-01

Work-related stress among physicians has been an issue of growing concern in recent years. How and why this may vary between different health care systems remains poorly understood. Using an established theoretical model (effort-reward imbalance), this study analyses levels of work stress among primary care physicians (PCPs) in three different health care systems, the United States, the United Kingdom and Germany. Whether professional autonomy and specific features of the work environment are associated with work stress and account for possible country differences are examined. Data are derived from self-administered questionnaires obtained from 640 randomly sampled physicians recruited for an international comparative study of medical decision making conducted from 2005–2007. Results demonstrate country-specific differences in work stress- with the highest level in Germany, intermediate level in the US and lowest level among UK physicians. A negative correlation between professional autonomy and work stress is observed in all three countries, but neither this association nor features of the work environment account for the observed country differences. Whether there will be adequate numbers of PCPs, or even a field of primary care in the future, is of increasing concern in several countries. To the extent that work-related stress contributes to this, identification of its organizational correlates in different health care systems may offer opportunities for remedial interventions. PMID:20494505

The relationship between visual working memory and attention: retention of precise colour information in the absence of effects on perceptual selection

PubMed Central

Hollingworth, Andrew; Hwang, Seongmin

2013-01-01

We examined the conditions under which a feature value in visual working memory (VWM) recruits visual attention to matching stimuli. Previous work has suggested that VWM supports two qualitatively different states of representation: an active state that interacts with perceptual selection and a passive (or accessory) state that does not. An alternative hypothesis is that VWM supports a single form of representation, with the precision of feature memory controlling whether or not the representation interacts with perceptual selection. The results of three experiments supported the dual-state hypothesis. We established conditions under which participants retained a relatively precise representation of a parcticular colour. If the colour was immediately task relevant, it reliably recruited attention to matching stimuli. However, if the colour was not immediately task relevant, it failed to interact with perceptual selection. Feature maintenance in VWM is not necessarily equivalent with feature-based attentional selection. PMID:24018723

Nanoclusters as a new family of high temperature superconductors (Conference Presentation)

NASA Astrophysics Data System (ADS)

Halder, Avik; Kresin, Vitaly V.

2017-03-01

Electrons in metal clusters organize into quantum shells, akin to atomic shells in the periodic table. Such nanoparticles are referred to as "superatoms". The electronic shell levels are highly degenerate giving rise to sharp peaks in the density of states, which can enable exceptionally strong electron pairing in certain clusters containing tens to hundreds of atoms. A spectroscopic investigation of size - resolved aluminum nanoclusters has revealed a sharp rise in the density of states near the Fermi level as the temperature decreases towards 100 K. The effect is especially prominent in the closed-shell "magic" cluster Al66 [1, 2]. The characteristics of this behavior are fully consistent with a pairing transition, implying a high temperature superconducting state with Tc < 100K. This value exceeds that of bulk aluminum by two orders of magnitude. As a new class of high-temperature superconductors, such metal nanocluster particles are promising building blocks for high-Tc materials, devices, and networks. ---------- 1. Halder, A., Liang, A., Kresin, V. V. A novel feature in aluminum cluster photoionization spectra and possibility of electron pairing at T 100K. Nano Lett 15, 1410 - 1413 (2015) 2. Halder, A., Kresin, V. V. A transition in the density of states of metal "superatom" nanoclusters and evidence for superconducting pairing at T 100K. Phys. Rev. B 92, 214506 (2015).

Damage/fault diagnosis in an operating wind turbine under uncertainty via a vibration response Gaussian mixture random coefficient model based framework

NASA Astrophysics Data System (ADS)

Avendaño-Valencia, Luis David; Fassois, Spilios D.

2017-07-01

The study focuses on vibration response based health monitoring for an operating wind turbine, which features time-dependent dynamics under environmental and operational uncertainty. A Gaussian Mixture Model Random Coefficient (GMM-RC) model based Structural Health Monitoring framework postulated in a companion paper is adopted and assessed. The assessment is based on vibration response signals obtained from a simulated offshore 5 MW wind turbine. The non-stationarity in the vibration signals originates from the continually evolving, due to blade rotation, inertial properties, as well as the wind characteristics, while uncertainty is introduced by random variations of the wind speed within the range of 10-20 m/s. Monte Carlo simulations are performed using six distinct structural states, including the healthy state and five types of damage/fault in the tower, the blades, and the transmission, with each one of them characterized by four distinct levels. Random vibration response modeling and damage diagnosis are illustrated, along with pertinent comparisons with state-of-the-art diagnosis methods. The results demonstrate consistently good performance of the GMM-RC model based framework, offering significant performance improvements over state-of-the-art methods. Most damage types and levels are shown to be properly diagnosed using a single vibration sensor.

Tuning the Kondo effect in Yb(Fe 1-xCo x) 2Zn 20

DOE PAGES

Kong, Tai; Taufour, Valentin; Bud'ko, Sergey L.; ...

2017-04-03

We study the evolution of the Kondo effect in heavy fermion compounds, Yb(Fe 1-xCo x) 2Zn 20 (0 ≲ x ≲ 1), by means of temperature-dependent electric resistivity and speci c heat. The ground state of YbFe 2Zn 20 can be well described by a Kondo model with degeneracy N = 8 and a T K ~30 K. In the presence of a very similar total CEF splitting with YbFe 2Zn 20, the ground state of YbCo 2Zn 20 is close to a Kondo state with degeneracy N = 2 and a much lower TK ~ 2 K. Upon Comore » substitution, the coherence temperature of YbFe 2Zn 20 is suppressed, accompanied by an emerging Schottky-like feature in speci c heat associated with the thermal depopulation of CEF levels upon cooling. For 0.4 ≲ x ≲ 0.9, the ground state remains roughly the same which can be qualitatively understood by Kondo effect in the presence of CEF splitting. There is no clear indication of Kondo coherence observable in resistivity within this substitution range down to 500 mK. The coherence re-appears at around x≳ 0.9 and the coherence temperature increases with higher Co concentration levels.« less

Multi-domain electromagnetic absorption of triangular quantum rings

NASA Astrophysics Data System (ADS)

Sitek, Anna; Thorgilsson, Gunnar; Gudmundsson, Vidar; Manolescu, Andrei

2016-06-01

We present a theoretical study of the unielectronic energy spectra, electron localization, and optical absorption of triangular core-shell quantum rings. We show how these properties depend on geometric details of the triangle, such as side thickness or corners’ symmetry. For equilateral triangles, the lowest six energy states (including spin) are grouped in an energy shell, are localized only around corner areas, and are separated by a large energy gap from the states with higher energy which are localized on the sides of the triangle. The energy levels strongly depend on the aspect ratio of the triangle sides, i.e., thickness/length ratio, in such a way that the energy differences are not monotonous functions of this ratio. In particular, the energy gap between the group of states localized in corners and the states localized on the sides strongly decreases with increasing the side thickness, and then slightly increases for thicker samples. With increasing the thickness the low-energy shell remains distinct but the spatial distribution of these states spreads. The behavior of the energy levels and localization leads to a thickness-dependent absorption spectrum where one transition may be tuned in the THz domain and a second transition can be tuned from THz to the infrared range of electromagnetic spectrum. We show how these features may be further controlled with an external magnetic field. In this work the electron-electron Coulomb repulsion is neglected.

Multi-domain electromagnetic absorption of triangular quantum rings.

PubMed

Sitek, Anna; Thorgilsson, Gunnar; Gudmundsson, Vidar; Manolescu, Andrei

2016-06-03

We present a theoretical study of the unielectronic energy spectra, electron localization, and optical absorption of triangular core-shell quantum rings. We show how these properties depend on geometric details of the triangle, such as side thickness or corners' symmetry. For equilateral triangles, the lowest six energy states (including spin) are grouped in an energy shell, are localized only around corner areas, and are separated by a large energy gap from the states with higher energy which are localized on the sides of the triangle. The energy levels strongly depend on the aspect ratio of the triangle sides, i.e., thickness/length ratio, in such a way that the energy differences are not monotonous functions of this ratio. In particular, the energy gap between the group of states localized in corners and the states localized on the sides strongly decreases with increasing the side thickness, and then slightly increases for thicker samples. With increasing the thickness the low-energy shell remains distinct but the spatial distribution of these states spreads. The behavior of the energy levels and localization leads to a thickness-dependent absorption spectrum where one transition may be tuned in the THz domain and a second transition can be tuned from THz to the infrared range of electromagnetic spectrum. We show how these features may be further controlled with an external magnetic field. In this work the electron-electron Coulomb repulsion is neglected.

Multiradar tracking for theater missile defense

NASA Astrophysics Data System (ADS)

Sviestins, Egils

1995-09-01

A prototype system for tracking tactical ballistic missiles using multiple radars has been developed. The tracking is based on measurement level fusion (`true' multi-radar) tracking. Strobes from passive sensors can also be used. We describe various features of the system with some emphasis on the filtering technique. This is based on the Interacting Multiple Model framework where the states are Free Flight, Drag, Boost, and Auxiliary. Measurement error modeling includes the signal to noise ratio dependence; outliers and miscorrelations are handled in the same way. The launch point is calculated within one minute from the detection of the missile. The impact point, and its uncertainty region, is calculated continually by extrapolating the track state vector using the equations of planetary motion.

Microwave-induced three-photon coherence of Rydberg atomic states

NASA Astrophysics Data System (ADS)

Kwak, Hyo Min; Jeong, Taek; Lee, Yoon-Seok; Moon, Han Seb

2016-12-01

We investigate the three-photon coherence (TPC) effects of the Rydberg state in a Doppler-broadened four-level ladder-type atomic system for the 5S1/2(F=3)-5P3/2(F‧=4)-50D5/2-51P3/2 transition of 85Rb atoms. Upon interaction of the Rydberg Rb atom of the ladder-type electromagnetically induced transparency (EIT) scheme with a resonant microwave (MW) field, we numerically analyze the spectral features of the Rydberg TPC from two viewpoints, Autler-Townes splitting (AT-splitting) of the Rydberg EIT and three-photon electromagnetically induced absorption (TPEIA). We determine the criterion to differentiate between AT-splitting of the Rydberg EIT and TPEIA in the Doppler-broadened ladder-type atomic system.

How does the 'rest-self overlap' mediate the qualitative and automatic features of self-reference?

PubMed

Northoff, Georg

2016-01-01

The target article points out the qualitative and automatic features of self-reference while leaving open the underlying neural mechanisms. Based on empirical evidence about rest-self overlap and rest-stimulus interaction being special for self-related stimuli, I postulate that the resting state shows self-specific organization. The resting state's self-specific organization may be encoded by activity balances between different networks which in turn predispose the qualitative features of subsequent self-related stimulus-induced activity in, for instance, SAN as well as the automatic features of self-reference effects.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

PubMed Central

Steimer, Andreas; Schindler, Kaspar

2015-01-01

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation. PMID:26203657

Significance of beach geomorphology on fecal indicator bacteria levels.

PubMed

Donahue, Allison; Feng, Zhixuan; Kelly, Elizabeth; Reniers, Ad; Solo-Gabriele, Helena M

2017-08-15

Large databases of fecal indicator bacteria (FIB) measurements are available for coastal waters. With the assistance of satellite imagery, we illustrated the power of assessing data for many sites by evaluating beach features such as geomorphology, distance from rivers and canals, presence of piers and causeways, and degree of urbanization coupled with the enterococci FIB database for the state of Florida. We found that beach geomorphology was the primary characteristic associated with enterococci levels that exceeded regulatory guidelines. Beaches in close proximity to marshes or within bays had higher enterococci exceedances in comparison to open coast beaches. For open coast beaches, greater enterococci exceedances were associated with nearby rivers and higher levels of urbanization. Piers and causeways had a minimal contribution, as their effect was often overwhelmed by beach geomorphology. Results can be used to understand the potential causes of elevated enterococci levels and to promote public health. Copyright © 2017 Elsevier Ltd. All rights reserved.

Real-time acquisition and tracking system with multiple Kalman filters

NASA Astrophysics Data System (ADS)

Beard, Gary C.; McCarter, Timothy G.; Spodeck, Walter; Fletcher, James E.

1994-07-01

The design of a real-time, ground-based, infrared tracking system with proven field success in tracking boost vehicles through burnout is presented with emphasis on the software design. The system was originally developed to deliver relative angular positions during boost, and thrust termination time to a sensor fusion station in real-time. Autonomous target acquisition and angle-only tracking features were developed to ensure success under stressing conditions. A unique feature of the system is the incorporation of multiple copies of a Kalman filter tracking algorithm running in parallel in order to minimize run-time. The system is capable of updating the state vector for an object at measurement rates approaching 90 Hz. This paper will address the top-level software design, details of the algorithms employed, system performance history in the field, and possible future upgrades.

A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

PubMed Central

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

2016-01-01

Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values. PMID:27110235

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

PubMed

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

2016-01-01

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

Copernicus observational searches for OH and H2O in diffuse clouds

NASA Technical Reports Server (NTRS)

Smith, W. H.; Snow, T. P., Jr.

1983-01-01

An intensive search for OH and H2O in the directions of sigma Sco, alpha Cam, and micron Per was undertaken with the Copernicus satellite. Multiple scans were carried out over the wavelength region for the expected absorption features due to the OH D-X and H2O C-X transitions. The feature due to OH was detected marginally towards sigma Sco, and only an upper limit can be given towards alpha Cam. H2O was not detected in any of the stars at the signal level accumulated. The OH abundance towards sigma Sco and the respective lower limits for the OH/H2O ratios are discussed with regard to the extant models for the steady state abundances of OH and H2O, and shown not to be inconsistent with ion-molecule schemes.

Copernicus observational searches for OH and H2O in diffuse clouds

NASA Technical Reports Server (NTRS)

Smith, W. H.; Snow, T. P., Jr.

1979-01-01

An intensive search for OH and H2O in the directions of Sigma Sco, Alpha Cam, and Omicron Per was undertaken with the Copernicus satellite. Multiple scans were carried out over the wavelength region for the expected absorption features due to the OH D-X and H2O C-X transitions. The feature due to OH was possibly detected toward Sigma Sco, and only an upper limit can be given toward Alpha Cam. H2O was not detected in any of the stars at the signal level accumulated. The OH abundance toward Sigma Sco and the respective lower limits for the OH/H2O ratios are discussed with regard to the extant models for the steady-state abundances of OH and H2O, and shown not to be inconsistent with ion-molecule schemes.

An algorithm of adaptive scale object tracking in occlusion

NASA Astrophysics Data System (ADS)

Zhao, Congmei

2017-05-01

Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, there are still some problems in handling scale variations, object occlusion, fast motions and so on. In this paper, a multi-scale kernel correlation filter algorithm based on random fern detector was proposed. The tracking task was decomposed into the target scale estimation and the translation estimation. At the same time, the Color Names features and HOG features were fused in response level to further improve the overall tracking performance of the algorithm. In addition, an online random fern classifier was trained to re-obtain the target after the target was lost. By comparing with some algorithms such as KCF, DSST, TLD, MIL, CT and CSK, experimental results show that the proposed approach could estimate the object state accurately and handle the object occlusion effectively.

What to Build for Middle-Agers to Come? Attractive and Necessary Functions of Exercise-Promotion Mobile Phone Apps: A Cross-Sectional Study

PubMed Central

Chien, Yu-Tai; Chen, Yu-Jen; Hsiung, Hsiao-Fang; Chen, Hsiao-Jung; Hsieh, Meng-Hua; Wu, Wen-Jie

2017-01-01

Background Physical activity is important for middle-agers to maintain health both in middle age and in old age. Although thousands of exercise-promotion mobile phone apps are available for download, current literature offers little understanding regarding which design features can enhance middle-aged adults’ quality perception toward exercise-promotion apps and which factor may influence such perception. Objectives The aims of this study were to understand (1) which design features of exercise-promotion apps can enhance quality perception of middle-agers, (2) whether their needs are matched by current functions offered in app stores, and (3) whether physical activity (PA) and mobile phone self-efficacy (MPSE) influence quality perception. Methods A total of 105 middle-agers participated and filled out three scales: the International Physical Activity Questionnaire (IPAQ), the MPSE scale, and the need for design features questionnaire. The design features were developed based on the Coventry, Aberdeen, and London—Refined (CALO-RE) taxonomy. Following the Kano quality model, the need for design features questionnaire asked participants to classify design features into five categories: attractive, one-dimensional, must-be, indifferent, and reverse. The quality categorization was conducted based on a voting approach and the categorization results were compared with the findings of a prevalence study to realize whether needs match current availability. In total, 52 multinomial logistic regression models were analyzed to evaluate the effects of PA level and MPSE on quality perception of design features. Results The Kano analysis on the total sample revealed that visual demonstration of exercise instructions is the only attractive design feature, whereas the other 51 design features were perceived with indifference. Although examining quality perception by PA level, 21 features are recommended to low level, 6 features to medium level, but none to high-level PA. In contrast, high-level MPSE is recommended with 14 design features, medium level with 6 features, whereas low-level participants are recommended with 1 feature. The analysis suggests that the implementation of demanded features could be low, as the average prevalence of demanded design features is 20% (4.3/21). Surprisingly, social comparison and social support, most implemented features in current apps, were categorized into the indifferent category. The magnitude of effect is larger for MPSE because it effects quality perception of more design features than PA. Delving into the 52 regression models revealed that high MPSE more likely induces attractive or one- dimensional categorization, suggesting the importance of technological self-efficacy on eHealth care promotion. Conclusions This study is the first to propose middle-agers’ needs in relation to mobile phone exercise-promotion. In addition to the tailor-made recommendations, suggestions are offered to app designers to enhance the performance of persuasive features. An interesting finding on change of quality perception attributed to MPSE is proposed as future research. PMID:28546140

Theory of mind and Darwin's legacy.

PubMed

Searle, John

2013-06-18

We do not have an adequate theory of consciousness. Both dualism and materialism are mistaken because they deny consciousness is part of the physical world. False claims include (i) behaviorism, (ii) computationalism, (iii) epiphenomenalism, (iv) the readiness potential, (v) subjectivity, and (vi) materialism. Ontological subjectivity does not preclude epistemic objectivity. Observer relative phenomena are created by consciousness, but consciousness is not itself observer relative. Consciousness consists of feeling, sentience, or awareness with (i) qualitativeness, (ii) ontological subjectivity, (iii) unified conscious field, (iv) intentionality, and (v) intentional causation. All conscious states are caused by lower level neurobiological processes in the brain, and they are realized in the brain as higher level features. Efforts to get a detailed scientific account of how brain processes cause consciousness are disappointing. The Darwinian revolution gave us a new form of explanation; two levels were substituted: a causal level, where we specify the mechanism by which the phenotype functions, and a functional level, where we specify the selectional advantage that the phenotype provides. Sociobiology attempted to explain general features of human society, ethics, etc. It failed. For the incest taboo, it confuses inhibition with prohibition. It did not explain the moral force of the taboo. To explain the function of consciousness we cannot ask, "What would be subtracted if we subtracted consciousness but left everything else the same?" We cannot leave everything else the same because consciousness is necessary for higher functions of human and animal life. The unified conscious field gives the organism vastly increased power.

Theory of mind and Darwin’s legacy

PubMed Central

Searle, John

2013-01-01

We do not have an adequate theory of consciousness. Both dualism and materialism are mistaken because they deny consciousness is part of the physical world. False claims include (i) behaviorism, (ii) computationalism, (iii) epiphenomenalism, (iv) the readiness potential, (v) subjectivity, and (vi) materialism. Ontological subjectivity does not preclude epistemic objectivity. Observer relative phenomena are created by consciousness, but consciousness is not itself observer relative. Consciousness consists of feeling, sentience, or awareness with (i) qualitativeness, (ii) ontological subjectivity, (iii) unified conscious field, (iv) intentionality, and (v) intentional causation. All conscious states are caused by lower level neurobiological processes in the brain, and they are realized in the brain as higher level features. Efforts to get a detailed scientific account of how brain processes cause consciousness are disappointing. The Darwinian revolution gave us a new form of explanation; two levels were substituted: a causal level, where we specify the mechanism by which the phenotype functions, and a functional level, where we specify the selectional advantage that the phenotype provides. Sociobiology attempted to explain general features of human society, ethics, etc. It failed. For the incest taboo, it confuses inhibition with prohibition. It did not explain the moral force of the taboo. To explain the function of consciousness we cannot ask, “What would be subtracted if we subtracted consciousness but left everything else the same?” We cannot leave everything else the same because consciousness is necessary for higher functions of human and animal life. The unified conscious field gives the organism vastly increased power. PMID:23754416

Positive state observer for the automatic control of the depth of anesthesia-Clinical results.

PubMed

Nogueira, Filipa N; Mendonça, T; Rocha, P

2016-09-13

The depth of anesthesia (DoA) is a crucial feature in general anesthesia. Nowadays the DoA is usually evaluated by the bispectral index (BIS). According to the surgical procedure, different reference levels for the BIS may be clinically required. This can be achieved by the simultaneous administration of an analgesic (e.g. remifentanil) and an hypnotic (eg propofol). As a contribution to the effort of automating the processes of drug delivery in general anesthesia, in this paper, a positive state observer is designed for the implementation of a control scheme proposed for the automatic administration of propofol and of remifentanil, in order to track a desired level for the BIS. It is proved and illustrated by simulations that the controller-observer scheme has a very good performance. This scheme was implemented, tested and evaluated both by means of simulations and for a set of patients during surgical procedures. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends.

PubMed

Haller, Sven; Lovblad, Karl-Olof; Giannakopoulos, Panteleimon; Van De Ville, Dimitri

2014-05-01

Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.

Drive-train dynamics technology - State-of-the-art and design of a test facility for advanced development

NASA Technical Reports Server (NTRS)

Badgley, R. H.; Fleming, D. P.; Smalley, A. J.

1975-01-01

A program for the development and verification of drive-train dynamic technology is described along with its basis and the results expected from it. A central feature of this program is a drive-train test facility designed for the testing and development of advanced drive-train components, including shaft systems, dampers, and couplings. Previous efforts in designing flexible dynamic drive-train systems are reviewed, and the present state of the art is briefly summarized. The design of the test facility is discussed with major attention given to the formulation of the test-rig concept, dynamic scaling of model shafts, and the specification of design parameters. Specific efforts envisioned for the test facility are briefly noted, including evaluations of supercritical test shafts, stability thresholds for various sources and types of instabilities that can exist in shaft systems, effects of structural flexibility on the dynamic performance of dampers, and methods for vibration control in two-level and three-level flexible shaft systems.

An overview of game-based learning in building services engineering education

NASA Astrophysics Data System (ADS)

Alanne, Kari

2016-03-01

To ensure proper competence development and short graduation times for engineering students, it is essential that the study motivation is encouraged by new learning methods. In game-based learning, the learner's engagement is increased and learning is made meaningful by applying game-like features such as competition and rewarding through virtual promotions or achievement badges. In this paper, the state of the art of game-based learning in building services engineering education at university level is reviewed and discussed. A systematic literature review indicates that educational games have been reported in the field of related disciplines, such as mechanical and civil engineering. The development of system-level educational games that realistically simulate work life in building services engineering is still in its infancy. Novel rewarding practices and more comprehensive approaches entailing the state-of-the-art information tools such as building information modelling, geographic information systems, building management systems and augmented reality are needed in the future.

Borderline Personality Features, Anger, and Intimate Partner Violence: An Experimental Manipulation of Rejection.

PubMed

Armenti, Nicholas A; Babcock, Julia C

2018-04-01

Individuals with borderline personality features may be susceptible to react to situational stressors with negative and interpersonally maladaptive emotionality (e.g., anger) and aggression. The current study attempted to test two moderated mediation models to investigate dispositional risk factors associated with borderline personality features and intimate partner violence (IPV). Results from an experimental rejection induction paradigm were examined using moderated regression to observe contextual reactions to imagined romantic rejection from a current romantic partner among individuals with borderline personality features. An ethnically diverse sample of 218 undergraduates at a large public university in the southwestern United States was recruited. Participants responded to demographic questions and self-report measures, and engaged in an experimental rejection induction paradigm. Borderline personality features was positively associated with rejection sensitivity, physical assault, and psychological aggression. Contrary to initial hypotheses, rejection sensitivity did not serve as a mediator of the relations between borderline personality features and physical assault and psychological aggression. However, trait anger mediated the relation between borderline personality features and psychological aggression. As such, trait anger may be an important explanatory variable in the relation between borderline personality features and psychological aggression specifically. Results of the rejection induction paradigm indicated that, for individuals who were asked to imagine an ambiguous rejection, the relation between borderline personality features and state anger post-rejection was strengthened. For individuals who imagined a critical rejection, there was no significant relation between borderline personality features and state anger post-rejection. Findings suggest that trait anger may be an important dispositional factor in the link between borderline personality features and IPV. In addition, contextual factors, such as ambiguous rejection by an intimate partner, may be especially relevant in activating anger or aggression in individuals with borderline personality features.

Photoionization of the valence shells of the neutral tungsten atom

NASA Astrophysics Data System (ADS)

Ballance, C. P.; McLaughlin, B. M.

2015-04-01

Results from large-scale theoretical cross section calculations for the total photoionization (PI) of the 4f, 5s, 5p and 6s orbitals of the neutral tungsten atom using the Dirac Coulomb R-matrix approximation (DARC: Dirac-atomic R-matrix codes) are presented. Comparisons are made with previous theoretical methods and prior experimental measurements. In previous experiments a time-resolved dual laser approach was employed for the photo-absorption of metal vapours and photo-absorption measurements on tungsten in a solid, using synchrotron radiation. The lowest ground state level of neutral tungsten is 5{{p}6}5{{d}4}6{{s}2}{{ }5}{{D}J}, with J = 0, and requires only a single dipole matrix for PI. To make a meaningful comparison with existing experimental measurements, we statistically average the large-scale theoretical PI cross sections from the levels associated with the ground state 5{{p}6}5{{d}4}6{{s}2}{{ }5}{{D}J} (J = 0, 1, 2, 3, 4) levels and the 5{{d}5}6{{s} 7}{{S}3} excited metastable level. As the experiments have a self-evident metastable component in their ground state measurement, averaging over the initial levels allows for a more consistent and realistic comparison to be made. In the wider context, the absence of many detailed electron-impact excitation (EIE) experiments for tungsten and its multi-charged ion stages allows current PI measurements and theory to provide a road-map for future EIE, ionization and di-electronic cross section calculations by identifying the dominant resonance structure and features across an energy range of hundreds of eV.

Changing patterns of the long-term activity of the intermediate polar V1223 Sgr

NASA Astrophysics Data System (ADS)

Šimon, Vojtěch

2014-11-01

High and low states represent a large variety of features in the optical activity of some types of cataclysmic variables. Here, we examine them in the intermediate polar V1223 Sgr. We use the photographic observations from the Bamberg Observatory and from Garnavich and Szkody, and the CCD ASAS-3 and NSVS data. One plate or CCD image was usually obtained per night. These data span 78 years. We use the histograms of brightness to set the separation between the high and low states in V1223 Sgr at the level of 13.9 mag(B). The profile of this statistical distribution is dominated by a broad bump with the peak-to-peak amplitude of more than 1 mag(B). This is the result of co-adding various episodes of the high states, sometimes with the brightness varying on a timescale of months even during a single episode. The high state is not any uniquely defined level of brightness. Groups of the low states represent tails from such bumps rather than forming specific levels of brightness. The fuzzy boundaries of the bright side of the statistical distributions of brightness show that the system cannot find equilibrium of the mass transfer even in the high state. We show that V1223 Sgr underwent a large change of its high/low state activity; we ascribe it to the changes of the stellar activity of the donor. The episodes of the low states with mutually similar depth and profile were observed to recur with a cycle of 1092 days in the data mapping the years from 1999 to 2009; this behavior was discordant with the typical recurrence times present before. In our scenario, this cycle is driven by the changing aspect of some structure (e.g. a sequence of star spots migrating across the L1 point) on the donor. Because of the differential rotation of the donor, this configuration cyclically influences the mass flow to the accretion disk.

Tool Wear Feature Extraction Based on Hilbert Marginal Spectrum

NASA Astrophysics Data System (ADS)

Guan, Shan; Song, Weijie; Pang, Hongyang

2017-09-01

In the metal cutting process, the signal contains a wealth of tool wear state information. A tool wear signal’s analysis and feature extraction method based on Hilbert marginal spectrum is proposed. Firstly, the tool wear signal was decomposed by empirical mode decomposition algorithm and the intrinsic mode functions including the main information were screened out by the correlation coefficient and the variance contribution rate. Secondly, Hilbert transform was performed on the main intrinsic mode functions. Hilbert time-frequency spectrum and Hilbert marginal spectrum were obtained by Hilbert transform. Finally, Amplitude domain indexes were extracted on the basis of the Hilbert marginal spectrum and they structured recognition feature vector of tool wear state. The research results show that the extracted features can effectively characterize the different wear state of the tool, which provides a basis for monitoring tool wear condition.

An algorithm for analytical solution of basic problems featuring elastostatic bodies with cavities and surface flaws

NASA Astrophysics Data System (ADS)

Penkov, V. B.; Levina, L. V.; Novikova, O. S.; Shulmin, A. S.

2018-03-01

Herein we propose a methodology for structuring a full parametric analytical solution to problems featuring elastostatic media based on state-of-the-art computing facilities that support computerized algebra. The methodology includes: direct and reverse application of P-Theorem; methods of accounting for physical properties of media; accounting for variable geometrical parameters of bodies, parameters of boundary states, independent parameters of volume forces, and remote stress factors. An efficient tool to address the task is the sustainable method of boundary states originally designed for the purposes of computerized algebra and based on the isomorphism of Hilbertian spaces of internal states and boundary states of bodies. We performed full parametric solutions of basic problems featuring a ball with a nonconcentric spherical cavity, a ball with a near-surface flaw, and an unlimited medium with two spherical cavities.

Why did eukaryotes evolve only once? Genetic and energetic aspects of conflict and conflict mediation

PubMed Central

Blackstone, Neil W.

2013-01-01

According to multi-level theory, evolutionary transitions require mediating conflicts between lower-level units in favour of the higher-level unit. By this view, the origin of eukaryotes and the origin of multicellularity would seem largely equivalent. Yet, eukaryotes evolved only once in the history of life, whereas multicellular eukaryotes have evolved many times. Examining conflicts between evolutionary units and mechanisms that mediate these conflicts can illuminate these differences. Energy-converting endosymbionts that allow eukaryotes to transcend surface-to-volume constraints also can allocate energy into their own selfish replication. This principal conflict in the origin of eukaryotes can be mediated by genetic or energetic mechanisms. Genome transfer diminishes the heritable variation of the symbiont, but requires the de novo evolution of the protein-import apparatus and was opposed by selection for selfish symbionts. By contrast, metabolic signalling is a shared primitive feature of all cells. Redox state of the cytosol is an emergent feature that cannot be subverted by an individual symbiont. Hypothetical scenarios illustrate how metabolic regulation may have mediated the conflicts inherent at different stages in the origin of eukaryotes. Aspects of metabolic regulation may have subsequently been coopted from within-cell to between-cell pathways, allowing multicellularity to emerge repeatedly. PMID:23754817

Why did eukaryotes evolve only once? Genetic and energetic aspects of conflict and conflict mediation.

PubMed

Blackstone, Neil W

2013-07-19

According to multi-level theory, evolutionary transitions require mediating conflicts between lower-level units in favour of the higher-level unit. By this view, the origin of eukaryotes and the origin of multicellularity would seem largely equivalent. Yet, eukaryotes evolved only once in the history of life, whereas multicellular eukaryotes have evolved many times. Examining conflicts between evolutionary units and mechanisms that mediate these conflicts can illuminate these differences. Energy-converting endosymbionts that allow eukaryotes to transcend surface-to-volume constraints also can allocate energy into their own selfish replication. This principal conflict in the origin of eukaryotes can be mediated by genetic or energetic mechanisms. Genome transfer diminishes the heritable variation of the symbiont, but requires the de novo evolution of the protein-import apparatus and was opposed by selection for selfish symbionts. By contrast, metabolic signalling is a shared primitive feature of all cells. Redox state of the cytosol is an emergent feature that cannot be subverted by an individual symbiont. Hypothetical scenarios illustrate how metabolic regulation may have mediated the conflicts inherent at different stages in the origin of eukaryotes. Aspects of metabolic regulation may have subsequently been coopted from within-cell to between-cell pathways, allowing multicellularity to emerge repeatedly.

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

NASA Astrophysics Data System (ADS)

Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-01

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

PubMed

Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-22

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

An adiabatic spectroscopic investigation of the CsRb system in ground and numerous excited states

NASA Astrophysics Data System (ADS)

Souissi, Hanen; Jellali, Soulef; Maha, Chaieb; Habli, Héla; Oujia, Brahim; Gadéa, Florent Xavier

2017-10-01

Via ab-initio approximations, we investigate the electronic and structural features of the CsRb molecule. Adiabatic potential energy curves of 261,3Σ+, 181,3Π and 61,3Δ electronic states with their derived spectroscopic constants as well as vibrational levels spacing have been carried out and well explained. Our approach is founded on an Effective Core Potential (ECP) describing the valence electrons of the system. Using a large Gaussian basis set, the full valence Configuration Interaction can be applied easily on the two-effective valence electrons of the CsRb system. Furthermore, a detailed analysis of the electric dipolar properties has been made through the investigation of both permanent and transition dipole moments (PDM and TDM). It is significant that the ionic character connected with electron transfer that is linked to Cs+ Rb- state has been clearly illustrated in the adiabatic permanent dipole moment.

The molecular mechanism of thermal noise in rod photoreceptors.

PubMed

Gozem, Samer; Schapiro, Igor; Ferré, Nicolas; Olivucci, Massimo

2012-09-07

Spontaneous electrical signals in the retina's photoreceptors impose a limit on visual sensitivity. Their origin is attributed to a thermal, rather than photochemical, activation of the transduction cascade. Although the mechanism of such a process is under debate, the observation of a relationship between the maximum absorption wavelength (λ(max)) and the thermal activation kinetic constant (k) of different visual pigments (the Barlow correlation) indicates that the thermal and photochemical activations are related. Here we show that a quantum chemical model of the bovine rod pigment provides a molecular-level understanding of the Barlow correlation. The transition state mediating thermal activation has the same electronic structure as the photoreceptor excited state, thus creating a direct link between λ(max) and k. Such a link appears to be the manifestation of intrinsic chromophore features associated with the existence of a conical intersection between its ground and excited states.

Equation of state for shock compression of distended solids

NASA Astrophysics Data System (ADS)

Grady, Dennis; Fenton, Gregg; Vogler, Tracy

2014-05-01

Shock Hugoniot data for full-density and porous compounds of boron carbide, silicon dioxide, tantalum pentoxide, uranium dioxide and playa alluvium are investigated for the purpose of equation-of-state representation of intense shock compression. Complications of multivalued Hugoniot behavior characteristic of highly distended solids are addressed through the application of enthalpy-based equations of state of the form originally proposed by Rice and Walsh in the late 1950's. Additive measures of cold and thermal pressure intrinsic to the Mie-Gruneisen EOS framework is replaced by isobaric additive functions of the cold and thermal specific volume components in the enthalpy-based formulation. Additionally, experimental evidence reveals enhancement of shock-induced phase transformation on the Hugoniot with increasing levels of initial distension for silicon dioxide, uranium dioxide and possibly boron carbide. Methods for addressing this experimentally observed feature of the shock compression are incorporated into the EOS model.

Quantum-optical nonlinearities induced by Rydberg-Rydberg interactions: A perturbative approach

NASA Astrophysics Data System (ADS)

Grankin, A.; Brion, E.; Bimbard, E.; Boddeda, R.; Usmani, I.; Ourjoumtsev, A.; Grangier, P.

2015-10-01

In this article, we theoretically study the quantum statistical properties of the light transmitted through or reflected from an optical cavity, filled by an atomic medium with strong optical nonlinearity induced by Rydberg-Rydberg van der Waals interactions. Atoms are driven on a two-photon transition from their ground state to a Rydberg level via an intermediate state by the combination of a weak signal field and a strong control beam. By using a perturbative approach, we get analytic results which remain valid in the regime of weak feeding fields, even when the intermediate state becomes resonant thus generalizing our previous results. We can thus investigate quantitatively new features associated with the resonant behavior of the system. We also propose an effective nonlinear three-boson model of the system which, in addition to leading to the same analytic results as the original problem, sheds light on the physical processes at work in the system.

Rotational band on a three-quasineutron isomer in 127Xe

NASA Astrophysics Data System (ADS)

Chakraborty, S.; Sharma, H. P.; Tiwary, S. S.; Majumder, C.; Banerjee, P.; Ganguly, S.; Rai, S.; Pragati, Modi, Swati; Arumugam, P.; Mayank, Kumar, S.; Palit, R.; Kumar, A.; Bhattacharjee, S. S.; Singh, R. P.; Muralithar, S.

2018-05-01

Excited states in 127Xe were populated via 122Sn(9Be,4 n γ ) fusion-evaporation reaction at Ebeam=48 MeV. A rotational band above an isomeric state at 2730.3 keV has been established. Jπ=23 /2+ has been assigned unambiguously to the bandhead. Structural features, configuration, and reduced hindrance factor per degree of K -forbiddenness (fν) have been discussed in the context of neighboring nuclei. Half-lives of 7 /2+ (t1 /2=37 ±1 ns) and 23 /2+ (t1 /2=28 ±1 ns) isomeric states have also been estimated and found to be consistent with the earlier reported values. The experimentally observed energy levels of the rotational band have been reproduced successfully by theoretical calculations carried out using the modified particle rotor model (MPRM) with β2˜0.22 and 15∘≲γ ≲22∘ .

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF

PubMed Central

Ali, Nouman; Bajwa, Khalid Bashir; Sablatnig, Robert; Chatzichristofis, Savvas A.; Iqbal, Zeshan; Rashid, Muhammad; Habib, Hafiz Adnan

2016-01-01

With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration. PMID:27315101

Reduced-Reference Quality Assessment Based on the Entropy of DWT Coefficients of Locally Weighted Gradient Magnitudes.

PubMed

Golestaneh, S Alireza; Karam, Lina

2016-08-24

Perceptual image quality assessment (IQA) attempts to use computational models to estimate the image quality in accordance with subjective evaluations. Reduced-reference (RR) image quality assessment (IQA) methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images. Finding a balance between the number of RR features and accuracy of the estimated image quality is essential and important in IQA. In this paper we propose a training-free low-cost RRIQA method that requires a very small number of RR features (6 RR features). The proposed RRIQA algorithm is based on the discrete wavelet transform (DWT) of locally weighted gradient magnitudes.We apply human visual system's contrast sensitivity and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. The RR features are computed by measuring the entropy of each DWT subband, for each scale, and pooling the subband entropies along all orientations, resulting in L RR features (one average entropy per scale) for an L-level DWT. Extensive experiments performed on seven large-scale benchmark databases demonstrate that the proposed RRIQA method delivers highly competitive performance as compared to the state-of-the-art RRIQA models as well as full reference ones for both natural and texture images. The MATLAB source code of REDLOG and the evaluation results are publicly available online at https://http://lab.engineering.asu.edu/ivulab/software/redlog/.

Local Environment Sensitivity of the Cu K-Edge XANES Features in Cu-SSZ-13: Analysis from First-Principles.

PubMed

Zhang, Renqin; McEwen, Jean-Sabin

2018-05-22

Cu K-edge X-ray absorption near-edge spectra (XANES) have been widely used to study the properties of Cu-SSZ-13. In this Letter, the sensitivity of the XANES features to the local environment for a Cu + cation with a linear configuration and a Cu 2+ cation with a square-linear configuration in Cu-SSZ-13 is reported. When a Cu + cation is bonded to H 2 O or NH 3 in a linear configuration, the XANES has a strong peak at around 8983 eV. The intensity of this peak decreases as the linear configuration is broken. As for the Cu 2+ cations in a square-planar configuration with a coordination number of 4, two peaks at around 8986 and 8993 eV are found. An intensity decrease for both peaks at around 8986 and 8993 eV is found in an NH 3 _4_Z 2 Cu model as the N-Cu-N angle changes from 180 to 100°. We correlate these features to the variation of the 4p state by PDOS analysis. In addition, the feature peaks for both the Cu + cation and Cu 2+ cation do not show a dependence on the Cu-N bond length. We further show that the feature peaks also change when the coordination number of the Cu cation is varied, while these feature peaks are independent of the zeolite topology. These findings help elucidate the experimental XANES features at an atomic and an electronic level.

The National Atlas of the United States now on the Web and in print

USGS Publications Warehouse

Hutchinson, John A.

2004-01-01

The National Atlas of the United States of America® was published in 1970 as a book, with more than 400 pages and 765 maps. Since then, many people have called for a new edition, and many maps have been published as single sheets using the classic National Atlas 1:7,500,000-scale format. Work began in 1997 on a new, web-based edition of the National Atlas of the United States®. Accessible at http://nationalatlas.gov, the new atlas features an interactive mapmaker with more than 1,000 data layers. Developed as a coordinated package of dynamic webbased map products and services, and printed and printable maps for selected themes, the National Atlas of the United States of America® has grown beyond a book. Yet, the cartographer’s fundamental job remains the same as it was in 1970—to translate national-level geographic data into an understandable view of the nation.

The KMO allele encoding Arg452 is associated with psychotic features in bipolar disorder type 1, and with increased CSF KYNA level and reduced KMO expression.

PubMed

Lavebratt, C; Olsson, S; Backlund, L; Frisén, L; Sellgren, C; Priebe, L; Nikamo, P; Träskman-Bendz, L; Cichon, S; Vawter, M P; Osby, U; Engberg, G; Landén, M; Erhardt, S; Schalling, M

2014-03-01

The kynurenine pathway metabolite kynurenic acid (KYNA), modulating glutamatergic and cholinergic neurotransmission, is increased in cerebrospinal fluid (CSF) of patients with schizophrenia or bipolar disorder type 1 with psychotic features. KYNA production is critically dependent on kynurenine 3-monooxygenase (KMO). KMO mRNA levels and activity in prefrontal cortex (PFC) are reduced in schizophrenia. We hypothesized that KMO expression in PFC would be reduced in bipolar disorder with psychotic features and that a functional genetic variant of KMO would associate with this disease, CSF KYNA level and KMO expression. KMO mRNA levels were reduced in PFC of bipolar disorder patients with lifetime psychotic features (P=0.005, n=19) or schizophrenia (P=0.02, n=36) compared with nonpsychotic patients and controls. KMO genetic association to psychotic features in bipolar disorder type 1 was studied in 493 patients and 1044 controls from Sweden. The KMO Arg(452) allele was associated with psychotic features during manic episodes (P=0.003). KMO Arg(452) was studied for association to CSF KYNA levels in an independent sample of 55 Swedish patients, and to KMO expression in 717 lymphoblastoid cell lines and 138 hippocampal biopsies. KMO Arg(452) associated with increased levels of CSF KYNA (P=0.03) and reduced lymphoblastoid and hippocampal KMO expression (P≤0.05). Thus, findings from five independent cohorts suggest that genetic variation in KMO influences the risk for psychotic features in mania of bipolar disorder patients. This provides a possible mechanism for the previous findings of elevated CSF KYNA levels in those bipolar patients with lifetime psychotic features and positive association between KYNA levels and number of manic episodes.

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

PubMed

Chen, Hao; Dou, Qi; Yu, Lequan; Qin, Jing; Heng, Pheng-Ann

2018-04-15

Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem. The main merit of residual learning is that it can alleviate the degradation problem when training a deep network so that the performance gains achieved by increasing the network depth can be fully leveraged. With this technique, our VoxResNet is built with 25 layers, and hence can generate more representative features to deal with the large variations of brain tissues than its rivals using hand-crafted features or shallower networks. In order to effectively train such a deep network with limited training data for brain segmentation, we seamlessly integrate multi-modality and multi-level contextual information into our network, so that the complementary information of different modalities can be harnessed and features of different scales can be exploited. Furthermore, an auto-context version of the VoxResNet is proposed by combining the low-level image appearance features, implicit shape information, and high-level context together for further improving the segmentation performance. Extensive experiments on the well-known benchmark (i.e., MRBrainS) of brain segmentation from 3D magnetic resonance (MR) images corroborated the efficacy of the proposed VoxResNet. Our method achieved the first place in the challenge out of 37 competitors including several state-of-the-art brain segmentation methods. Our method is inherently general and can be readily applied as a powerful tool to many brain-related studies, where accurate segmentation of brain structures is critical. Copyright © 2017 Elsevier Inc. All rights reserved.

Near-death states reported in a sample of 50 misusers.

PubMed

Corazza, Ornella; Schifano, Fabrizio

2010-05-01

Increase in recreational ketamine use may be a cause for concern. We aimed here at assessing, in a sample of ketamine misusers, concordance between the typical near-death experience (NDE) features and the on-drug psychoactive effects the subjects experienced. In 2003-2005, a sample of previous ketamine misusers recollecting a ketamine-related NDE were recruited through snowballing and screened with the means of the Greyson NDE Scale; 125 participants made an initial contact with the researcher and 50 reported a minimum score of seven at the "Greyson NDE Scale". Interviewees were in the range 21-66 years old; 27 participants (54%) were educated at BA level, 18 (36%) had an MSc, and 5 (10%) a PhD. Eight (16%) interviewees had a definite religious background. An average lifetime ketamine intake of 140 occasions was reported by the interviewees, who typically presented with a polydrug, including cannabis and MDMA/ecstasy, misuse history. In 45 (90%) cases, the NDE occurred during the first few occasions of intake. Most frequent features of reported NDE states included: altered perception of time (90%), strong sense of detaching from own physical body (88%), and a sense of peace/joy (76% of subjects). Although results here described were elicited from a self-selected, nonrandomized, limited size sample of misusers, we suggest that recreational ketamine intake may be associated with occurrence of near-death related states.

What makes virtual agents believable?

NASA Astrophysics Data System (ADS)

Bogdanovych, Anton; Trescak, Tomas; Simoff, Simeon

2016-01-01

In this paper we investigate the concept of believability and make an attempt to isolate individual characteristics (features) that contribute to making virtual characters believable. As the result of this investigation we have produced a formalisation of believability and based on this formalisation built a computational framework focused on simulation of believable virtual agents that possess the identified features. In order to test whether the identified features are, in fact, responsible for agents being perceived as more believable, we have conducted a user study. In this study we tested user reactions towards the virtual characters that were created for a simulation of aboriginal inhabitants of a particular area of Sydney, Australia in 1770 A.D. The participants of our user study were exposed to short simulated scenes, in which virtual agents performed some behaviour in two different ways (while possessing a certain aspect of believability vs. not possessing it). The results of the study indicate that virtual agents that appear resource bounded, are aware of their environment, own interaction capabilities and their state in the world, agents that can adapt to changes in the environment and exist in correct social context are those that are being perceived as more believable. Further in the paper we discuss these and other believability features and provide a quantitative analysis of the level of contribution for each such feature to the overall perceived believability of a virtual agent.

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study

PubMed Central

2012-01-01

Background There is a need for automated methods to learn general features of the interactions of a ligand class with its diverse set of protein receptors. An appropriate machine learning approach is Inductive Logic Programming (ILP), which automatically generates comprehensible rules in addition to prediction. The development of ILP systems which can learn rules of the complexity required for studies on protein structure remains a challenge. In this work we use a new ILP system, ProGolem, and demonstrate its performance on learning features of hexose-protein interactions. Results The rules induced by ProGolem detect interactions mediated by aromatics and by planar-polar residues, in addition to less common features such as the aromatic sandwich. The rules also reveal a previously unreported dependency for residues cys and leu. They also specify interactions involving aromatic and hydrogen bonding residues. This paper shows that Inductive Logic Programming implemented in ProGolem can derive rules giving structural features of protein/ligand interactions. Several of these rules are consistent with descriptions in the literature. Conclusions In addition to confirming literature results, ProGolem’s model has a 10-fold cross-validated predictive accuracy that is superior, at the 95% confidence level, to another ILP system previously used to study protein/hexose interactions and is comparable with state-of-the-art statistical learners. PMID:22783946

Learning Compact Binary Face Descriptor for Face Recognition.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Xiuzhuang; Zhou, Jie

2015-10-01

Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.

The influence of structural stigma and rejection sensitivity on young sexual minority men's daily tobacco and alcohol use

PubMed Central

Pachankis, John E.; Hatzenbuehler, Mark L.; Starks, Tyrel J.

2018-01-01

Stigma occurs at both individual and structural levels, but existing research tends to examine the effect of individual and structural forms of stigma in isolation, rather than considering potential synergistic effects. To address this gap, our study examined whether stigma at the individual level, namely gay-related rejection sensitivity, interacts with structural stigma to predict substance use among young sexual minority men. Sexual minority (n = 119) participants completed online measures of our constructs (e.g., rejection sensitivity). Participants currently resided across a broad array of geographic areas (i.e., 24 U.S. states), and had attended high school in 28 states, allowing us to capture sufficient variance in current and past forms of structural stigma, defined as (1) a lack of state-level policies providing equal opportunities for heterosexual and sexual minority individuals and (2) negative state-aggregated attitudes toward sexual minorities. To measure daily substance use, we utilized a daily diary approach, whereby all participants were asked to indicate whether they used tobacco or alcohol on nine consecutive days. Results indicated that structural stigma interacted with rejection sensitivity to predict tobacco and alcohol use, and that this relationship depended on the developmental timing of exposure to structural stigma. In contrast, rejection sensitivity did not mediate the relationship between structural stigma and substance use. These results suggest that psychological predispositions, such as rejection sensitivity, interact with features of the social environment, such as structural stigma, to predict important health behaviors among young sexual minority men. These results add to a growing body of research documenting the multiple levels through which stigma interacts to produce negative health outcomes among sexual minority individuals. PMID:24507912

Temporal BYY encoding, Markovian state spaces, and space dimension determination.

PubMed

Xu, Lei

2004-09-01

As a complementary to those temporal coding approaches of the current major stream, this paper aims at the Markovian state space temporal models from the perspective of the temporal Bayesian Ying-Yang (BYY) learning with both new insights and new results on not only the discrete state featured Hidden Markov model and extensions but also the continuous state featured linear state spaces and extensions, especially with a new learning mechanism that makes selection of the state number or the dimension of state space either automatically during adaptive learning or subsequently after learning via model selection criteria obtained from this mechanism. Experiments are demonstrated to show how the proposed approach works.

Nature of the high-binding-energy dip in the low-temperature photoemission spectra of Bi2Sr2CaCu2O8+δ

NASA Astrophysics Data System (ADS)

Dessau, D. S.; Shen, Z.-X.; Wells, B. O.; King, D. M.; Spicer, W. E.; Arko, A. J.; Lombardo, L. W.; Mitzi, D. B.; Kapitulnik, A.

1992-03-01

At the transition to superconductivity, an anomalous high-binding-energy (~=-90 meV) dip appears in the low-temperature photoemission spectra taken along the Γ-M¯ high-symmetry direction of Bi2Sr2CaCu2O8+δ. This paper details experiments which further characterize the energy and k-space dependence of this dip structure. The dip occurs over a wide portion of the Γ-M¯ zone diagonal (110), yet shows minimal energy dispersion. In the spectra taken along the Γ-X zone edge (100), the dip is very weak or not present. We show that these results imply that the dip is not an artifact dependent on the experiment or special features of the band structure and therefore is an intrinsic feature of the superconducting state of Bi2Sr2CaCu2O8+δ. The behavior of the normal-state bands along Γ-M¯ in relation to the local-density-approximation prediction of a Bi-O-based electron ``pocket'' is also discussed, with our data explained most naturally if the Bi-O band remains above the Fermi level for all k.

Alexithymia in anorexia nervosa: the mediating role of depression.

PubMed

Torres, Sandra; Guerra, Marina Prista; Lencastre, Leonor; Miller, Kylee; Vieira, Filipa Mucha; Roma-Torres, António; Brandão, Isabel; Costa, Patrício

2015-01-30

The role of depression in the expression of alexithymia in anorexia nervosa (AN) has been controversially explained and several variables that may mask or increase the presence of emotional difficulties have scant examination in previous studies. This study aims to analyze the associations between alexithymia and state variables, such as age, BMI, illness duration, treatment duration, and medication status in AN participants, and to test the mediating role of depression in emotional difficulties. The Toronto Alexithymia Scale (TAS-20) and the Zung Self-Rating Depression Scale were administrated to 160 females: 80 participants with AN and 80 healthy controls. High levels of alexithymia were not a function of state variables. The mediating role of depression differed by the alexithymia dimension, with total mediation found for the TAS-DDF and partial mediation found for the TAS-DIF. Alexithymia is a relevant feature throughout the spectrum of AN and does not seem to be related to developmental maturation and some clinical features. Depression is probably the variable that best accounts for the variance in alexithymia, but is not a complete explanation for the known cognitive-affective disturbances in AN. Specific emotional competencies require scrutiny during psychiatric treatment. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Nonlinear spectroscopy of trapped ions

NASA Astrophysics Data System (ADS)

Schlawin, Frank; Gessner, Manuel; Mukamel, Shaul; Buchleitner, Andreas

2014-08-01

Nonlinear spectroscopy employs a series of laser pulses to interrogate dynamics in large interacting many-body systems, and it has become a highly successful method for experiments in chemical physics. Current quantum optical experiments approach system sizes and levels of complexity that require the development of efficient techniques to assess spectral and dynamical features with scalable experimental overhead. However, established methods from optical spectroscopy of macroscopic ensembles cannot be applied straightforwardly to few-atom systems. Based on the ideas proposed in M. Gessner et al., (arXiv:1312.3365), we develop a diagrammatic approach to construct nonlinear measurement protocols for controlled quantum systems, and we discuss experimental implementations with trapped ion technology in detail. These methods, in combination with distinct features of ultracold-matter systems, allow us to monitor and analyze excitation dynamics in both the electronic and vibrational degrees of freedom. They are independent of system size, and they can therefore reliably probe systems in which, e.g., quantum state tomography becomes prohibitively expensive. We propose signals that can probe steady-state currents, detect the influence of anharmonicities on phonon transport, and identify signatures of chaotic dynamics near a quantum phase transition in an Ising-type spin chain.

A multiple distributed representation method based on neural network for biomedical event extraction.

PubMed

Wang, Anran; Wang, Jian; Lin, Hongfei; Zhang, Jianhai; Yang, Zhihao; Xu, Kan

2017-12-20

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.

Effect of temperature and magnetic field on disorder in semiconductor structures

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

Agrinskaya, N. V., E-mail: nina.agrins@mail.ioffe.ru; Kozub, V. I.

We present the results of consistent theoretical analysis of various factors that may lead to influence of temperature and external magnetic field on disorder in semiconductor structures. Main attention is paid to quantum well (QW) structures in which only QWs or both QW and barriers are doped (the doping level is assumed to be close to the value corresponding to the metal–insulator transition). The above factors include (i) ionization of localized states to the region of delocalized states above the mobility edge, which is presumed to exist in the impurity band; (ii) the coexistence in the upper and lower Hubbardmore » bands (upon doping of QWs as well as barriers); in this case, in particular, the external magnetic field determines the relative contribution of the upper Hubbard band due to spin correlations at doubly filled sites; and (iii) the contribution of the exchange interaction at pairs of sites, in which the external magnetic field can affect the relation between ferromagnetic and antiferromagnetic configurations. All these factors, which affect the structure and degree of disorder, lead to specific features in the temperature dependence of resistivity and determine specific features of the magnetoresistance. Our conclusions are compared with available experimental data.« less

The 3D Elevation Program: summary for Missouri

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Landau levels and magnetic oscillations in gapped Dirac materials with intrinsic Rashba interaction

NASA Astrophysics Data System (ADS)

Tsaran, V. Yu.; Sharapov, S. G.

2014-11-01

A new family of the low-buckled Dirac materials which includes silicene, germanene, etc. is expected to possess a more complicated sequence of Landau levels than in pristine graphene. Their energies depend, among other factors, on the strength of the intrinsic spin-orbit (SO) and Rashba SO couplings and can be tuned by an applied electric field Ez. We studied the influence of the intrinsic Rashba SO term on the energies of Landau levels using both analytical and numerical methods. The quantum magnetic oscillations of the density of states are also investigated. A specific feature of the oscillations is the presence of the beats with the frequency proportional to the field Ez. The frequency of the beats becomes also dependent on the carrier concentration when Rashba interaction is present allowing experimental determination of its strength.

The 3D Elevation Program: summary for Montana

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The new 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Louisiana

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Tennessee

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for New York

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

3D Elevation Program: summary for Vermont

USGS Publications Warehouse

Carswell, William J.

2015-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Maryland

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 ifsar data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

The 3D Elevation Program: summary for Ohio

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation's natural and constructed features.

The 3D Elevation Program: summary for Indiana

USGS Publications Warehouse

Carswell, William J.

2014-01-01

The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation's natural and constructed features.