Home Literacy Environment and Head Start Children's Language Development: The Role of Approaches to Learning

ERIC Educational Resources Information Center

Meng, Christine

2015-01-01

Research Findings: This study examined whether approaches to learning moderate the association between home literacy environment and English receptive vocabulary development. The Head Start Family and Child Experiences Survey (2003 cohort) was used for analysis. Latent growth curve modeling was utilized to test a quadratic model of English…

Nurses and opioids: results of a bi-national survey on mental models regarding opioid administration in hospitals.

PubMed

Guest, Charlotte; Sobotka, Fabian; Karavasopoulou, Athina; Ward, Stephen; Bantel, Carsten

2017-01-01

Pain remains insufficiently treated in hospitals. Increasing evidence suggests human factors contribute to this, due to nurses failing to administer opioids. This behavior might be the consequence of nurses' mental models about opioids. As personal experience and conceptions shape these models, the aim of this prospective survey was to identify model-influencing factors. A questionnaire was developed comprising of 14 statements concerning ideations about opioids and seven questions concerning demographics, indicators of adult learning, and strength of religious beliefs. Latent variables that may underlie nurses' mental models were identified using undirected graphical dependence models. Representative items of latent variables were employed for ordinal regression analysis. Questionnaires were distributed to 1,379 nurses in two London, UK, hospitals (n=580) and one German (n=799) hospital between September 2014 and February 2015. A total of 511 (37.1%) questionnaires were returned. Mean (standard deviation) age of participants were 37 (11) years; 83.5% participants were female; 45.2% worked in critical care; and 51.5% had more than 10 years experience. Of the nurses, 84% were not scared of opioids, 87% did not regard opioids as drugs to help patients die, and 72% did not view them as drugs of abuse. More English (41%) than German (28%) nurses were afraid of criminal investigations and were constantly aware of side effects (UK, 94%; Germany, 38%) when using opioids. Four latent variables were identified which likely influence nurses' mental models: "conscious decision-making"; "medication-related fears"; "practice-based observations"; and "risk assessment". They were predicted by strength of religious beliefs and indicators of informal learning such as experience but not by indicators of formal learning such as conference attendance. Nurses in both countries employ analytical and affective mental models when administering the opioids and seem to learn from experience rather than from formal teaching. Additionally, some attitudes and emotions towards opioids are likely the result of nurses' cultural background.

A Latent Profile Analysis and Structural Equation Modeling of the Instructional Quality of Mathematics Classrooms Based on the PISA 2012 Results of Korea and Singapore

ERIC Educational Resources Information Center

Yi, Hyun Sook; Lee, Yuree

2017-01-01

Teachers' classroom behaviors and their effects on student learning have received significant attention from educators, because the quality of instruction is a critical factor closely tied to students' learning experiences. Based on a theoretical model conceptualizing the quality of instruction, this study examined the characteristics of…

At the Interface between Language Testing and Second Language Acquisition: Language Ability and Context of Learning

ERIC Educational Resources Information Center

Gu, Lin

2014-01-01

This study investigated the relationship between latent components of academic English language ability and test takers' study-abroad and classroom learning experiences through a structural equation modeling approach in the context of TOEFL iBT® testing. Data from the TOEFL iBT public dataset were used. The results showed that test takers'…

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

PubMed

Yamazaki, Keisuke

2015-09-01

Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

School is out on noisy reefs: the effect of boat noise on predator learning and survival of juvenile coral reef fishes.

PubMed

Ferrari, Maud C O; McCormick, Mark I; Meekan, Mark G; Simpson, Stephen D; Nedelec, Sophie L; Chivers, Douglas P

2018-01-31

Noise produced by anthropogenic activities is increasing in many marine ecosystems. We investigated the effect of playback of boat noise on fish cognition. We focused on noise from small motorboats, since its occurrence can dominate soundscapes in coastal communities, the number of noise-producing vessels is increasing rapidly and their proximity to marine life has the potential to cause deleterious effects. Cognition-or the ability of individuals to learn and remember information-is crucial, given that most species rely on learning to achieve fitness-promoting tasks, such as finding food, choosing mates and recognizing predators. The caveat with cognition is its latent effect: the individual that fails to learn an important piece of information will live normally until the moment where it needs the information to make a fitness-related decision. Such latent effects can easily be overlooked by traditional risk assessment methods. Here, we conducted three experiments to assess the effect of boat noise playbacks on the ability of fish to learn to recognize predation threats, using a common, conserved learning paradigm. We found that fish that were trained to recognize a novel predator while being exposed to 'reef + boat noise' playbacks failed to subsequently respond to the predator, while their 'reef noise' counterparts responded appropriately. We repeated the training, giving the fish three opportunities to learn three common reef predators, and released the fish in the wild. Those trained in the presence of 'reef + boat noise' playbacks survived 40% less than the 'reef noise' controls over our 72 h monitoring period, a performance equal to that of predator-naive fish. Our last experiment indicated that these results were likely due to failed learning, as opposed to stress effects from the sound exposure. Neither playbacks nor real boat noise affected survival in the absence of predator training. Our results indicate that boat noise has the potential to cause latent effects on learning long after the stressor has gone. © 2018 The Author(s).

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

PubMed Central

Lee, Seong-Whan

2014-01-01

Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis. PMID:24363140

The role of test context in latent inhibition of conditioned inhibition: Part of a search for general principles of associative interference.

PubMed

Miguez, Gonzalo; Soares, Julia S; Miller, Ralph R

2015-09-01

In two lick suppression experiments with rats, we assessed interference with behavior indicative of conditioned inhibition by a latent inhibition treatment as a function of test context. We asked what effect the test context has, given identical latent inhibition treatments in Phase 1 and identical conditioned inhibition trainings in Phase 2. In Experiment 1, an AAA versus AAB context-shift design determined that the latent inhibition treatment in Phase 1 attenuated behavior indicative of the conditioned inhibition training administered in Phase 2, regardless of the test context, which could reflect a failure to either acquire or express conditioned inhibition. In Experiment 2, an ABA versus ABB design showed that test performance in Contexts A and B reflected the treatments that had been administered in those contexts (i.e., conditioned inhibition was observed in Context B but not A), which could reflect either the context specificity of either latent inhibition or conditioned inhibition. In either case, latent inhibition of conditioned inhibition training in at least some situations was seen to reflect an expression deficit rather than an acquisition deficit. These data, in conjunction with prior reports, suggest that latent inhibition is relatively specific to the context in which it was administered, whereas conditioned inhibition is specific to its training context only when it is the second-learned relationship concerning the target cue. These experiments are part of a larger effort to delineate control by the test context of two-phase associative interference, as a function of the nature of target training and the nature of interference training.

Enhancing effects of lithium on memory are not by-products of learning or attentional deficits.

PubMed

Tsaltas, Eleftheria; Kyriazi, Theodora; Poulopoulou, Cornelia; Kontis, Dimitrios; Maillis, Antonios

2007-06-18

We recently reported that chronic lithium (LiCl), at therapeutic plasma levels, enhanced spatial working memory and retention of an aversive contingency. Here we examine the possibility that these effects be secondary to LiCl effects on the ability to ignore irrelevant stimuli or on fear conditioning. In Experiment 1, rats subjected to >30 daily intraperitoneal injections of LiCl (2mmol/kg) or saline underwent conditioned emotional response training (CER: 2 CS pairings with 1-s, 1-mA shock) after 40 pre-exposures either to the CS (latent inhibition-LiCl/latent inhibition-saline, n=8) or to another stimulus (control-LiCl/control-saline, n=8). In Experiment 2, eight LiCl and eight saline animals were trained in on-the-baseline (VI-60s) CER (1-s, 0.15-mA shock in CS-signalled periods) in the Skinner box. In Experiment 1, LiCl animals showed normal latent inhibition. In both experiments, their fear conditioning was unimpaired. Therefore, the previously reported memory improvement under chronic lithium cannot be attributed to changes in the ability to ignore irrelevant stimuli or in fear conditioning.

A context-specific latent inhibition effect in a human conditioned suppression task.

PubMed

Byron Nelson, James; del Carmen Sanjuan, Maria

2006-06-01

Three studies used a computer video game preparation to demonstrate latent inhibition in adult humans. In all studies participants fired torpedoes at a target spaceship by clicking the mouse. Conditioned stimuli (CSs) were presented in the form of coloured "sensors" at the bottom of the screen. Conditioning was conducted by pairing a sensor with an attack from the target spaceship. Participants learned to suppress their rate of mouse clicking in preparation for an attack. In Experiment 1 a total of 10 preexposures to the sensor CS, prior to conditioning, retarded acquisition of suppression. In Experiment 2 the effect of preexposure was shown to be context specific. Experiment 3 showed little generalization of the preexposure effect from one sensor CS to another. Experiment 3 also showed that preexposure did not make the sensor CS inhibitory. Comparisons with conditioned suppression procedures with animals and negative-priming procedures are briefly discussed.

Learned Vector-Space Models for Document Retrieval.

ERIC Educational Resources Information Center

Caid, William R.; And Others

1995-01-01

The Latent Semantic Indexing and MatchPlus systems examine similar contexts in which words appear and create representational models that capture the similarity of meaning of terms and then use the representation for retrieval. Text Retrieval Conference experiments using these systems demonstrate the computational feasibility of using…

Nurses and opioids: results of a bi-national survey on mental models regarding opioid administration in hospitals

PubMed Central

Guest, Charlotte; Sobotka, Fabian; Karavasopoulou, Athina; Ward, Stephen; Bantel, Carsten

2017-01-01

Objective Pain remains insufficiently treated in hospitals. Increasing evidence suggests human factors contribute to this, due to nurses failing to administer opioids. This behavior might be the consequence of nurses’ mental models about opioids. As personal experience and conceptions shape these models, the aim of this prospective survey was to identify model-influencing factors. Material and methods A questionnaire was developed comprising of 14 statements concerning ideations about opioids and seven questions concerning demographics, indicators of adult learning, and strength of religious beliefs. Latent variables that may underlie nurses’ mental models were identified using undirected graphical dependence models. Representative items of latent variables were employed for ordinal regression analysis. Questionnaires were distributed to 1,379 nurses in two London, UK, hospitals (n=580) and one German (n=799) hospital between September 2014 and February 2015. Results A total of 511 (37.1%) questionnaires were returned. Mean (standard deviation) age of participants were 37 (11) years; 83.5% participants were female; 45.2% worked in critical care; and 51.5% had more than 10 years experience. Of the nurses, 84% were not scared of opioids, 87% did not regard opioids as drugs to help patients die, and 72% did not view them as drugs of abuse. More English (41%) than German (28%) nurses were afraid of criminal investigations and were constantly aware of side effects (UK, 94%; Germany, 38%) when using opioids. Four latent variables were identified which likely influence nurses’ mental models: “conscious decision-making”; “medication-related fears”; “practice-based observations”; and “risk assessment”. They were predicted by strength of religious beliefs and indicators of informal learning such as experience but not by indicators of formal learning such as conference attendance. Conclusion Nurses in both countries employ analytical and affective mental models when administering the opioids and seem to learn from experience rather than from formal teaching. Additionally, some attitudes and emotions towards opioids are likely the result of nurses’ cultural background. PMID:28280383

Heterogeneity of Student Perceptions of the Classroom Climate: A Latent Profile Approach

ERIC Educational Resources Information Center

Schenke, Katerina; Ruzek, Erik; Lam, Arena C.; Karabenick, Stuart A.; Eccles, Jacquelynne S.

2017-01-01

Student perceptions are a pivotal point of measurement for understanding why classroom learning environments are effective. Yet there is some evidence that student perceptions cannot be reliably aggregated at the classroom level and, instead, could represent idiosyncratic experiences of students. The present study examines whether heterogeneity in…

Latent Memory of Unattended Stimuli Reactivated by Practice: An fMRI Study on the Role of Consciousness and Attention in Learning

PubMed Central

Meuwese, Julia D. I.; Scholte, H. Steven; Lamme, Victor A. F.

2014-01-01

Although we can only report about what is in the focus of our attention, much more than that is actually processed. And even when attended, stimuli may not always be reportable, for instance when they are masked. A stimulus can thus be unreportable for different reasons: the absence of attention or the absence of a conscious percept. But to what extent does the brain learn from exposure to these unreportable stimuli? In this fMRI experiment subjects were exposed to textured figure-ground stimuli, of which reportability was manipulated either by masking (which only interferes with consciousness) or with an inattention paradigm (which only interferes with attention). One day later learning was assessed neurally and behaviorally. Positive neural learning effects were found for stimuli presented in the inattention paradigm; for attended yet masked stimuli negative adaptation effects were found. Interestingly, these inattentional learning effects only became apparent in a second session after a behavioral detection task had been administered during which performance feedback was provided. This suggests that the memory trace that is formed during inattention is latent until reactivated by behavioral practice. However, no behavioral learning effects were found, therefore we cannot conclude that perceptual learning has taken place for these unattended stimuli. PMID:24603676

Latent memory of unattended stimuli reactivated by practice: an FMRI study on the role of consciousness and attention in learning.

PubMed

Meuwese, Julia D I; Scholte, H Steven; Lamme, Victor A F

2014-01-01

Although we can only report about what is in the focus of our attention, much more than that is actually processed. And even when attended, stimuli may not always be reportable, for instance when they are masked. A stimulus can thus be unreportable for different reasons: the absence of attention or the absence of a conscious percept. But to what extent does the brain learn from exposure to these unreportable stimuli? In this fMRI experiment subjects were exposed to textured figure-ground stimuli, of which reportability was manipulated either by masking (which only interferes with consciousness) or with an inattention paradigm (which only interferes with attention). One day later learning was assessed neurally and behaviorally. Positive neural learning effects were found for stimuli presented in the inattention paradigm; for attended yet masked stimuli negative adaptation effects were found. Interestingly, these inattentional learning effects only became apparent in a second session after a behavioral detection task had been administered during which performance feedback was provided. This suggests that the memory trace that is formed during inattention is latent until reactivated by behavioral practice. However, no behavioral learning effects were found, therefore we cannot conclude that perceptual learning has taken place for these unattended stimuli.

Large-scale weakly supervised object localization via latent category learning.

PubMed

Chong Wang; Kaiqi Huang; Weiqiang Ren; Junge Zhang; Maybank, Steve

2015-04-01

Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets.

Modeling change in learning strategies throughout higher education: a multi-indicator latent growth perspective.

PubMed

Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

2013-01-01

The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles--Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain.

Modeling Change in Learning Strategies throughout Higher Education: A Multi-Indicator Latent Growth Perspective

PubMed Central

Coertjens, Liesje; Donche, Vincent; De Maeyer, Sven; Vanthournout, Gert; Van Petegem, Peter

2013-01-01

The change in learning strategies during higher education is an important topic of research in the Student Approaches to Learning field. Although the studies on this topic are increasingly longitudinal, analyses have continued to rely primarily on traditional statistical methods. The present research is innovative in the way it uses a multi-indicator latent growth analysis in order to more accurately estimate the general and differential development in learning strategy scales. Moreover, the predictive strength of the latent growth models are estimated. The sample consists of one cohort of Flemish University College students, 245 of whom participated in the three measurement waves by filling out the processing and regulation strategies scales of the Inventory of Learning Styles – Short Versions. Independent-samples t-tests revealed that the longitudinal group is a non-random subset of students starting University College. For each scale, a multi-indicator latent growth model is estimated using Mplus 6.1. Results suggest that, on average, during higher education, students persisting in their studies in a non-delayed manner seem to shift towards high-quality learning and away from undirected and surface-oriented learning. Moreover, students from the longitudinal group are found to vary in their initial levels, while, unexpectedly, not in their change over time. Although the growth models fit the data well, significant residual variances in the latent factors remain. PMID:23844112

Indentifying Latent Classes and Testing Their Determinants in Early Adolescents' Use of Computers and Internet for Learning

ERIC Educational Resources Information Center

Heo, Gyun

2013-01-01

The purpose of the present study was to identify latent classes resting on early adolescents' change trajectory patterns in using computers and the Internet for learning and to test the effects of gender, self-control, self-esteem, and game use in South Korea. Latent growth mixture modeling (LGMM) was used to identify subpopulations in the Korea…

The Development of English and Mathematics Self-Efficacy: A Latent Growth Curve Analysis

ERIC Educational Resources Information Center

Phan, Huy P.

2012-01-01

Empirical research has provided evidence supporting the validation and prediction of 4 major sources of self-efficacy: enactive performance accomplishments, vicarious experiences, verbal persuasion, and emotional states. Other research studies have also attested to the importance and potency of self-efficacy in academic learning and achievement.…

An Analysis of the Relationship between the Learning Process and Learning Motivation Profiles of Japanese Pharmacy Students Using Structural Equation Modeling.

PubMed

Yamamura, Shigeo; Takehira, Rieko

2018-04-23

Pharmacy students in Japan have to maintain strong motivation to learn for six years during their education. The authors explored the students’ learning structure. All pharmacy students in their 4th through to 6th year at Josai International University participated in the survey. The revised two factor study process questionnaire and science motivation questionnaire II were used to assess their learning process and learning motivation profiles, respectively. Structural equation modeling (SEM) was used to examine a causal relationship between the latent variables in the learning process and those in the learning motivation profile. The learning structure was modeled on the idea that the learning process affects the learning motivation profile of respondents. In the multi-group SEM, the estimated mean of the deep learning to learning motivation profile increased just after their clinical clerkship for 6th year students. This indicated that the clinical experience benefited students’ deep learning, which is probably because the experience of meeting with real patients encourages meaningful learning in pharmacy studies.

Assessing the relationship between latent inhibition and the partial reinforcement extinction effect in autoshaping with rats.

PubMed

Boughner, Robert L; Papini, Mauricio R

2008-05-01

Results from a variety of independently run experiments suggest that latent inhibition (LI) and the partial reinforcement extinction effect (PREE) share underlying mechanisms. Experiment 1 tested this LI=PREE hypothesis by training the same set of rats in situations involving both nonreinforced preexposure to the conditioned stimulus (LI stage) and partial reinforcement training (PREE stage). Control groups were also included to assess both LI and the PREE. The results demonstrated a significant, but negative correlation between the size of the LI effect and that of the PREE. Experiment 2 extended this analysis to the effects on LI and the PREE of the anxiolytic benzodiazepine chlordiazepoxide (5 mg/kg, i.p.). Whereas chlordiazepoxide had no effect on LI, it delayed the onset of the PREE. No evidence in support of the LI=PREE hypothesis was obtained when these two learning phenomena were compared within the same experiment and under the same general conditions of training.

Measurement of latent cognitive abilities involved in concept identification learning.

PubMed

Thomas, Michael L; Brown, Gregory G; Gur, Ruben C; Moore, Tyler M; Patt, Virginie M; Nock, Matthew K; Naifeh, James A; Heeringa, Steven; Ursano, Robert J; Stein, Murray B

2015-01-01

We used cognitive and psychometric modeling techniques to evaluate the construct validity and measurement precision of latent cognitive abilities measured by a test of concept identification learning: the Penn Conditional Exclusion Test (PCET). Item response theory parameters were embedded within classic associative- and hypothesis-based Markov learning models and were fitted to 35,553 Army soldiers' PCET data from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Data were consistent with a hypothesis-testing model with multiple latent abilities-abstraction and set shifting. Latent abstraction ability was positively correlated with number of concepts learned, and latent set-shifting ability was negatively correlated with number of perseverative errors, supporting the construct validity of the two parameters. Abstraction was most precisely assessed for participants with abilities ranging from 1.5 standard deviations below the mean to the mean itself. Measurement of set shifting was acceptably precise only for participants making a high number of perseverative errors. The PCET precisely measures latent abstraction ability in the Army STARRS sample, especially within the range of mildly impaired to average ability. This precision pattern is ideal for a test developed to measure cognitive impairment as opposed to cognitive strength. The PCET also measures latent set-shifting ability, but reliable assessment is limited to the impaired range of ability, reflecting that perseverative errors are rare among cognitively healthy adults. Integrating cognitive and psychometric models can provide information about construct validity and measurement precision within a single analytical framework.

Proactive interference by cues presented without outcomes: Differences in context specificity of latent inhibition and conditioned inhibition.

PubMed

Miguez, Gonzalo; McConnell, Bridget; Polack, Cody W; Miller, Ralph R

2018-01-08

This report is part of a larger project examining associative interference as a function of the nature of the interfering and target associations. Lick suppression experiments with rats assessed the effects of context shifts on proactive outcome interference by latent inhibition (LI) and Pavlovian conditioned inhibition (CI) treatments on subsequently trained Pavlovian conditioned excitation treatment. LI and CI were trained in Context A during Phase 1, and then excitation treatment was administered in Context B during Phase 2, followed by tests for conditioned excitation in Contexts A, B, or C. Experiment 1 preliminarily established our LI and CI treatments and resulted in equally retarded acquisition of behavioral control when the target cue was subsequently trained as a conditioned excitor and tested in Context A. However, only CI treatment caused the target to pass a summation test for inhibition. Centrally, Experiment 2 consisted of LI and CI treatments in Context A followed by excitatory training in Context B. Testing found low excitatory control by both LI and CI cues in Context A relative to strong excitatory control in Context B, but CI treatment transferred to Context C more strongly than LI treatment. Experiment 3 determined that LI treatment failed to transfer to Context C even when the number of LI trials was greatly increased. Thus, first-learned LI appears to be relatively context specific, whereas first-learned CI generalizes to a neutral context. These observations add to existing evidence that LI and CI treatments result in different types of learning that diverge sharply in transfer to a novel test context.

Facilitation of Taste Memory Acquisition by Experiencing Previous Novel Taste Is Protein-Synthesis Dependent

ERIC Educational Resources Information Center

Merhav, Maayan; Rosenblum, Kobi

2008-01-01

Very little is known about the biological and molecular mechanisms that determine the effect of previous experience on implicit learning tasks. In the present study, we first defined weak and strong taste inputs according to measurements in the behavioral paradigm known as latent inhibition of conditioned taste aversion. We then demonstrated that…

Modeling loosely annotated images using both given and imagined annotations

NASA Astrophysics Data System (ADS)

Tang, Hong; Boujemaa, Nozha; Chen, Yunhao; Deng, Lei

2011-12-01

In this paper, we present an approach to learn latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: 1. ambiguous correspondences between visual features and annotated keywords; 2. incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning a topic model. In particular, some ``imagined'' keywords are poured into the incomplete annotation through measuring similarity between keywords in terms of their co-occurrence. Then, both given and imagined annotations are employed to learn probabilistic topic models for automatically annotating new images. We conduct experiments on two image databases (i.e., Corel and ESP) coupled with their loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods. The proposed method improves word-driven probability latent semantic analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.

A Latent Profile Analysis of University Students' Self-Regulated Learning Strategies

ERIC Educational Resources Information Center

Ning, Hoi Kwan; Downing, Kevin

2015-01-01

Based on self-reported cognitive, metacognitive, and behavioural strategy measures obtained from 828 final-year students from a university in Hong Kong, latent profile analysis (LPA) identified four distinct types of students with differential self-regulated learning strategy orientations: "competent self-regulated learners",…

[The personality of obese persons in psychological tests with special consideration on latent obesity].

PubMed

Pudel, V; Metzdorff, M; Oetting, M

1975-01-01

The results of psychological tests of the obese are inconsistent and no characteristic personality structure of the obese can be deduced from them. Investigations in childhood obesity failed to establish a general psychogenetic model of obesity. Yet overweight and ideal weight-subjects differ in spontaneous eating behaviour. Appetite and satiety of obese subjects are controlled by external stimuli to a far greater extent than in nonobese. From a behavioural scientific viewpoint it is proposed that learning experiences during childhood socialisation generate the disposition for obesity which can manifest itself later, after interaction with a special environment. At this stage, however, individual reactions to starting overweight are insolved; this process is strongly influenced by individual personality structures: an inadequate conflict management favours obesity; by cognitive control normal weight can be preserved in spite of the acquired disposition for obesity. Taking these "latently obese" as an example the role of personality structure and wrong eating habits is discussed and related to possible therapeutic strategies. A model of the psychogenetic basis of obesity is proposed. In this model eating-related learning experience is attributed a primary role and individual personality structure a secondary role in the psychogenesis of obesity.

Comment Data Mining to Estimate Student Performance Considering Consecutive Lessons

ERIC Educational Resources Information Center

Sorour, Shaymaa E.; Goda, Kazumasa; Mine, Tsunenori

2017-01-01

The purpose of this study is to examine different formats of comment data to predict student performance. Having students write comment data after every lesson can reflect students' learning attitudes, tendencies and learning activities involved with the lesson. In this research, Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic…

Hyper-Spectral Image Analysis With Partially Latent Regression and Spatial Markov Dependencies

NASA Astrophysics Data System (ADS)

Deleforge, Antoine; Forbes, Florence; Ba, Sileye; Horaud, Radu

2015-09-01

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.

Online Object Tracking, Learning and Parsing with And-Or Graphs.

PubMed

Wu, Tianfu; Lu, Yang; Zhu, Song-Chun

2017-12-01

This paper presents a method, called AOGTracker, for simultaneously tracking, learning and parsing (TLP) of unknown objects in video sequences with a hierarchical and compositional And-Or graph (AOG) representation. The TLP method is formulated in the Bayesian framework with a spatial and a temporal dynamic programming (DP) algorithms inferring object bounding boxes on-the-fly. During online learning, the AOG is discriminatively learned using latent SVM [1] to account for appearance (e.g., lighting and partial occlusion) and structural (e.g., different poses and viewpoints) variations of a tracked object, as well as distractors (e.g., similar objects) in background. Three key issues in online inference and learning are addressed: (i) maintaining purity of positive and negative examples collected online, (ii) controling model complexity in latent structure learning, and (iii) identifying critical moments to re-learn the structure of AOG based on its intrackability. The intrackability measures uncertainty of an AOG based on its score maps in a frame. In experiments, our AOGTracker is tested on two popular tracking benchmarks with the same parameter setting: the TB-100/50/CVPR2013 benchmarks  , [3] , and the VOT benchmarks [4] -VOT 2013, 2014, 2015 and TIR2015 (thermal imagery tracking). In the former, our AOGTracker outperforms state-of-the-art tracking algorithms including two trackers based on deep convolutional network   [5] , [6] . In the latter, our AOGTracker outperforms all other trackers in VOT2013 and is comparable to the state-of-the-art methods in VOT2014, 2015 and TIR2015.

Anxiety is not enough to drive me away: A latent profile analysis on math anxiety and math motivation.

PubMed

Wang, Zhe; Shakeshaft, Nicholas; Schofield, Kerry; Malanchini, Margherita

2018-01-01

Mathematics anxiety (MA) and mathematics motivation (MM) are important multi-dimensional non-cognitive factors in mathematics learning. While the negative relation between global MA and MM is well replicated, the relations between specific dimensions of MA and MM are largely unexplored. The present study utilized latent profile analysis to explore profiles of various aspects of MA (including learning MA and exam MA) and MM (including importance, self-perceived ability, and interest), to provide a more holistic understanding of the math-specific emotion and motivation experiences. In a sample of 927 high school students (13-21 years old), we found 8 distinct profiles characterized by various combinations of dimensions of MA and MM, revealing the complexity in the math-specific emotion-motivation relation beyond a single negative correlation. Further, these profiles differed on mathematics learning behaviors and mathematics achievement. For example, the highest achieving students reported modest exam MA and high MM, whereas the most engaged students were characterized by a combination of high exam MA and high MM. These results call for the need to move beyond linear relations among global constructs to address the complexity in the emotion-motivation-cognition interplay in mathematics learning, and highlight the importance of customized intervention for these heterogeneous groups.

Anxiety is not enough to drive me away: A latent profile analysis on math anxiety and math motivation

PubMed Central

Shakeshaft, Nicholas; Schofield, Kerry; Malanchini, Margherita

2018-01-01

Mathematics anxiety (MA) and mathematics motivation (MM) are important multi-dimensional non-cognitive factors in mathematics learning. While the negative relation between global MA and MM is well replicated, the relations between specific dimensions of MA and MM are largely unexplored. The present study utilized latent profile analysis to explore profiles of various aspects of MA (including learning MA and exam MA) and MM (including importance, self-perceived ability, and interest), to provide a more holistic understanding of the math-specific emotion and motivation experiences. In a sample of 927 high school students (13–21 years old), we found 8 distinct profiles characterized by various combinations of dimensions of MA and MM, revealing the complexity in the math-specific emotion-motivation relation beyond a single negative correlation. Further, these profiles differed on mathematics learning behaviors and mathematics achievement. For example, the highest achieving students reported modest exam MA and high MM, whereas the most engaged students were characterized by a combination of high exam MA and high MM. These results call for the need to move beyond linear relations among global constructs to address the complexity in the emotion-motivation-cognition interplay in mathematics learning, and highlight the importance of customized intervention for these heterogeneous groups. PMID:29444137

Compositional clustering in task structure learning

PubMed Central

Frank, Michael J.

2018-01-01

Humans are remarkably adept at generalizing knowledge between experiences in a way that can be difficult for computers. Often, this entails generalizing constituent pieces of experiences that do not fully overlap, but nonetheless share useful similarities with, previously acquired knowledge. However, it is often unclear how knowledge gained in one context should generalize to another. Previous computational models and data suggest that rather than learning about each individual context, humans build latent abstract structures and learn to link these structures to arbitrary contexts, facilitating generalization. In these models, task structures that are more popular across contexts are more likely to be revisited in new contexts. However, these models can only re-use policies as a whole and are unable to transfer knowledge about the transition structure of the environment even if only the goal has changed (or vice-versa). This contrasts with ecological settings, where some aspects of task structure, such as the transition function, will be shared between context separately from other aspects, such as the reward function. Here, we develop a novel non-parametric Bayesian agent that forms independent latent clusters for transition and reward functions, affording separable transfer of their constituent parts across contexts. We show that the relative performance of this agent compared to an agent that jointly clusters reward and transition functions depends environmental task statistics: the mutual information between transition and reward functions and the stochasticity of the observations. We formalize our analysis through an information theoretic account of the priors, and propose a meta learning agent that dynamically arbitrates between strategies across task domains to optimize a statistical tradeoff. PMID:29672581

Low-Rank Discriminant Embedding for Multiview Learning.

PubMed

Li, Jingjing; Wu, Yue; Zhao, Jidong; Lu, Ke

2017-11-01

This paper focuses on the specific problem of multiview learning where samples have the same feature set but different probability distributions, e.g., different viewpoints or different modalities. Since samples lying in different distributions cannot be compared directly, this paper aims to learn a latent subspace shared by multiple views assuming that the input views are generated from this latent subspace. Previous approaches usually learn the common subspace by either maximizing the empirical likelihood, or preserving the geometric structure. However, considering the complementarity between the two objectives, this paper proposes a novel approach, named low-rank discriminant embedding (LRDE), for multiview learning by taking full advantage of both sides. By further considering the duality between data points and features of multiview scene, i.e., data points can be grouped based on their distribution on features, while features can be grouped based on their distribution on the data points, LRDE not only deploys low-rank constraints on both sample level and feature level to dig out the shared factors across different views, but also preserves geometric information in both the ambient sample space and the embedding feature space by designing a novel graph structure under the framework of graph embedding. Finally, LRDE jointly optimizes low-rank representation and graph embedding in a unified framework. Comprehensive experiments in both multiview manner and pairwise manner demonstrate that LRDE performs much better than previous approaches proposed in recent literatures.

Inhibition of Vicariously Learned Fear in Children Using Positive Modeling and Prior Exposure

PubMed Central

2015-01-01

One of the challenges to conditioning models of fear acquisition is to explain how different individuals can experience similar learning events and only some of them subsequently develop fear. Understanding factors moderating the impact of learning events on fear acquisition is key to understanding the etiology and prevention of fear in childhood. This study investigates these moderators in the context of vicarious (observational) learning. Two experiments tested predictions that the acquisition or inhibition of fear via vicarious learning is driven by associative learning mechanisms similar to direct conditioning. In Experiment 1, 3 groups of children aged 7 to 9 years received 1 of 3 inhibitive information interventions—psychoeducation, factual information, or no information (control)—prior to taking part in a vicarious fear learning procedure. In Experiment 2, 3 groups of children aged 7 to 10 years received 1 of 3 observational learning interventions—positive modeling (immunization), observational familiarity (latent inhibition), or no prevention (control)—before vicarious fear learning. Results indicated that observationally delivered manipulations inhibited vicarious fear learning, while preventions presented via written information did not. These findings confirm that vicarious learning shares some of the characteristics of direct conditioning and can explain why not all individuals will develop fear following a vicarious learning event. They also suggest that the modality of inhibitive learning is important and should match the fear learning pathway for increased chances of inhibition. Finally, the results demonstrate that positive modeling is likely to be a particularly effective method for preventing fear-related observational learning in children. PMID:26653136

Inhibition of vicariously learned fear in children using positive modeling and prior exposure.

PubMed

Askew, Chris; Reynolds, Gemma; Fielding-Smith, Sarah; Field, Andy P

2016-02-01

One of the challenges to conditioning models of fear acquisition is to explain how different individuals can experience similar learning events and only some of them subsequently develop fear. Understanding factors moderating the impact of learning events on fear acquisition is key to understanding the etiology and prevention of fear in childhood. This study investigates these moderators in the context of vicarious (observational) learning. Two experiments tested predictions that the acquisition or inhibition of fear via vicarious learning is driven by associative learning mechanisms similar to direct conditioning. In Experiment 1, 3 groups of children aged 7 to 9 years received 1 of 3 inhibitive information interventions-psychoeducation, factual information, or no information (control)-prior to taking part in a vicarious fear learning procedure. In Experiment 2, 3 groups of children aged 7 to 10 years received 1 of 3 observational learning interventions-positive modeling (immunization), observational familiarity (latent inhibition), or no prevention (control)-before vicarious fear learning. Results indicated that observationally delivered manipulations inhibited vicarious fear learning, while preventions presented via written information did not. These findings confirm that vicarious learning shares some of the characteristics of direct conditioning and can explain why not all individuals will develop fear following a vicarious learning event. They also suggest that the modality of inhibitive learning is important and should match the fear learning pathway for increased chances of inhibition. Finally, the results demonstrate that positive modeling is likely to be a particularly effective method for preventing fear-related observational learning in children. (c) 2016 APA, all rights reserved).

Deep Processing Strategies and Critical Thinking: Developmental Trajectories Using Latent Growth Analyses

ERIC Educational Resources Information Center

Phan, Huy P.

2011-01-01

The author explored the developmental courses of deep learning approach and critical thinking over a 2-year period. Latent growth curve modeling (LGM) procedures were used to test and trace the trajectories of both theoretical frameworks over time. Participants were 264 (119 women, 145 men) university undergraduates. The Deep Learning subscale of…

Latent Learning and Deferred Imitation at 3 Months

ERIC Educational Resources Information Center

Campanella, Jennifer; Rovee-Collier, Carolyn

2005-01-01

Young infants spend most of their waking time looking around, but whether they learn anything about what they see is unknown. We used a sensory preconditioning paradigm and a deferred imitation task to assess if 3-month-olds formed a latent association between 2 objects (S[subscript 1], S[subscript 2]) that they merely saw together. Because…

Global, broad, or specific cognitive differences? Using a MIMIC model to examine differences in CHC abilities in children with learning disabilities.

PubMed

Niileksela, Christopher R; Reynolds, Matthew R

2014-01-01

This study was designed to better understand the relations between learning disabilities and different levels of latent cognitive abilities, including general intelligence (g), broad cognitive abilities, and specific abilities based on the Cattell-Horn-Carroll theory of intelligence (CHC theory). Data from the Differential Ability Scales-Second Edition (DAS-II) were used to create a multiple-indicator multiple cause model to examine the latent mean differences in cognitive abilities between children with and without learning disabilities in reading (LD reading), math (LD math), and reading and writing(LD reading and writing). Statistically significant differences were found in the g factor between the norm group and the LD groups. After controlling for differences in g, the LD reading and LD reading and writing groups showed relatively lower latent processing speed, and the LD math group showed relatively higher latent comprehension-knowledge. There were also some differences in some specific cognitive abilities, including lower scores in spatial relations and numerical facility for the LD math group, and lower scores in visual memory for the LD reading and writing group. These specific mean differences were above and beyond any differences in the latent cognitive factor means.

Validation and scopolamine-reversal of latent learning in the water maze utilizing a revised direct platform placement procedure.

PubMed

Malin, David H; Schaar, Krystal L; Izygon, Jonathan J; Nghiem, Duyen M; Jabitta, Sikirat Y; Henceroth, Mallori M; Chang, Yu-Hsuan; Daggett, Jenny M; Ward, Christopher P

2015-08-01

The Morris water maze is routinely used to explore neurobiological mechanisms of working memory. Humans can often acquire working memory relevant to performing a task by mere sensory observation, without having to actually perform the task followed by reinforcement. This can be modeled in the water maze through direct placement of a rat on the escape platform so that it can observe the location, and then assessing the subject's performance in swimming back to the platform. However, direct placement procedures have hardly been studied for two decades, reflecting a controversy about whether direct placement resulted in sufficiently rapid and direct swims back to the platform. In the present study, utilizing revised training methods, a more comprehensive measure of trajectory directness, a more rigorous sham-trained control procedure and an optimal placement-test interval, rats swam almost directly back to the platform in under 4s, significantly more quickly and directly than sham-trained subjects. Muscarinic cholinergic mechanisms, which are inactivated by scopolamine, are essential to memory for standard learning paradigms in the water maze. This experiment determined whether this would also be true for latent learning. ANOVA revealed significant negative effects of scopolamine on both speed and accuracy of trajectory, as well as significant positive effects of direct placement training vs. sham-training. In a probe trial, placement-trained animals without scopolamine spent significantly more time and path length in the target quadrant than trained rats with scopolamine and sham-trained rats without scopolamine. Scopolamine impairments are likely due to effects on memory, since the same dose had little effect on performance with a visible platform. The revised direct placement model offers a means of further comparing the neural mechanisms of latent learning with those of standard instrumental learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Harry Potter and the sorcerer's scope: latent scope biases in explanatory reasoning.

PubMed

Khemlani, Sangeet S; Sussman, Abigail B; Oppenheimer, Daniel M

2011-04-01

What makes a good explanation? We examine the function of latent scope, i.e., the number of unobserved phenomena that an explanation can account for. We show that individuals prefer narrow latent scope explanations-those that account for fewer unobserved effects-to broader explanations. In Experiments 1a-d, participants found narrow latent scope explanations to be both more satisfying and more likely. In Experiment 2 we directly manipulated base rate information and again found a preference for narrow latent scope explanations. Participants in Experiment 3 evaluated more natural explanations of unexpected observations, and again displayed a bias for narrow latent scope explanations. We conclude by considering what this novel bias tells us about how humans evaluate explanations and engage in causal reasoning.

A study of the latent effects of family learning courses in science

NASA Astrophysics Data System (ADS)

Gennaro, Eugene D.; Hereid, Nancy; Ostlund, Karen

It is well documented that students' exposure to science in the middle school is critical for their later selection of science courses, yet instruction time and course offerings in science during the middle school years are often limited. Out-of-School Science Experiences with funds from the National Science Foundation (DISE No. 07872) produced five short science courses intended for children in middle school grades (6, 7, and 8) and their parents that supplement normal science instruction based on topics that are integral to traditional science teaching. The courses were offered through Community Education programs and through informal science learning centers (e.g., zoos, museums, and planetariums). An added strength of the program is that it employs the family as a motivator and reinforcer in a cooperative learning venture. The study reported here is an attempt to determine participant reaction two to three years after having taken the courses, to the course experience, the influence that the courses had on subsequent learning behavior, and the relationship between parents and children.

The Latent Structures of the Learning and Study Strategies Inventory (LASSI): A Comparative Analysis.

ERIC Educational Resources Information Center

Obiekwe, Jerry C.

The first purpose of this study was to analyze the results of the confirmatory factor analyses, via EQS, with regard to the latent structures of the Learning and Study Strategies Inventory (LASSI) (C. Weinstein, D. Palmer, and A. Schulte, 1987) as proposed by S. Olejnik and S. Nist (1992), A. Olivarez and M. Tallent-Runnels (1994), B. Olaussen and…

Discrete Sparse Coding.

PubMed

Exarchakis, Georgios; Lücke, Jörg

2017-11-01

Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

Modeling semantic aspects for cross-media image indexing.

PubMed

Monay, Florent; Gatica-Perez, Daniel

2007-10-01

To go beyond the query-by-example paradigm in image retrieval, there is a need for semantic indexing of large image collections for intuitive text-based image search. Different models have been proposed to learn the dependencies between the visual content of an image set and the associated text captions, then allowing for the automatic creation of semantic indices for unannotated images. The task, however, remains unsolved. In this paper, we present three alternatives to learn a Probabilistic Latent Semantic Analysis model (PLSA) for annotated images, and evaluate their respective performance for automatic image indexing. Under the PLSA assumptions, an image is modeled as a mixture of latent aspects that generates both image features and text captions, and we investigate three ways to learn the mixture of aspects. We also propose a more discriminative image representation than the traditional Blob histogram, concatenating quantized local color information and quantized local texture descriptors. The first learning procedure of a PLSA model for annotated images is a standard EM algorithm, which implicitly assumes that the visual and the textual modalities can be treated equivalently. The other two models are based on an asymmetric PLSA learning, allowing to constrain the definition of the latent space on the visual or on the textual modality. We demonstrate that the textual modality is more appropriate to learn a semantically meaningful latent space, which translates into improved annotation performance. A comparison of our learning algorithms with respect to recent methods on a standard dataset is presented, and a detailed evaluation of the performance shows the validity of our framework.

Can an old dog learn (and want to experience) new tricks? Cognitive training increases openness to experience in older adults

PubMed Central

Jackson, Joshua J.; Hill, Patrick L.; Payne, Brennan R.; Roberts, Brent W.; Stine-Morrow, Elizabeth A. L.

2012-01-01

The present study investigated whether an intervention aimed to increase cognitive ability in older adults also changes the personality trait of openness to experience. Older adults completed a 16-week program in inductive reasoning training supplemented by weekly crossword and Sudoku puzzles. Changes in openness to experience were modeled across four assessments over 30 weeks using latent growth curve models. Results indicate that participants in the intervention condition increased in the trait of openness compared to a waitlist control group. The study is one of the first to demonstrate that personality traits can change through non-psychopharmocological interventions. PMID:22251379

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hybrid generative-discriminative human action recognition by combining spatiotemporal words with supervised topic models

NASA Astrophysics Data System (ADS)

Sun, Hao; Wang, Cheng; Wang, Boliang

2011-02-01

We present a hybrid generative-discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting space-time interest points and describing these points using both shape and motion cues. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure that is most relevant to action categorization. The proposed algorithm retains most of the desirable properties of generative learning while increasing the classification performance though a discriminative setting. It has also been extended to exploit both labeled data and unlabeled data to learn human actions under a unified framework. We test our algorithm on three challenging data sets: the KTH human motion data set, the Weizmann human action data set, and a ballet data set. Our results are either comparable to or significantly better than previously published results on these data sets and reflect the promise of hybrid generative-discriminative learning approaches.

Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes

NASA Astrophysics Data System (ADS)

Bauer, Johannes; Dávila-Chacón, Jorge; Wermter, Stefan

2015-10-01

Humans and other animals have been shown to perform near-optimally in multi-sensory integration tasks. Probabilistic population codes (PPCs) have been proposed as a mechanism by which optimal integration can be accomplished. Previous approaches have focussed on how neural networks might produce PPCs from sensory input or perform calculations using them, like combining multiple PPCs. Less attention has been given to the question of how the necessary organisation of neurons can arise and how the required knowledge about the input statistics can be learned. In this paper, we propose a model of learning multi-sensory integration based on an unsupervised learning algorithm in which an artificial neural network learns the noise characteristics of each of its sources of input. Our algorithm borrows from the self-organising map the ability to learn latent-variable models of the input and extends it to learning to produce a PPC approximating a probability density function over the latent variable behind its (noisy) input. The neurons in our network are only required to perform simple calculations and we make few assumptions about input noise properties and tuning functions. We report on a neurorobotic experiment in which we apply our algorithm to multi-sensory integration in a humanoid robot to demonstrate its effectiveness and compare it to human multi-sensory integration on the behavioural level. We also show in simulations that our algorithm performs near-optimally under certain plausible conditions, and that it reproduces important aspects of natural multi-sensory integration on the neural level.

Social isolation induces deficit of latent learning performance in mice: a putative animal model of attention deficit/hyperactivity disorder.

PubMed

Ouchi, Hirofumi; Ono, Kazuya; Murakami, Yukihisa; Matsumoto, Kinzo

2013-02-01

Social isolation of rodents (SI) elicits a variety of stress responses such as increased aggressiveness, hyper-locomotion, and reduced susceptibility to pentobarbital. To obtain a better understanding of the relevance of SI-induced behavioral abnormalities to psychiatric disorders, we examined the effect of SI on latent learning as an index of spatial attention, and discussed the availability of SI as an epigenetic model of attention deficit hyperactivity disorder (ADHD). Except in specially stated cases, 4-week-old male mice were housed in a group or socially isolated for 3-70 days before experiments. The animals socially isolated for 1 week or more exhibited spatial attention deficit in the water-finding test. Re-socialized rearing for 5 weeks after 1-week SI failed to attenuate the spatial attention deficit. The effect of SI on spatial attention showed no gender difference or correlation with increased aggressive behavior. Moreover, SI had no effect on cognitive performance elucidated in a modified Y-maze or an object recognition test, but it significantly impaired contextual and conditional fear memory elucidated in the fear-conditioning test. Drugs used for ADHD therapy, methylphenidate (1-10 mg/kg, i.p.) and caffeine (0.5-1 mg/kg, i.p.), improved SI-induced latent learning deficit in a manner reversible with cholinergic but not dopaminergic antagonists. Considering the behavioral features of SI mice together with their susceptibility to ADHD drugs, the present findings suggest that SI provides an epigenetic animal model of ADHD and that central cholinergic systems play a role in the effect of methylphenidate on SI-induced spatial attention deficit. Copyright © 2012 Elsevier B.V. All rights reserved.

Supervised embedding of textual predictors with applications in clinical diagnostics for pediatric cardiology.

PubMed

Perry, Thomas Ernest; Zha, Hongyuan; Zhou, Ke; Frias, Patricio; Zeng, Dadan; Braunstein, Mark

2014-02-01

Electronic health records possess critical predictive information for machine-learning-based diagnostic aids. However, many traditional machine learning methods fail to simultaneously integrate textual data into the prediction process because of its high dimensionality. In this paper, we present a supervised method using Laplacian Eigenmaps to enable existing machine learning methods to estimate both low-dimensional representations of textual data and accurate predictors based on these low-dimensional representations at the same time. We present a supervised Laplacian Eigenmap method to enhance predictive models by embedding textual predictors into a low-dimensional latent space, which preserves the local similarities among textual data in high-dimensional space. The proposed implementation performs alternating optimization using gradient descent. For the evaluation, we applied our method to over 2000 patient records from a large single-center pediatric cardiology practice to predict if patients were diagnosed with cardiac disease. In our experiments, we consider relatively short textual descriptions because of data availability. We compared our method with latent semantic indexing, latent Dirichlet allocation, and local Fisher discriminant analysis. The results were assessed using four metrics: the area under the receiver operating characteristic curve (AUC), Matthews correlation coefficient (MCC), specificity, and sensitivity. The results indicate that supervised Laplacian Eigenmaps was the highest performing method in our study, achieving 0.782 and 0.374 for AUC and MCC, respectively. Supervised Laplacian Eigenmaps showed an increase of 8.16% in AUC and 20.6% in MCC over the baseline that excluded textual data and a 2.69% and 5.35% increase in AUC and MCC, respectively, over unsupervised Laplacian Eigenmaps. As a solution, we present a supervised Laplacian Eigenmap method to embed textual predictors into a low-dimensional Euclidean space. This method allows many existing machine learning predictors to effectively and efficiently capture the potential of textual predictors, especially those based on short texts.

Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning.

PubMed

Molloy, Katharine; Moore, David R; Sohoglu, Ediz; Amitay, Sygal

2012-01-01

The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions.

Less Is More: Latent Learning Is Maximized by Shorter Training Sessions in Auditory Perceptual Learning

PubMed Central

Molloy, Katharine; Moore, David R.; Sohoglu, Ediz; Amitay, Sygal

2012-01-01

Background The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. Methodology/Principal Findings We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (∼8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (∼4 hr in total) relative to a group that trained for a single session (∼1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Conclusions/Significance Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions. PMID:22606309

Controlling coaching and athlete thriving in elite adolescent netballers: The buffering effect of athletes' mental toughness.

PubMed

Gucciardi, Daniel F; Stamatis, Andreas; Ntoumanis, Nikos

2017-08-01

The purposes of this study were to examine the association between controlling coach behaviours and athlete experiences of thriving and test the buffering effect of mental toughness on this relation. A cross-sectional survey. In total, 232 female netballers aged 11 to 17 years (14.97+1.52) with between 1 and 15 years of experience in their sport (7.50+2.28) completed measures of controlling coach interpersonal style, mental toughness and thriving. Latent moderated structural models indicated that (i) controlling coach behaviours were inversely related with experiences of vitality and learning; (ii) mental toughness was positively associated with psychological experiences of both dimensions of thriving; and (iii) mental toughness moderated the effect of coach's controlling interpersonal style on learning but not vitality experiences, such that the effect was weaker for individuals who reported higher levels of mental toughness. This study extends past work and theory to show that mental toughness may enable athletes to counteract the potentially deleterious effect of controlling coach interpersonal styles. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

ERIC Educational Resources Information Center

Steinley, Douglas; Brusco, Michael J.

2011-01-01

This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

An unsupervised machine learning model for discovering latent infectious diseases using social media data.

PubMed

Lim, Sunghoon; Tucker, Conrad S; Kumara, Soundar

2017-02-01

The authors of this work propose an unsupervised machine learning model that has the ability to identify real-world latent infectious diseases by mining social media data. In this study, a latent infectious disease is defined as a communicable disease that has not yet been formalized by national public health institutes and explicitly communicated to the general public. Most existing approaches to modeling infectious-disease-related knowledge discovery through social media networks are top-down approaches that are based on already known information, such as the names of diseases and their symptoms. In existing top-down approaches, necessary but unknown information, such as disease names and symptoms, is mostly unidentified in social media data until national public health institutes have formalized that disease. Most of the formalizing processes for latent infectious diseases are time consuming. Therefore, this study presents a bottom-up approach for latent infectious disease discovery in a given location without prior information, such as disease names and related symptoms. Social media messages with user and temporal information are extracted during the data preprocessing stage. An unsupervised sentiment analysis model is then presented. Users' expressions about symptoms, body parts, and pain locations are also identified from social media data. Then, symptom weighting vectors for each individual and time period are created, based on their sentiment and social media expressions. Finally, latent-infectious-disease-related information is retrieved from individuals' symptom weighting vectors. Twitter data from August 2012 to May 2013 are used to validate this study. Real electronic medical records for 104 individuals, who were diagnosed with influenza in the same period, are used to serve as ground truth validation. The results are promising, with the highest precision, recall, and F 1 score values of 0.773, 0.680, and 0.724, respectively. This work uses individuals' social media messages to identify latent infectious diseases, without prior information, quicker than when the disease(s) is formalized by national public health institutes. In particular, the unsupervised machine learning model using user, textual, and temporal information in social media data, along with sentiment analysis, identifies latent infectious diseases in a given location. Copyright © 2016 Elsevier Inc. All rights reserved.

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

Effect of three different cultivars of Lepidium meyenii (Maca) on learning and depression in ovariectomized mice

PubMed Central

Rubio, Julio; Caldas, Maria; Dávila, Sonia; Gasco, Manuel; Gonzales, Gustavo F

2006-01-01

Background Lepidium meyenii Walp. (Brassicaceae), known as Maca, is a Peruvian hypocotyl growing exclusively between 4000 and 4500 m altitude in the central Peruvian Andes, particularly in Junin plateau and is used traditionally to enhance fertility. Maca is a cultivated plant and different cultivars are described according to the color of the hypocotyls. Methods The study aimed to elucidate the effect of Yellow, Red and Black Maca on cognitive function and depression in ovariectomized (OVX) mice. In all experiments OVX mice were treated during 21 days and divided in four groups: control group, Yellow Maca, Red Maca and Black Maca. Latent learning was assessed using the water finding task and the antidepressant activity of the three varieties of Maca was evaluated using the forced swimming test. Animals were sacrificed at the end of each treatment and the uterus were excised and weighed. Results Black Maca was the variety that showed the best response in the water finding task, particularly in the trained mice. The three varieties were effective to reduce finding latency in non trained and trained mice (P < 0.05). In the force swimming test, all varieties assessed reduced the time of immobility and increased uterine weight in OVX mice. Conclusion Black Maca appeared to have more beneficial effects on latent learning in OVX mice; meanwhile, all varieties of Maca showed antidepressant activity. PMID:16796734

Effect of three different cultivars of Lepidium meyenii (Maca) on learning and depression in ovariectomized mice.

PubMed

Rubio, Julio; Caldas, Maria; Dávila, Sonia; Gasco, Manuel; Gonzales, Gustavo F

2006-06-23

Lepidium meyenii Walp. (Brassicaceae), known as Maca, is a Peruvian hypocotyl growing exclusively between 4000 and 4500 m altitude in the central Peruvian Andes, particularly in Junin plateau and is used traditionally to enhance fertility. Maca is a cultivated plant and different cultivars are described according to the color of the hypocotyls. The study aimed to elucidate the effect of Yellow, Red and Black Maca on cognitive function and depression in ovariectomized (OVX) mice. In all experiments OVX mice were treated during 21 days and divided in four groups: control group, Yellow Maca, Red Maca and Black Maca. Latent learning was assessed using the water finding task and the antidepressant activity of the three varieties of Maca was evaluated using the forced swimming test. Animals were sacrificed at the end of each treatment and the uterus were excised and weighed. Black Maca was the variety that showed the best response in the water finding task, particularly in the trained mice. The three varieties were effective to reduce finding latency in non trained and trained mice (P < 0.05). In the force swimming test, all varieties assessed reduced the time of immobility and increased uterine weight in OVX mice. Black Maca appeared to have more beneficial effects on latent learning in OVX mice; meanwhile, all varieties of Maca showed antidepressant activity.

How do verbal short-term memory and working memory relate to the acquisition of vocabulary and grammar? A comparison between first and second language learners.

PubMed

Verhagen, Josje; Leseman, Paul

2016-01-01

Previous studies show that verbal short-term memory (VSTM) is related to vocabulary learning, whereas verbal working memory (VWM) is related to grammar learning in children learning a second language (L2) in the classroom. In this study, we investigated whether the same relationships apply to children learning an L2 in a naturalistic setting and to monolingual children. We also investigated whether relationships with verbal memory differ depending on the type of grammar skill investigated (i.e., morphology vs. syntax). Participants were 63 Turkish children who learned Dutch as an L2 and 45 Dutch monolingual children (mean age = 5 years). Children completed a series of VSTM and VWM tasks, a Dutch vocabulary task, and a Dutch grammar task. A confirmatory factor analysis showed that VSTM and VWM represented two separate latent factors in both groups. Structural equation modeling showed that VSTM, treated as a latent factor, significantly predicted vocabulary and grammar. VWM, treated as a latent factor, predicted only grammar. Both memory factors were significantly related to the acquisition of morphology and syntax. There were no differences between the two groups. These results show that (a) VSTM and VWM are differentially associated with language learning and (b) the same memory mechanisms are employed for learning vocabulary and grammar in L1 children and in L2 children who learn their L2 naturalistically. Copyright © 2015 Elsevier Inc. All rights reserved.

Regulating approaches to learning: Testing learning strategy convergences across a year at university.

PubMed

Fryer, Luke K; Vermunt, Jan D

2018-03-01

Contemporary models of student learning within higher education are often inclusive of processing and regulation strategies. Considerable research has examined their use over time and their (person-centred) convergence. The longitudinal stability/variability of learning strategy use, however, is poorly understood, but essential to supporting student learning across university experiences. Develop and test a person-centred longitudinal model of learning strategies across the first-year university experience. Japanese university students (n = 933) completed surveys (deep and surface approaches to learning; self, external, and lack of regulation) at the beginning and end of their first year. Following invariance and cross-sectional tests, latent profile transition analysis (LPTA) was undertaken. Initial difference testing supported small but significant differences for self-/external regulation. Fit indices supported a four-group model, consistent across both measurement points. These subgroups were labelled Low Quality (low deep approaches and self-regulation), Low Quantity (low strategy use generally), Average (moderate strategy use), and High Quantity (intense use of all strategies) strategies. The stability of these groups ranged from stable to variable: Average (93% stayers), Low Quality (90% stayers), High Quantity (72% stayers), and Low Quantity (40% stayers). The three largest transitions presented joint shifts in processing/regulation strategy preference across the year, from adaptive to maladaptive and vice versa. Person-centred longitudinal findings presented patterns of learning transitions that different students experience during their first year at university. Stability/variability of students' strategy use was linked to the nature of initial subgroup membership. Findings also indicated strong connections between processing and regulation strategy changes across first-year university experiences. Implications for theory and practice are discussed. © 2017 The British Psychological Society.

Chronic mild stress impairs latent inhibition and induces region-specific neural activation in CHL1-deficient mice, a mouse model of schizophrenia.

PubMed

Buhusi, Mona; Obray, Daniel; Guercio, Bret; Bartlett, Mitchell J; Buhusi, Catalin V

2017-08-30

Schizophrenia is a neurodevelopmental disorder characterized by abnormal processing of information and attentional deficits. Schizophrenia has a high genetic component but is precipitated by environmental factors, as proposed by the 'two-hit' theory of schizophrenia. Here we compared latent inhibition as a measure of learning and attention, in CHL1-deficient mice, an animal model of schizophrenia, and their wild-type littermates, under no-stress and chronic mild stress conditions. All unstressed mice as well as the stressed wild-type mice showed latent inhibition. In contrast, CHL1-deficient mice did not show latent inhibition after exposure to chronic stress. Differences in neuronal activation (c-Fos-positive cell counts) were noted in brain regions associated with latent inhibition: Neuronal activation in the prelimbic/infralimbic cortices and the nucleus accumbens shell was affected solely by stress. Neuronal activation in basolateral amygdala and ventral hippocampus was affected independently by stress and genotype. Most importantly, neural activation in nucleus accumbens core was affected by the interaction between stress and genotype. These results provide strong support for a 'two-hit' (genes x environment) effect on latent inhibition in CHL1-deficient mice, and identify CHL1-deficient mice as a model of schizophrenia-like learning and attention impairments. Copyright © 2017 Elsevier B.V. All rights reserved.

A further assessment of the Hall-Rodriguez theory of latent inhibition.

PubMed

Leung, Hiu Tin; Killcross, A S; Westbrook, R Frederick

2013-04-01

The Hall-Rodriguez (G. Hall & G. Rodriguez, 2010, Associative and nonassociative processes in latent inhibition: An elaboration of the Pearce-Hall model, in R. E. Lubow & I. Weiner, Eds., Latent inhibition: Data, theories, and applications to schizophrenia, pp. 114-136, Cambridge, England: Cambridge University Press) theory of latent inhibition predicts that it will be deepened when a preexposed target stimulus is given additional preexposures in compound with (a) a novel stimulus or (b) another preexposed stimulus, and (c) that deepening will be greater when the compound contains a novel rather than another preexposed stimulus. A series of experiments studied these predictions using a fear conditioning procedure with rats. In each experiment, rats were preexposed to 3 stimuli, 1 (A) taken from 1 modality (visual or auditory) and the remaining 2 (X and Y) taken from another modality (auditory or visual). Then A was compounded with X, and Y was compounded with a novel stimulus (B) taken from the same modality as A. A previous series of experiments (H. T. Leung, A. S. Killcross, & R. F. Westbrook, 2011, Additional exposures to a compound of two preexposed stimuli deepen latent inhibition, Journal of Experimental Psychology: Animal Behavior Processes, Vol. 37, pp. 394-406) compared A with Y, finding that A was more latently inhibited than Y, the opposite of what was predicted. The present experiments confirmed that A was more latently inhibited than Y, showed that this was due to A entering the compound more latently inhibited than Y, and finally, that a comparison of X and Y confirmed the 3 predictions made by the theory.

Latent Inhibition in an Insect: The Role of Aminergic Signaling

ERIC Educational Resources Information Center

Fernandez, Vanesa M.; Giurfa, Martin; Devaud, Jean-Marc; Farina, Walter M.

2012-01-01

Latent inhibition (LI) is a decrement in learning performance that results from the nonreinforced pre-exposure of the to-be-conditioned stimulus, in both vertebrates and invertebrates. In vertebrates, LI development involves dopamine and serotonin; in invertebrates there is yet no information. We studied differential olfactory conditioning of the…

[Influence of combined vitamin deficiency on unconditioned reflexes and learning in growing rats].

PubMed

Vrzhesinskaya, O A; Kodentsova, V M; Beketova, N A; Pereverzeva, O G; Kosheleva, O V; Sidorova, Yu S; Zorin, S N; Mazo, V K

2015-01-01

The aim of this study was to investigate the effect of combined deficiency of all vitamins on the manifestation of unconditioned reflex and learning (in response to an electric current) in growing Wistar rats with initial body weight 53.4 ± 1.2 g (45.5-62.0 g). 20 of 46 tested male rats (latent period of transition from the illuminated chamber to the dark compartment did not exceed 60 s) were included in the experiment. Rats were randomly divided into 2 groups (control and experimental) for the duration of the latent period and body mass. Within 23 days the rats of the control group received a complete semisynthetic diet. Combined vitamin deficiency in tested rats was caused by 5-fold diet decrease of the amount of vitamin mixture without vitamin E. On the 12th day the second phase of testing was performed, during which the rat received electrocutaneous irritation on paws (current 0.4 mA, 8 seconds) after transition to the dark compartment of the chamber. Preservation of the conducted reflex was performed 24 h and 9 days after training. On the 23rd day pre-anesthetized with ether rats were taken out from the experiment by decapitation. The content of vitamin A (retinol and retinol palmitate) and E (tocopherols) in plasma and liver and in the sunflower oil was analyzed by HPLC, the level of vitamins B1 and B2 in liver and casein by fluorimetric method, blood serum malondialdehyde content--by spectrophotometric method. Reducing of vitamin mixture amount of the diet lead to significant reduction in liver vitamin A, E, B1, and B2 level and in blood plasma vitamin A and E concentration by the end of the experiment, but had no effect on blood plasma MDA concentration. On the 12th day of vitamin deficiency in rats manifestation of unconditioned reflex (photophobia) has been deteriorated, as evidenced by the significant 3,2-fold increase of latent period of transition to the dark compartment compared with animals fed a complete diet (47.8 ± 15.8 vs 14.8 ± 3.6 sec), but their ability to learn hadn't been effected. Based on the data that vitamin deficiency, especially of vitamin-antioxidants, causes oxidative stress, and that increase of corticosterone level in hippocampus during aging significantly inhibits the function of the brain, we can assume that increasing of corticosterone level may be one of the cause of the detected cognitive impairment, as isolated vitamin A deficiency in rats increases tissue corticosterone levels.

Studies of Phase Change Materials and a Latent Heat Storage Unit Used for a Natural Circulation Cooling/Latent Heat Storage System

NASA Astrophysics Data System (ADS)

Sakitani, Katsumi; Honda, Hiroshi

Experiments were performed to investigate feasibility of using organic materials as a PCM for a latent heat storage unit of a natural circulation cooling/latent heat storage system. This system was designed to cool a shelter accommodating telecommunication equipment located in subtropical deserts or similar regions without using a power source. Taking into account practical considerations and the results of various experiments regarding the thermodynamic properties, thermal degradation, and corrosiveness to metals, lauric acid and iron was selected for the PCM and the latent heat storage unit material, respectively. Cyclic heating and cooling of the latent heat storage unit undergoing solid-liquid phase change was repeated for more than 430 days. The results showed that the heating-cooling curve was almost unchanged between the early stage and the 1,870th cycle. It was concluded that the latent heat storage unit could be used safely for more than ten years as a component of the cooling system.

Learning to Learn about Uncertain Feedback

ERIC Educational Resources Information Center

Faraut, Mailys C. M.; Procyk, Emmanuel; Wilson, Charles R. E.

2016-01-01

Unexpected outcomes can reflect noise in the environment or a change in the current rules. We should ignore noise but shift strategy after rule changes. How we learn to do this is unclear, but one possibility is that it relies on learning to learn in uncertain environments. We propose that acquisition of latent task structure during learning to…

Representation learning via Dual-Autoencoder for recommendation.

PubMed

Zhuang, Fuzhen; Zhang, Zhiqiang; Qian, Mingda; Shi, Chuan; Xie, Xing; He, Qing

2017-06-01

Recommendation has provoked vast amount of attention and research in recent decades. Most previous works employ matrix factorization techniques to learn the latent factors of users and items. And many subsequent works consider external information, e.g., social relationships of users and items' attributions, to improve the recommendation performance under the matrix factorization framework. However, matrix factorization methods may not make full use of the limited information from rating or check-in matrices, and achieve unsatisfying results. Recently, deep learning has proven able to learn good representation in natural language processing, image classification, and so on. Along this line, we propose a new representation learning framework called Recommendation via Dual-Autoencoder (ReDa). In this framework, we simultaneously learn the new hidden representations of users and items using autoencoders, and minimize the deviations of training data by the learnt representations of users and items. Based on this framework, we develop a gradient descent method to learn hidden representations. Extensive experiments conducted on several real-world data sets demonstrate the effectiveness of our proposed method compared with state-of-the-art matrix factorization based methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

Individual Differences in a Positional Learning Task across the Adult Lifespan

ERIC Educational Resources Information Center

Rast, Philippe; Zimprich, Daniel

2010-01-01

This study aimed at modeling individual and average non-linear trajectories of positional learning using a structured latent growth curve approach. The model is based on an exponential function which encompasses three parameters: Initial performance, learning rate, and asymptotic performance. These learning parameters were compared in a positional…

Latent constructs of the autobiographical memory questionnaire: a recollection-belief model of autobiographical experience.

PubMed

Fitzgerald, Joseph M; Broadbridge, Carissa L

2013-01-01

Many researchers employ single-item scales of subjective experiences such as imagery and confidence to assess autobiographical memory. We tested the hypothesis that four latent constructs, recollection, belief, impact, and rehearsal, account for the variance in commonly used scales across four different types of autobiographical memory: earliest childhood memory, cue word memory of personal experience, highly vivid memory, and most stressful memory. Participants rated each memory on scales hypothesised to be indicators of one of four latent constructs. Multi-group confirmatory factor analyses and structural analyses confirmed the similarity of the latent constructs of recollection, belief, impact, and rehearsal, as well as the similarity of the structural relationships among those constructs across memory type. The observed pattern of mean differences between the varieties of autobiographical experiences was consistent with prior research and theory in the study of autobiographical memory.

Dorsal bundle lesions do not affect latent inhibition of conditioned suppression.

PubMed

Tsaltas, E; Preston, G C; Rawlins, J N; Winocur, G; Gray, J A

1984-01-01

Three experiments are reported which examine the effects of lesions of the dorsal ascending noradrenergic bundle (DB) on latent inhibition using a conditioned suppression procedure in rats. In none of the experiments did the DB lesion have any effect, despite changes in the extent of latent inhibition and in the control procedures used to assess it. The results are discussed in relation to the attentional theory of DB function.

Latent Class Analysis of Students' Mathematics Learning Strategies and the Relationship between Learning Strategy and Mathematical Literacy

ERIC Educational Resources Information Center

Lin, Su-Wei; Tai, Wen-Chun

2015-01-01

This study investigated how various mathematics learning strategies affect the mathematical literacy of students. The data for this study were obtained from the 2012 Programme for International Student Assessment (PISA) data of Taiwan. The PISA learning strategy survey contains three types of learning strategies: elaboration, control, and…

Topic Model for Graph Mining.

PubMed

Xuan, Junyu; Lu, Jie; Zhang, Guangquan; Luo, Xiangfeng

2015-12-01

Graph mining has been a popular research area because of its numerous application scenarios. Many unstructured and structured data can be represented as graphs, such as, documents, chemical molecular structures, and images. However, an issue in relation to current research on graphs is that they cannot adequately discover the topics hidden in graph-structured data which can be beneficial for both the unsupervised learning and supervised learning of the graphs. Although topic models have proved to be very successful in discovering latent topics, the standard topic models cannot be directly applied to graph-structured data due to the "bag-of-word" assumption. In this paper, an innovative graph topic model (GTM) is proposed to address this issue, which uses Bernoulli distributions to model the edges between nodes in a graph. It can, therefore, make the edges in a graph contribute to latent topic discovery and further improve the accuracy of the supervised and unsupervised learning of graphs. The experimental results on two different types of graph datasets show that the proposed GTM outperforms the latent Dirichlet allocation on classification by using the unveiled topics of these two models to represent graphs.

The nature of expertise in fingerprint examiners.

PubMed

Busey, Thomas A; Parada, Francisco J

2010-04-01

Latent print examinations involve a complex set of psychological and cognitive processes. This article summarizes existing work that has addressed how training and experience creates changes in latent print examiners. Experience appears to improve overall accuracy, increase visual working memory, and lead to configural processing of upright fingerprints. Experts also demonstrate a narrower visual filter and, as a group, tend to show greater consistency when viewing ink prints. These findings address recent criticisms of latent print evidence, but many open questions still exist. Cognitive scientists are well positioned to conduct studies that will improve the training and practices of latent print examiners, and suggestions for becoming involved in fingerprint research are provided.

Scalable non-negative matrix tri-factorization.

PubMed

Čopar, Andrej; Žitnik, Marinka; Zupan, Blaž

2017-01-01

Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining. Matrix factorization learns a latent data model that takes a data matrix and transforms it into a latent feature space enabling generalization, noise removal and feature discovery. However, factorization algorithms are numerically intensive, and hence there is a pressing challenge to scale current algorithms to work with large datasets. Our focus in this paper is matrix tri-factorization, a popular method that is not limited by the assumption of standard matrix factorization about data residing in one latent space. Matrix tri-factorization solves this by inferring a separate latent space for each dimension in a data matrix, and a latent mapping of interactions between the inferred spaces, making the approach particularly suitable for biomedical data mining. We developed a block-wise approach for latent factor learning in matrix tri-factorization. The approach partitions a data matrix into disjoint submatrices that are treated independently and fed into a parallel factorization system. An appealing property of the proposed approach is its mathematical equivalence with serial matrix tri-factorization. In a study on large biomedical datasets we show that our approach scales well on multi-processor and multi-GPU architectures. On a four-GPU system we demonstrate that our approach can be more than 100-times faster than its single-processor counterpart. A general approach for scaling non-negative matrix tri-factorization is proposed. The approach is especially useful parallel matrix factorization implemented in a multi-GPU environment. We expect the new approach will be useful in emerging procedures for latent factor analysis, notably for data integration, where many large data matrices need to be collectively factorized.

Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes.

PubMed

Alaa, Ahmed M; Yoon, Jinsung; Hu, Scott; van der Schaar, Mihaela

2018-01-01

In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit admissions for clinically deteriorating patients. The risk scoring system is based on the idea of sequential hypothesis testing under an uncertain time horizon. The system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process experts, where each expert models the physiological data streams associated with a specific patient subtype. Transfer learning techniques are used to learn the relationship between a patient's latent subtype and her static admission information (e.g., age, gender, transfer status, ICD-9 codes, etc). Experiments conducted on data from a heterogeneous cohort of 6321 patients admitted to Ronald Reagan UCLA medical center show that our score significantly outperforms the currently deployed risk scores, such as the Rothman index, MEWS, APACHE, and SOFA scores, in terms of timeliness, true positive rate, and positive predictive value. Our results reflect the importance of adopting the concepts of personalized medicine in critical care settings; significant accuracy and timeliness gains can be achieved by accounting for the patients' heterogeneity. The proposed risk scoring methodology can confer huge clinical and social benefits on a massive number of critically ill inpatients who exhibit adverse outcomes including, but not limited to, cardiac arrests, respiratory arrests, and septic shocks.

Analysis and Visualization of Relations in eLearning

NASA Astrophysics Data System (ADS)

Dráždilová, Pavla; Obadi, Gamila; Slaninová, Kateřina; Martinovič, Jan; Snášel, Václav

The popularity of eLearning systems is growing rapidly; this growth is enabled by the consecutive development in Internet and multimedia technologies. Web-based education became wide spread in the past few years. Various types of learning management systems facilitate development of Web-based courses. Users of these courses form social networks through the different activities performed by them. This chapter focuses on searching the latent social networks in eLearning systems data. These data consist of students activity records wherein latent ties among actors are embedded. The social network studied in this chapter is represented by groups of students who have similar contacts and interact in similar social circles. Different methods of data clustering analysis can be applied to these groups, and the findings show the existence of latent ties among the group members. The second part of this chapter focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships as well as the amount of independent groups in a given network. When applied to the field of eLearning, data visualization simplifies the process of monitoring the study activities of individuals or groups, as well as the planning of educational curriculum, the evaluation of study processes, etc.

Transgenerational latent early-life associated regulation unites environment and genetics across generations

PubMed Central

Lahiri, Debomoy K; Maloney, Bryan; Bayon, Baindu L; Chopra, Nipun; White, Fletcher A; Greig, Nigel H; Nurnberger, John I

2016-01-01

The origin of idiopathic diseases is still poorly understood. The latent early-life associated regulation (LEARn) model unites environmental exposures and gene expression while providing a mechanistic underpinning for later-occurring disorders. We propose that this process can occur across generations via transgenerational LEARn (tLEARn). In tLEARn, each person is a ‘unit’ accumulating preclinical or subclinical ‘hits’ as in the original LEARn model. These changes can then be epigenomically passed along to offspring. Transgenerational accumulation of ‘hits’ determines a sporadic disease state. Few significant transgenerational hits would accompany conception or gestation of most people, but these may suffice to ‘prime’ someone to respond to later-life hits. Hits need not produce symptoms or microphenotypes to have a transgenerational effect. Testing tLEARn requires longitudinal approaches. A recently proposed longitudinal epigenome/envirome-wide association study would unite genetic sequence, epigenomic markers, environmental exposures, patient personal history taken at multiple time points and family history. PMID:26950428

A unified framework of image latent feature learning on Sina microblog

NASA Astrophysics Data System (ADS)

Wei, Jinjin; Jin, Zhigang; Zhou, Yuan; Zhang, Rui

2015-10-01

Large-scale user-contributed images with texts are rapidly increasing on the social media websites, such as Sina microblog. However, the noise and incomplete correspondence between the images and the texts give rise to the difficulty in precise image retrieval and ranking. In this paper, a hypergraph-based learning framework is proposed for image ranking, which simultaneously utilizes visual feature, textual content and social link information to estimate the relevance between images. Representing each image as a vertex in the hypergraph, complex relationship between images can be reflected exactly. Then updating the weight of hyperedges throughout the hypergraph learning process, the effect of different edges can be adaptively modulated in the constructed hypergraph. Furthermore, the popularity degree of the image is employed to re-rank the retrieval results. Comparative experiments on a large-scale Sina microblog data-set demonstrate the effectiveness of the proposed approach.

Spectral Learning for Supervised Topic Models.

PubMed

Ren, Yong; Wang, Yining; Zhu, Jun

2018-03-01

Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on variational approximation or Monte Carlo sampling, which often suffers from the local minimum defect. Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees. This paper investigates the possibility of applying spectral methods to recover the parameters of supervised LDA (sLDA). We first present a two-stage spectral method, which recovers the parameters of LDA followed by a power update method to recover the regression model parameters. Then, we further present a single-phase spectral algorithm to jointly recover the topic distribution matrix as well as the regression weights. Our spectral algorithms are provably correct and computationally efficient. We prove a sample complexity bound for each algorithm and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the spectral algorithms. In fact, our results on a large-scale review rating dataset demonstrate that our single-phase spectral algorithm alone gets comparable or even better performance than state-of-the-art methods, while previous work on spectral methods has rarely reported such promising performance.

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis

PubMed Central

BOYSAN, Murat

2014-01-01

Introduction There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Method Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson’s product-moment correlation coefficients. Results The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. Conclusion It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism. PMID:28360635

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis.

PubMed

Boysan, Murat

2014-09-01

There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson's product-moment correlation coefficients. The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism.

Simulation of LD Identification Accuracy Using a Pattern of Processing Strengths and Weaknesses Method with Multiple Measures

ERIC Educational Resources Information Center

Miciak, Jeremy; Taylor, W. Pat; Stuebing, Karla K.; Fletcher, Jack M.

2018-01-01

We investigated the classification accuracy of learning disability (LD) identification methods premised on the identification of an intraindividual pattern of processing strengths and weaknesses (PSW) method using multiple indicators for all latent constructs. Known LD status was derived from latent scores; values at the observed level identified…

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-11-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. Copyright © 2014 Elsevier Inc. All rights reserved.

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-01-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)1, a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. PMID:25042445

Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

PubMed

Huang, Yan; Wang, Wei; Wang, Liang; Tan, Tieniu

2017-09-01

Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.

Timing of hospital admission in labour: latent versus active phase, mode of birth and intrapartum interventions. A correlational study.

PubMed

Rota, A; Antolini, L; Colciago, E; Nespoli, A; Borrelli, S E; Fumagalli, S

2017-10-17

Hospitalization of women in latent labour often leads to a cascade of unnecessary intrapartum interventions, to avoid potential disadvantages the recommendation should be to stay at home to improve women's experience and perinatal outcomes. The primary aim of this study was to investigate the association between hospital admission diagnosis (latent vs active phase) and mode of birth. The secondary aim was to explore the relationship between hospital admission diagnosis, intrapartum intervention rates and maternal/neonatal outcomes. A correlational study was conducted in a large Italian maternity hospital. Data from January 2013 to December 2014 were collected from the hospital electronic records. 1.446 records of low risk women were selected. These were dichotomized into two groups based on admission diagnosis: 'latent phase' or 'active phase' of labour. 52.7% of women were admitted in active labour and 47.3% in the latent phase. Women in the latent phase group were more likely to experience a caesarean section or an instrumental birth, artificial rupture of membranes, oxytocin augmentation and epidural analgesia. Admission in the latent phase was associated with higher intrapartum interventions, which were statistically correlated to the mode of birth. Women admitted in the latent phase were more likely to experience intrapartum interventions, which increase the probability of caesarean section. Maternity services should be organized around women and families needs, providing early labour support, to enable women to feel reassured facilitating their admission in labour to avoid the cascade of intrapartum interventions which increases the risk of caesarean section. Copyright © 2017 Australian College of Midwives. All rights reserved.

Dorsal Hippocampus Function in Learning and Expressing a Spatial Discrimination

ERIC Educational Resources Information Center

White, Norman M.; Gaskin, Stephane

2006-01-01

Learning to discriminate between spatial locations defined by two adjacent arms of a radial maze in the conditioned cue preference paradigm requires two kinds of information: latent spatial learning when the rats explore the maze with no food available, and learning about food availability in two spatial locations when the rats are then confined…

Correlates of Individual, and Age-Related, Differences in Short-Term Learning

ERIC Educational Resources Information Center

Zhang, Zhiyong; Davis, Hasker P.; Salthouse, Timothy A.; Tucker-Drob, Elliot M.

2007-01-01

Latent growth models were applied to data on multitrial verbal and spatial learning tasks from two independent studies. Although significant individual differences in both initial level of performance and subsequent learning were found in both tasks, age differences were found only in mean initial level, and not in mean learning. In neither task…

Predicting Learned Helplessness Based on Personality

ERIC Educational Resources Information Center

Maadikhah, Elham; Erfani, Nasrollah

2014-01-01

Learned helplessness as a negative motivational state can latently underlie repeated failures and create negative feelings toward the education as well as depression in students and other members of a society. The purpose of this paper is to predict learned helplessness based on students' personality traits. The research is a predictive…

Longitudinal Examination of Procrastination and Anxiety, and Their Relation to Self-Efficacy for Self- Regulated Learning: Latent Growth Curve Modeling

ERIC Educational Resources Information Center

Yerdelen, Sündüs; McCaffrey, Adam; Klassen, Robert M.

2016-01-01

This study investigated the longitudinal association between students' anxiety and procrastination and the relation of self-efficacy for self-regulation to these constructs. Latent Growth Curve Modeling was used to analyze data gathered from 182 undergraduate students (134 female, 48 male) at 4 times during a semester. Our results showed that…

Latent Profiles of Reading and Language and Their Association with Standardized Reading Outcomes in Kindergarten through 10th Grade

ERIC Educational Resources Information Center

Foorman, Barbara R.; Petscher, Yaacov; Stanley, Christopher

2016-01-01

The idea of targeting reading instruction to profiles of students' strengths and weaknesses in component skills is central to teaching. However, these profiles are often based on unreliable descriptions of students' oral reading errors, text reading levels, or learning profiles. This research utilized latent profile analysis (LPA) to examine…

Food deprivation enhances both autoshaping and autoshaping impairment by a latent inhibition procedure.

PubMed

Sparber, S B; Bollweg, G L; Messing, R B

1991-02-01

The influence of food deprivation on acquisition of autoshaped operant behavior was measured. In one study separate groups of young, male rats that were deprived to 75%, 80%, 85%, 90%, and 95% of ad lib weight were subjected to an autoshaping procedure in which a 6 s delay was interposed between lever retraction (which occurred when rats made a lever touch, or automatically after 15 s) and food pellet delivery. In a second study, groups of rats were deprived to 80% or 90% of ad lib weight prior to testing in a latent inhibition variation of the same autoshaping procedure. This was done to determine if greater food deprivation would enhance learning which, because of the latent inhibition manipulation, is manifest as less lever-directed behavior. Greater food deprivation was associated both with fast acquisition of autoshaped lever responding and with more reliable failure to increase lever responding in the latent inhibition paradigm. Thus, increasing food deprivation was associated with enhanced acquisition regardless of whether the required performance was an increase or a failure to increase the same behavior, indicating a specific effect on learning. Copyright © 1991. Published by Elsevier B.V.

The Latent Variable Approach as Applied to Transitive Reasoning

ERIC Educational Resources Information Center

Bouwmeester, Samantha; Vermunt, Jeroen K.; Sijtsma, Klaas

2012-01-01

We discuss the limitations of hypothesis testing using (quasi-) experiments in the study of cognitive development and suggest latent variable modeling as a viable alternative to experimentation. Latent variable models allow testing a theory as a whole, incorporating individual differences with respect to developmental processes or abilities in the…

Developing a Learning Progression of Buoyancy to Model Conceptual Change: A Latent Class and Rule Space Model Analysis

NASA Astrophysics Data System (ADS)

Gao, Yizhu; Zhai, Xiaoming; Andersson, Björn; Zeng, Pingfei; Xin, Tao

2018-06-01

We applied latent class analysis and the rule space model to verify the cumulative characteristic of conceptual change by developing a learning progression for buoyancy. For this study, we first abstracted seven attributes of buoyancy and then developed a hypothesized learning progression for buoyancy. A 14-item buoyancy instrument was administered to 1089 8th grade students to verify and refine the learning progression. The results suggest four levels of progression during conceptual change when 8th grade students understand buoyancy. Students at level 0 can only master Density. When students progress to level 1, they can grasp Direction, Identification, Submerged volume, and Relative density on the basis of the prior level. Then, students gradually master Archimedes' theory as they reach level 2. The most advanced students can further grasp Relation with motion and arrive at level 3. In addition, this four-level learning progression can be accounted for by the Qualitative-Quantitative-Integrative explanatory model.

Associations between the Classroom Learning Environment and Student Engagement in Learning 2: A Structural Equation Modelling Approach

ERIC Educational Resources Information Center

Harbaugh, Allen G.; Cavanagh, Robert F.

2012-01-01

This report is about the second of two phases in an investigation into associations between student engagement in classroom learning and the classroom-learning environment. Whereas the first phase utilized Rasch modelling (Cavanagh, 2012), this report uses latent variable modelling to explore the data. The investigations in both phases of this…

Participatory Equity and Student Outcomes in Living-Learning Programs of Differing Thematic Types

ERIC Educational Resources Information Center

Soldner, Matthew Edward

2011-01-01

This study evaluated participatory equity in varying thematic types of living-learning programs and, for a subset of student group x program type combinations found to be below equity, used latent mean modeling to determine whether statistically significant mean differences existed between the outcome scores of living-learning participants and…

Do Online Learning Patterns Exhibit Regional and Demographic Differences?

ERIC Educational Resources Information Center

Hsieh, Tsui-Chuan; Yang, Chyan

2012-01-01

This paper used a multi-level latent class model to evaluate whether online learning patterns exhibit regional differences and demographics. This study discovered that the Internet learning pattern consists of five segments, and the region of Taiwan is divided into two segments and further found that both the user and the regional segments are…

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

ERIC Educational Resources Information Center

Rast, Philippe

2011-01-01

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

Prevention of Substance Use among Adolescents through Social and Emotional Training in School: A Latent-Class Analysis of a Five-Year Intervention in Sweden

ERIC Educational Resources Information Center

Kimber, Birgitta; Sandell, Rolf

2009-01-01

The study considers the impact of a program for social and emotional learning in Swedish schools on use of drugs, volatile substances, alcohol and tobacco. The program was evaluated in an effectiveness study. Intervention students were compared longitudinally with non-intervention students using nonparametric latent class analysis to identify…

An All-Fragments Grammar for Simple and Accurate Parsing

DTIC Science & Technology

2012-03-21

Tsujii. Probabilistic CFG with latent annotations. In Proceedings of ACL, 2005. Slav Petrov and Dan Klein. Improved Inference for Unlexicalized Parsing. In...Proceedings of NAACL-HLT, 2007. Slav Petrov and Dan Klein. Sparse Multi-Scale Grammars for Discriminative Latent Variable Parsing. In Proceedings of...EMNLP, 2008. Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein. Learning Accurate, Compact, and Interpretable Tree Annotation. In Proceedings

Asymmetric latent semantic indexing for gene expression experiments visualization.

PubMed

González, Javier; Muñoz, Alberto; Martos, Gabriel

2016-08-01

We propose a new method to visualize gene expression experiments inspired by the latent semantic indexing technique originally proposed in the textual analysis context. By using the correspondence word-gene document-experiment, we define an asymmetric similarity measure of association for genes that accounts for potential hierarchies in the data, the key to obtain meaningful gene mappings. We use the polar decomposition to obtain the sources of asymmetry of the similarity matrix, which are later combined with previous knowledge. Genetic classes of genes are identified by means of a mixture model applied in the genes latent space. We describe the steps of the procedure and we show its utility in the Human Cancer dataset.

Topic detection using paragraph vectors to support active learning in systematic reviews.

PubMed

Hashimoto, Kazuma; Kontonatsios, Georgios; Miwa, Makoto; Ananiadou, Sophia

2016-08-01

Systematic reviews require expert reviewers to manually screen thousands of citations in order to identify all relevant articles to the review. Active learning text classification is a supervised machine learning approach that has been shown to significantly reduce the manual annotation workload by semi-automating the citation screening process of systematic reviews. In this paper, we present a new topic detection method that induces an informative representation of studies, to improve the performance of the underlying active learner. Our proposed topic detection method uses a neural network-based vector space model to capture semantic similarities between documents. We firstly represent documents within the vector space, and cluster the documents into a predefined number of clusters. The centroids of the clusters are treated as latent topics. We then represent each document as a mixture of latent topics. For evaluation purposes, we employ the active learning strategy using both our novel topic detection method and a baseline topic model (i.e., Latent Dirichlet Allocation). Results obtained demonstrate that our method is able to achieve a high sensitivity of eligible studies and a significantly reduced manual annotation cost when compared to the baseline method. This observation is consistent across two clinical and three public health reviews. The tool introduced in this work is available from https://nactem.ac.uk/pvtopic/. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Semantic contextual cuing and visual attention.

PubMed

Goujon, Annabelle; Didierjean, André; Marmèche, Evelyne

2009-02-01

Since M. M. Chun and Y. Jiang's (1998) original study, a large body of research based on the contextual cuing paradigm has shown that the visuocognitive system is capable of capturing certain regularities in the environment in an implicit way. The present study investigated whether regularities based on the semantic category membership of the context can be learned implicitly and whether that learning depends on attention. The contextual cuing paradigm was used with lexical displays in which the semantic category of the contextual words either did or did not predict the target location. Experiments 1 and 2 revealed that implicit contextual cuing effects can be extended to semantic category regularities. Experiments 3 and 4 indicated an implicit contextual cuing effect when the predictive context appeared in an attended color but not when the predictive context appeared in an ignored color. However, when the previously ignored context suddenly became attended, it immediately facilitated performance. In contrast, when the previously attended context suddenly became ignored, no benefit was observed. Results suggest that the expression of implicit semantic knowledge depends on attention but that latent learning can nevertheless take place outside the attentional field. Copyright 2009 APA, all rights reserved.

Prefrontal Cortical GABA Transmission Modulates Discrimination and Latent Inhibition of Conditioned Fear: Relevance for Schizophrenia

PubMed Central

Piantadosi, Patrick T; Floresco, Stan B

2014-01-01

Inhibitory gamma-aminobutyric acid (GABA) transmission within the prefrontal cortex (PFC) regulates numerous functions, and perturbations in GABAergic transmission within this region have been proposed to contribute to some of the cognitive and behavioral abnormalities associated with disorders such as schizophrenia. These abnormalities include deficits in emotional regulation and aberrant attributions of affective salience. Yet, how PFC GABA regulates these types of emotional processes are unclear. To address this issue, we investigated the contribution of PFC GABA transmission to different aspects of Pavlovian emotional learning in rats using translational discriminative fear conditioning and latent inhibition (LI) assays. Reducing prelimbic PFC GABAA transmission via infusions of the antagonist bicuculline before the acquisition or expression of fear conditioning eliminated the ability to discriminate between an aversive conditioned stimulus (CS+) paired with footshock vs a neutral CS–, resembling similar deficits observed in schizophrenic patients. In a separate experiment, blockade of PFC GABAA receptors before CS preexposure (PE) and conditioning did not affect subsequent expression of LI, but did enhance fear in rats that were not preexposed to the CS. In contrast, PFC GABA-blockade before a fear expression test disrupted the recall of learned irrelevance and abolished LI. These data suggest that normal PFC GABA transmission is critical for regulating and mitigating multiple aspects of aversive learning, including discrimination between fear vs safety signals and recall of information about the irrelevance of stimuli. Furthermore, they suggest that similar deficits in emotional regulation observed in schizophrenia may be driven in part by deficient PFC GABA activity. PMID:24784549

Prefrontal cortical GABA transmission modulates discrimination and latent inhibition of conditioned fear: relevance for schizophrenia.

PubMed

Piantadosi, Patrick T; Floresco, Stan B

2014-09-01

Inhibitory gamma-aminobutyric acid (GABA) transmission within the prefrontal cortex (PFC) regulates numerous functions, and perturbations in GABAergic transmission within this region have been proposed to contribute to some of the cognitive and behavioral abnormalities associated with disorders such as schizophrenia. These abnormalities include deficits in emotional regulation and aberrant attributions of affective salience. Yet, how PFC GABA regulates these types of emotional processes are unclear. To address this issue, we investigated the contribution of PFC GABA transmission to different aspects of Pavlovian emotional learning in rats using translational discriminative fear conditioning and latent inhibition (LI) assays. Reducing prelimbic PFC GABAA transmission via infusions of the antagonist bicuculline before the acquisition or expression of fear conditioning eliminated the ability to discriminate between an aversive conditioned stimulus (CS+) paired with footshock vs a neutral CS-, resembling similar deficits observed in schizophrenic patients. In a separate experiment, blockade of PFC GABAA receptors before CS preexposure (PE) and conditioning did not affect subsequent expression of LI, but did enhance fear in rats that were not preexposed to the CS. In contrast, PFC GABA-blockade before a fear expression test disrupted the recall of learned irrelevance and abolished LI. These data suggest that normal PFC GABA transmission is critical for regulating and mitigating multiple aspects of aversive learning, including discrimination between fear vs safety signals and recall of information about the irrelevance of stimuli. Furthermore, they suggest that similar deficits in emotional regulation observed in schizophrenia may be driven in part by deficient PFC GABA activity.

Verbal task demands are key in explaining the relationship between paired-associate learning and reading ability.

PubMed

Clayton, Francina J; Sears, Claire; Davis, Alice; Hulme, Charles

2018-07-01

Paired-associate learning (PAL) tasks measure the ability to form a novel association between a stimulus and a response. Performance on such tasks is strongly associated with reading ability, and there is increasing evidence that verbal task demands may be critical in explaining this relationship. The current study investigated the relationships between different forms of PAL and reading ability. A total of 97 children aged 8-10 years completed a battery of reading assessments and six different PAL tasks (phoneme-phoneme, visual-phoneme, nonverbal-nonverbal, visual-nonverbal, nonword-nonword, and visual-nonword) involving both familiar phonemes and unfamiliar nonwords. A latent variable path model showed that PAL ability is captured by two correlated latent variables: auditory-articulatory and visual-articulatory. The auditory-articulatory latent variable was the stronger predictor of reading ability, providing support for a verbal account of the PAL-reading relationship. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Multi-view non-negative tensor factorization as relation learning in healthcare data.

PubMed

Hang Wu; Wang, May D

2016-08-01

Discovering patterns in co-occurrences data between objects and groups of concepts is a useful task in many domains, such as healthcare data analysis, information retrieval, and recommender systems. These relational representations come from objects' behaviors in different views, posing a challenging task of integrating information from these views to uncover the shared latent structures. The problem is further complicated by the high dimension of data and the large ratio of missing data. We propose a new paradigm of learning semantic relations using tensor factorization, by jointly factorizing multi-view tensors and searching for a consistent underlying semantic space across each views. We formulate the idea as an optimization problem and propose efficient optimization algorithms, with a special treatment of missing data as well as high-dimensional data. Experiments results show the potential and effectiveness of our algorithms.

Determination of the Latent Heats and Triple Point of Perfluorocyclobutane

ERIC Educational Resources Information Center

Briggs, A. G.; Strachan, A. N.

1977-01-01

Proposes the use of Perfluorocyclobutane in physical chemistry courses to conduct experiments on latent heat, triple point temperatures and pressures, boiling points, and entropy of vaporization. (SL)

Measuring the Heats of Water.

ERIC Educational Resources Information Center

Hunt, James L.; Tegart, Tracy L.

1994-01-01

Uses common equipment (tea kettle and vacuum bottles) to precisely measure the specific heat, latent heat of fusion, and latent heat of vaporization of water. Provides descriptions for all three experiments. (MVL)

Techniques and Practices in the Training of Digital Operator Skills

DTIC Science & Technology

2007-09-01

changes in environmental stimuli. Early behaviorists strongly opposed the study of any sort of mental event, but more recent behaviorists like Albert ... Bandura and Edward Tolman recognized that processes like vicarious learning and latent learning could not be explained unless some unobservable

Intuitive experimentation in the physical world.

PubMed

Bramley, Neil R; Gerstenberg, Tobias; Tenenbaum, Joshua B; Gureckis, Todd M

2018-06-06

Many aspects of our physical environment are hidden. For example, it is hard to estimate how heavy an object is from visual observation alone. In this paper we examine how people actively "experiment" within the physical world to discover such latent properties. In the first part of the paper, we develop a novel framework for the quantitative analysis of the information produced by physical interactions. We then describe two experiments that present participants with moving objects in "microworlds" that operate according to continuous spatiotemporal dynamics similar to everyday physics (i.e., forces of gravity, friction, etc.). Participants were asked to interact with objects in the microworlds in order to identify their masses, or the forces of attraction/repulsion that governed their movement. Using our modeling framework, we find that learners who freely interacted with the physical system selectively produced evidence that revealed the physical property consistent with their inquiry goal. As a result, their inferences were more accurate than for passive observers and, in some contexts, for yoked participants who watched video replays of an active learner's interactions. We characterize active learners' actions into a range of micro-experiment strategies and discuss how these might be learned or generalized from past experience. The technical contribution of this work is the development of a novel analytic framework and methodology for the study of interactively learning about the physical world. Its empirical contribution is the demonstration of sophisticated goal directed human active learning in a naturalistic context. Copyright © 2018 Elsevier Inc. All rights reserved.

Epidemiologic methods lessons learned from environmental public health disasters: Chernobyl, the World Trade Center, Bhopal, and Graniteville, South Carolina.

PubMed

Svendsen, Erik R; Runkle, Jennifer R; Dhara, Venkata Ramana; Lin, Shao; Naboka, Marina; Mousseau, Timothy A; Bennett, Charles

2012-08-01

Environmental public health disasters involving hazardous contaminants may have devastating effects. While much is known about their immediate devastation, far less is known about long-term impacts of these disasters. Extensive latent and chronic long-term public health effects may occur. Careful evaluation of contaminant exposures and long-term health outcomes within the constraints imposed by limited financial resources is essential. Here, we review epidemiologic methods lessons learned from conducting long-term evaluations of four environmental public health disasters involving hazardous contaminants at Chernobyl, the World Trade Center, Bhopal, and Graniteville (South Carolina, USA). We found several lessons learned which have direct implications for the on-going disaster recovery work following the Fukushima radiation disaster or for future disasters. These lessons should prove useful in understanding and mitigating latent health effects that may result from the nuclear reactor accident in Japan or future environmental public health disasters.

Analyzing the Latent Emotional Transfer Pattern (LETP) of a Learning Community in an Online Peer-Assessment Activity

ERIC Educational Resources Information Center

Hou, Huei-Tse; Cheng, Kun-Hung

2012-01-01

Peer assessment is utilized extensively in digital learning, and many studies have examined the positive influences of online peer assessment on learning (eg, Tseng & Tsai, 2007). However, other studies indicate that this type of activity has negative influences. For example, students may question the fairness of an assessment or disagree with…

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

PubMed

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

2017-07-01

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

Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering.

PubMed

Peng, Xi; Yu, Zhiding; Yi, Zhang; Tang, Huajin

2017-04-01

Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intrasubspace data points). Recent works achieve good performance by modeling errors into their objective functions to remove the errors from the inputs. However, these approaches face the limitations that the structure of errors should be known prior and a complex convex problem must be solved. In this paper, we present a novel method to eliminate the effects of the errors from the projection space (representation) rather than from the input space. We first prove that l 1 -, l 2 -, l ∞ -, and nuclear-norm-based linear projection spaces share the property of intrasubspace projection dominance, i.e., the coefficients over intrasubspace data points are larger than those over intersubspace data points. Based on this property, we introduce a method to construct a sparse similarity graph, called L2-graph. The subspace clustering and subspace learning algorithms are developed upon L2-graph. We conduct comprehensive experiment on subspace learning, image clustering, and motion segmentation and consider several quantitative benchmarks classification/clustering accuracy, normalized mutual information, and running time. Results show that L2-graph outperforms many state-of-the-art methods in our experiments, including L1-graph, low rank representation (LRR), and latent LRR, least square regression, sparse subspace clustering, and locally linear representation.

Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval.

PubMed

Wang, Yang; Lin, Xuemin; Wu, Lin; Zhang, Wenjie

2017-03-01

Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and/or angles, and may, subsequently, yield very different results. In fact, dealing with the landmarks with low quality shapes caused by the photography of q-users is often nontrivial and has seldom been studied. In this paper, we propose a novel framework, namely, multi-query expansions, to retrieve semantically robust landmarks by two steps. First, we identify the top- k photos regarding the latent topics of a query landmark to construct multi-query set so as to remedy its possible low quality shape. For this purpose, we significantly extend the techniques of Latent Dirichlet Allocation. Then, motivated by the typical collaborative filtering methods, we propose to learn a collaborative deep networks-based semantically, nonlinear, and high-level features over the latent factor for landmark photo as the training set, which is formed by matrix factorization over collaborative user-photo matrix regarding the multi-query set. The learned deep network is further applied to generate the features for all the other photos, meanwhile resulting into a compact multi-query set within such space. Then, the final ranking scores are calculated over the high-level feature space between the multi-query set and all other photos, which are ranked to serve as the final ranking list of landmark retrieval. Extensive experiments are conducted on real-world social media data with both landmark photos together with their user information to show the superior performance over the existing methods, especially our recently proposed multi-query based mid-level pattern representation method [1].

A Rational Analysis of the Acquisition of Multisensory Representations

ERIC Educational Resources Information Center

Yildirim, Ilker; Jacobs, Robert A.

2012-01-01

How do people learn multisensory, or amodal, representations, and what consequences do these representations have for perceptual performance? We address this question by performing a rational analysis of the problem of learning multisensory representations. This analysis makes use of a Bayesian nonparametric model that acquires latent multisensory…

Student Satisfaction with Online Learning: Is It a Psychological Contract?

ERIC Educational Resources Information Center

Dziuban, Charles; Moskal, Patsy; Thompson, Jessica; Kramer, Lauren; DeCantis, Genevieve; Hermsdorfer, Andrea

2015-01-01

The authors explore the possible relationship between student satisfaction with online learning and the theory of psychological contracts. The study incorporates latent trait models using the image analysis procedure and computation of Anderson and Rubin factors scores with contrasts for students who are satisfied, ambivalent, or dissatisfied with…

Replicates in high dimensions, with applications to latent variable graphical models.

PubMed

Tan, Kean Ming; Ning, Yang; Witten, Daniela M; Liu, Han

2016-12-01

In classical statistics, much thought has been put into experimental design and data collection. In the high-dimensional setting, however, experimental design has been less of a focus. In this paper, we stress the importance of collecting multiple replicates for each subject in this setting. We consider learning the structure of a graphical model with latent variables, under the assumption that these variables take a constant value across replicates within each subject. By collecting multiple replicates for each subject, we are able to estimate the conditional dependence relationships among the observed variables given the latent variables. To test the null hypothesis of conditional independence between two observed variables, we propose a pairwise decorrelated score test. Theoretical guarantees are established for parameter estimation and for this test. We show that our proposal is able to estimate latent variable graphical models more accurately than some existing proposals, and apply the proposed method to a brain imaging dataset.

Clinical risk management in obstetrics.

PubMed

Holden, Deborah A; Quin, Maureen; Holden, Des P

2004-04-01

Over recent years there has been a growing appreciation that a small but significant proportion of patients experience (sometimes serious) adverse events in the hands of health care workers. Although research in this area is very much in its infancy there has been an increasing move towards applying principles of risk management from industry to health care organizations. With the particularly disastrous and costly nature of adverse outcomes in obstetrics it is appropriate to review clinical risk management issues in maternity. This review explores the appropriateness of applying lessons learned in industry to maternity. The classification of errors into individual and latent, or organizational, is examined. Furthermore, the way in which these errors can be identified and subsequently analysed, with examples from maternity units in the UK and USA, is discussed. The importance of an educational and supportive environment, rather than a blame culture, for both reporting of incidents and learning from adverse outcomes is emphasized. Improvement in patient experience of health care rests not just with improved treatments, but also with a reduction in the adverse events which occur in health care institutions. The principles by which risk can be identified prospectively and retrospectively, and the mechanisms for both local risk management and regional/national reporting and learning are considered.

Positive and negative affect produce opposing task-irrelevant stimulus preexposure effects.

PubMed

Lazar, Josef; Kaplan, Oren; Sternberg, Terri; Lubow, R E

2012-06-01

In three experiments, groups were exposed to either positive or negative affect video clips, after which they were presented with a series of task-irrelevant stimuli. In the subsequent test task, subjects were required to learn an association between the previously irrelevant stimulus and a consequence, and between a new stimulus and a consequence. Induced positive affect produced a latent inhibition effect (poorer evidence of learning with the previously irrelevant stimulus than with the novel stimulus). In opposition to this, induced negative affect resulted in better evidence of learning with a previously irrelevant stimulus than with a novel stimulus. In general, the opposing effects also were present in participants scoring high on self-report questionnaires of depression (Experiments 2 and 3). These unique findings were predicted and accounted for on the basis of two principles: (a) positive affect broadens the attentional field and negative affect contracts it; and (b) task-irrelevant stimuli are processed in two successive stages, the first encodes stimulus properties, and the second encodes stimulus relationships. The opposing influences of negative and positive mood on the processing of irrelevant stimuli have implications for the role of emotion in general theories of cognition, and possibly for resolving some of the inconsistent findings in research with schizophrenia patients.

Robust Measurement via A Fused Latent and Graphical Item Response Theory Model.

PubMed

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

2018-03-12

Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning. To provide more robust measurements, we propose an integrated approach by adding a graphical component to a multidimensional IRT model that can offset the effect of unknown local dependence. The new model contains a confirmatory latent variable component, which measures the targeted latent traits, and a graphical component, which captures the local dependence. An efficient proximal algorithm is proposed for the parameter estimation and structure learning of the local dependence. This approach can substantially improve the measurement, given no prior information on the local dependence structure. The model can be applied to measure both a unidimensional latent trait and multidimensional latent traits.

Chemical Composition of Latent Fingerprints by Gas Chromatography-Mass Spectrometry

ERIC Educational Resources Information Center

Hartzell-Baguley, Brittany; Hipp, Rachael E.; Morgan, Neal R.; Morgan, Stephen L.

2007-01-01

An experiment in which gas chromatography-mass spectrometry (GC-MS) is used for latent fingerprint extraction and analysis on glass beads or glass slides is conducted. The results determine that the fingerprint residues are gender dependent.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Internet gamblers: a latent class analysis of their behaviours and health experiences.

PubMed

Lloyd, Joanne; Doll, Helen; Hawton, Keith; Dutton, William H; Geddes, John R; Goodwin, Guy M; Rogers, Robert D

2010-09-01

In order to learn about the behaviours and health experiences of people who gamble on the Internet, we conducted an international online survey with respondents recruited via gambling and gambling-related websites. The mean (SD) age of the 4,125 respondents completing the survey was 35.5 (11.8) years, with 79.1% being male and 68.8% UK residents. Respondents provided demographic details and completed validated psychometric screening instruments for problem gambling, mood disturbances, as well as alcohol and substance misuse, and history of deliberate self harm. We applied latent class analysis to respondents' patterns of regular online gambling activities, and identified subgroups of individuals who used the Internet to gamble in different ways (L (2) = 44.27, bootstrap P = 0.07). We termed the characteristic profiles as 'non-to-minimal gamblers'; 'sports bettors'; 'casino & sports gamblers'; 'lottery players'; and 'multi-activity gamblers'. Furthermore, these subgroups of respondents differed on other demographic and psychological dimensions, with significant inter-cluster differences in proportion of individuals scoring above threshold for problem gambling, mood disorders and substance misuse, and history of deliberate self harm (all Chi (2)s > 23.4, all P-values <0.001). The 'casino & sports' and 'multi-activity-gamblers' clusters had the highest prevalence of mental disorder. Internet gamblers appear to be heterogeneous but composed of several subgroups, differing markedly on both demographic and clinical characteristics.

Verbal learning changes in older adults across 18 months.

PubMed

Zimprich, Daniel; Rast, Philippe

2009-07-01

The major aim of this study was to investigate individual changes in verbal learning across a period of 18 months. Individual differences in verbal learning have largely been neglected in the last years and, even more so, individual differences in change in verbal learning. The sample for this study comes from the Zurich Longitudinal Study on Cognitive Aging (ZULU; Zimprich et al., 2008a) and comprised 336 older adults in the age range of 65-80 years at first measurement occasion. In order to address change in verbal learning we used a latent change model of structured latent growth curves to account for the non-linearity of the verbal learning data. The individual learning trajectories were captured by a hyperbolic function which yielded three psychologically distinct parameters: initial performance, learning rate, and asymptotic performance. We found that average performance increased with respect to initial performance, but not in learning rate or in asymptotic performance. Further, variances and covariances remained stable across both measurement occasions, indicating that the amount of individual differences in the three parameters remained stable, as did the relationships among them. Moreover, older adults differed reliably in their amount of change in initial performance and asymptotic performance. Eventually, changes in asymptotic performance and learning rate were strongly negatively correlated. It thus appears as if change in verbal learning in old age is a constrained process: an increase in total learning capacity implies that it takes longer to learn. Together, these results point to the significance of individual differences in change of verbal learning in the elderly.

Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering.

PubMed

Wang, Yang; Wu, Lin

2018-07-01

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Surface energy and radiation balance systems - General description and improvements

NASA Technical Reports Server (NTRS)

Fritschen, Leo J.; Simpson, James R.

1989-01-01

Surface evaluation of sensible and latent heat flux densities and the components of the radiation balance were desired for various vegetative surfaces during the ASCOT84 experiment to compare with modeled results and to relate these values to drainage winds. Five battery operated data systems equipped with sensors to determine the above values were operated for 105 station days during the ASCOT84 experiment. The Bowen ratio energy balance technique was used to partition the available energy into the sensible and latent heat flux densities. A description of the sensors and battery operated equipment used to collect and process the data is presented. In addition, improvements and modifications made since the 1984 experiment are given. Details of calculations of soil heat flow at the surface and an alternate method to calculate sensible and latent heat flux densities are provided.

Odor Learning and Its Experience-Dependent Modulation in the South American Native Bumblebee Bombus atratus (Hymenoptera: Apidae).

PubMed

Palottini, Florencia; Estravis Barcala, María C; Farina, Walter M

2018-01-01

Learning about olfactory stimuli is essential in bumblebees' life since it is involved in orientation, recognition of nest sites, foraging efficiency and food yield for the colony as a whole. To evaluate associative learning abilities in bees under controlled environmental conditions, the proboscis extension response (PER) assay is a well-established method used in honey bees, stingless bees and successfully adapted to bumblebees of the genus Bombus . However, studies on the learning capacity of Bombus atratus (Hymenoptera: Apidae), one of the most abundant native species in South America, are non-existent. In this study, we examined the cognitive abilities of worker bees of this species, carrying out an olfactory PER conditioning experiment. Bumblebees were able to learn a pure odor when it was presented in paired association with sugared reward, but not when odor and reward were presented in an unpaired manner. Furthermore, if the bees were preexposed to the conditioned odor, the results differed depending on the presence of the scent either as a volatile in the rearing environment or diluted in the food. A decrement in learning performance results from the non-reinforced pre-exposure to the to-be-conditioned odor, showing a latent inhibition phenomenon. However, if the conditioned odor has been previously offered diluted in sugared reward, the food odor acts as a stimulus that improves the learning performance during PER conditioning. The native bumblebee B. atratus is thus a new hymenopteran species capable of being trained under controlled experimental conditions. Since it is an insect increasingly reared for pollination service, this knowledge could be useful in its management in crops.

Odor Learning and Its Experience-Dependent Modulation in the South American Native Bumblebee Bombus atratus (Hymenoptera: Apidae)

PubMed Central

Palottini, Florencia; Estravis Barcala, María C.; Farina, Walter M.

2018-01-01

Learning about olfactory stimuli is essential in bumblebees’ life since it is involved in orientation, recognition of nest sites, foraging efficiency and food yield for the colony as a whole. To evaluate associative learning abilities in bees under controlled environmental conditions, the proboscis extension response (PER) assay is a well-established method used in honey bees, stingless bees and successfully adapted to bumblebees of the genus Bombus. However, studies on the learning capacity of Bombus atratus (Hymenoptera: Apidae), one of the most abundant native species in South America, are non-existent. In this study, we examined the cognitive abilities of worker bees of this species, carrying out an olfactory PER conditioning experiment. Bumblebees were able to learn a pure odor when it was presented in paired association with sugared reward, but not when odor and reward were presented in an unpaired manner. Furthermore, if the bees were preexposed to the conditioned odor, the results differed depending on the presence of the scent either as a volatile in the rearing environment or diluted in the food. A decrement in learning performance results from the non-reinforced pre-exposure to the to-be-conditioned odor, showing a latent inhibition phenomenon. However, if the conditioned odor has been previously offered diluted in sugared reward, the food odor acts as a stimulus that improves the learning performance during PER conditioning. The native bumblebee B. atratus is thus a new hymenopteran species capable of being trained under controlled experimental conditions. Since it is an insect increasingly reared for pollination service, this knowledge could be useful in its management in crops. PMID:29755391

Learning to ignore: A modeling study of a decremental cholinergic pathway and its influence on attention and learning

PubMed Central

Oros, Nicolas; Chiba, Andrea A.; Nitz, Douglas A.; Krichmar, Jeffrey L.

2014-01-01

Learning to ignore irrelevant stimuli is essential to achieving efficient and fluid attention, and serves as the complement to increasing attention to relevant stimuli. The different cholinergic (ACh) subsystems within the basal forebrain regulate attention in distinct but complementary ways. ACh projections from the substantia innominata/nucleus basalis region (SI/nBM) to the neocortex are necessary to increase attention to relevant stimuli and have been well studied. Lesser known are ACh projections from the medial septum/vertical limb of the diagonal band (MS/VDB) to the hippocampus and the cingulate that are necessary to reduce attention to irrelevant stimuli. We developed a neural simulation to provide insight into how ACh can decrement attention using this distinct pathway from the MS/VDB. We tested the model in behavioral paradigms that require decremental attention. The model exhibits behavioral effects such as associative learning, latent inhibition, and persisting behavior. Lesioning the MS/VDB disrupts latent inhibition, and drastically increases perseverative behavior. Taken together, the model demonstrates that the ACh decremental pathway is necessary for appropriate learning and attention under dynamic circumstances and suggests a canonical neural architecture for decrementing attention. PMID:24443744

Ethnic differences in longitudinal latent verbal profiles in the millennium cohort study.

PubMed

Zilanawala, Afshin; Kelly, Yvonne; Sacker, Amanda

2016-12-01

Development of verbal skills during early childhood and school age years is consequential for children's educational achievement and adult outcomes. We examine ethnic differences in longitudinal latent verbal profiles and assess the contribution of family process and family resource factors to observed differences. Using data from the UK Millennium Cohort Study and the latent profile analysis, we estimate longitudinal latent verbal profiles using verbal skills measured 4 times from age 3-11 years. We investigate the odds of verbal profiles by ethnicity (reported in infancy), and the extent observed differences are mediated by the home learning environment, family routines, and psychosocial environment (measured at age 3). Indian children were twice as likely (OR = 2.14, CI: 1.37-3.33) to be in the high achieving profile, compared to White children. Socioeconomic markers attenuated this advantage to nonsignificance. Pakistani and Bangladeshi children were significantly more likely to be in the low performing group (OR = 2.23, CI: 1.61-3.11; OR = 3.37, CI: 2.20-5.17, respectively). Socioeconomic and psychosocial factors had the strongest mediating influence on the association between lower achieving profiles and Pakistani children, whereas for Bangladeshi children, there was mediation by the home learning environment, family routines, and psychosocial factors. Family process and resource factors explain ethnic differences in longitudinal latent verbal profiles. Family resources explain verbal advantages for Indian children, whereas a range of home environment and socioeconomic factors explain disparities for Pakistani and Bangladeshi children. Future policy initiatives focused on reducing ethnic disparities in children's development should consider supporting and enhancing family resources and processes. © The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association.

Ethnic differences in longitudinal latent verbal profiles in the millennium cohort study*

PubMed Central

Kelly, Yvonne; Sacker, Amanda

2016-01-01

Background: Development of verbal skills during early childhood and school age years is consequential for children’s educational achievement and adult outcomes. We examine ethnic differences in longitudinal latent verbal profiles and assess the contribution of family process and family resource factors to observed differences. Methods: Using data from the UK Millennium Cohort Study and the latent profile analysis, we estimate longitudinal latent verbal profiles using verbal skills measured 4 times from age 3–11 years. We investigate the odds of verbal profiles by ethnicity (reported in infancy), and the extent observed differences are mediated by the home learning environment, family routines, and psychosocial environment (measured at age 3). Results: Indian children were twice as likely (OR = 2.14, CI: 1.37–3.33) to be in the high achieving profile, compared to White children. Socioeconomic markers attenuated this advantage to nonsignificance. Pakistani and Bangladeshi children were significantly more likely to be in the low performing group (OR = 2.23, CI: 1.61–3.11; OR = 3.37, CI: 2.20–5.17, respectively). Socioeconomic and psychosocial factors had the strongest mediating influence on the association between lower achieving profiles and Pakistani children, whereas for Bangladeshi children, there was mediation by the home learning environment, family routines, and psychosocial factors. Conclusion: Family process and resource factors explain ethnic differences in longitudinal latent verbal profiles. Family resources explain verbal advantages for Indian children, whereas a range of home environment and socioeconomic factors explain disparities for Pakistani and Bangladeshi children. Future policy initiatives focused on reducing ethnic disparities in children’s development should consider supporting and enhancing family resources and processes. PMID:27999155

Beyond IQ: A Latent State-Trait Analysis of General Intelligence, Dynamic Decision Making, and Implicit Learning

ERIC Educational Resources Information Center

Danner, Daniel; Hagemann, Dirk; Schankin, Andrea; Hager, Marieke; Funke, Joachim

2011-01-01

The present study investigated cognitive performance measures beyond IQ. In particular, we investigated the psychometric properties of dynamic decision making variables and implicit learning variables and their relation with general intelligence and professional success. N = 173 employees from different companies and occupational groups completed…

Reliability and Validity of a Turkish Version of the DELES

ERIC Educational Resources Information Center

Ozkok, Alev; Walker, Scott L.; Buyukozturk, Sener

2009-01-01

The primary aim of this study was to examine the reliability and validity of a Turkish version of the Distance Education Learning Environment Survey (DELES) in post-secondary distance education. The second aim was to investigate empirically the conceptualisation of the distance education learning environment as a singular latent construct, within…

Microgenetic Patterns of Children's Multiplication Learning: Confirming the Overlapping Waves Model by Latent Growth Modeling

ERIC Educational Resources Information Center

van der Ven, Sanne H. G.; Boom, Jan; Kroesbergen, Evelyn H.; Leseman, Paul P. M.

2012-01-01

Variability in strategy selection is an important characteristic of learning new skills such as mathematical skills. Strategies gradually come and go during this development. In 1996, Siegler described this phenomenon as ''overlapping waves.'' In the current microgenetic study, we attempted to model these overlapping waves statistically. In…

Discriminative Multi-View Interactive Image Re-Ranking.

PubMed

Li, Jun; Xu, Chang; Yang, Wankou; Sun, Changyin; Tao, Dacheng

2017-07-01

Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users' intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.

MRM-Lasso: A Sparse Multiview Feature Selection Method via Low-Rank Analysis.

PubMed

Yang, Wanqi; Gao, Yang; Shi, Yinghuan; Cao, Longbing

2015-11-01

Learning about multiview data involves many applications, such as video understanding, image classification, and social media. However, when the data dimension increases dramatically, it is important but very challenging to remove redundant features in multiview feature selection. In this paper, we propose a novel feature selection algorithm, multiview rank minimization-based Lasso (MRM-Lasso), which jointly utilizes Lasso for sparse feature selection and rank minimization for learning relevant patterns across views. Instead of simply integrating multiple Lasso from view level, we focus on the performance of sample-level (sample significance) and introduce pattern-specific weights into MRM-Lasso. The weights are utilized to measure the contribution of each sample to the labels in the current view. In addition, the latent correlation across different views is successfully captured by learning a low-rank matrix consisting of pattern-specific weights. The alternating direction method of multipliers is applied to optimize the proposed MRM-Lasso. Experiments on four real-life data sets show that features selected by MRM-Lasso have better multiview classification performance than the baselines. Moreover, pattern-specific weights are demonstrated to be significant for learning about multiview data, compared with view-specific weights.

Epidemiologic Methods Lessons Learned from Environmental Public Health Disasters: Chernobyl, the World Trade Center, Bhopal, and Graniteville, South Carolina

PubMed Central

Svendsen, Erik R.; Runkle, Jennifer R.; Dhara, Venkata Ramana; Lin, Shao; Naboka, Marina; Mousseau, Timothy A.; Bennett, Charles

2012-01-01

Background: Environmental public health disasters involving hazardous contaminants may have devastating effects. While much is known about their immediate devastation, far less is known about long-term impacts of these disasters. Extensive latent and chronic long-term public health effects may occur. Careful evaluation of contaminant exposures and long-term health outcomes within the constraints imposed by limited financial resources is essential. Methods: Here, we review epidemiologic methods lessons learned from conducting long-term evaluations of four environmental public health disasters involving hazardous contaminants at Chernobyl, the World Trade Center, Bhopal, and Graniteville (South Carolina, USA). Findings: We found several lessons learned which have direct implications for the on-going disaster recovery work following the Fukushima radiation disaster or for future disasters. Interpretation: These lessons should prove useful in understanding and mitigating latent health effects that may result from the nuclear reactor accident in Japan or future environmental public health disasters. PMID:23066404

On Deep Learning for Trust-Aware Recommendations in Social Networks.

PubMed

Deng, Shuiguang; Huang, Longtao; Xu, Guandong; Wu, Xindong; Wu, Zhaohui

2017-05-01

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

Ranking Highlights in Personal Videos by Analyzing Edited Videos.

PubMed

Sun, Min; Farhadi, Ali; Chen, Tseng-Hung; Seitz, Steve

2016-11-01

We present a fully automatic system for ranking domain-specific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a targeted domain such as "surfing," our system mines the YouTube database to find pairs of raw and their corresponding edited videos. Leveraging the assumption that an edited video is more likely to contain highlights than the trimmed parts of the raw video, we obtain pair-wise ranking constraints to train our model. The learning task is challenging due to the amount of noise and variation in the mined data. Hence, a latent loss function is incorporated to mitigate the issues caused by the noise. We efficiently learn the latent model on a large number of videos (about 870 min in total) using a novel EM-like procedure. Our latent ranking model outperforms its classification counterpart and is fairly competitive compared with a fully supervised ranking system that requires labels from Amazon Mechanical Turk. We further show that a state-of-the-art audio feature mel-frequency cepstral coefficients is inferior to a state-of-the-art visual feature. By combining both audio-visual features, we obtain the best performance in dog activity, surfing, skating, and viral video domains. Finally, we show that impressive highlights can be detected without additional human supervision for seven domains (i.e., skating, surfing, skiing, gymnastics, parkour, dog activity, and viral video) in unconstrained personal videos.

Between Students' Instrumental Goals and How They Learn: Goal Content Is the Gap to Mind

ERIC Educational Resources Information Center

Fryer, Luke K.; Ginns, Paul; Walker, Richard

2014-01-01

Background: Experimental/correlational studies have consistently demonstrated that the contents of an individual's goals play an important role within future motivations, learning processes, and outcomes. Aims: The aim of the study was to extend past findings by employing a three-point, cross-lagged latent simultaneous structural model in the…

A Model for New Linkages for Prior Learning Assessment

ERIC Educational Resources Information Center

Kalz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

2008-01-01

Purpose: The purpose of this paper is twofold: first the paper aims to sketch the theoretical basis for the use of electronic portfolios for prior learning assessment; second it endeavours to introduce latent semantic analysis (LSA) as a powerful method for the computation of semantic similarity between texts and a basis for a new observation link…

A Co-Citation Network of Young Children's Learning with Technology

ERIC Educational Resources Information Center

Tang, Kai-Yu; Li, Ming-Chaun; Hsin, Ching-Ting; Tsai, Chin-Chung

2016-01-01

This paper used a novel literature review approach--co-citation network analysis--to illuminate the latent structure of 87 empirical papers in the field of young children's learning with technology (YCLT). Based on the document co-citation analysis, a total of 206 co-citation relationships among the 87 papers were identified and then graphically…

Evaluation of the Technical Adequacy of Three Methods for Identifying Specific Learning Disabilities Based on Cognitive Discrepancies

ERIC Educational Resources Information Center

Stuebing, Karla K.; Fletcher, Jack M.; Branum-Martin, Lee; Francis, David J.

2012-01-01

This study used simulation techniques to evaluate the technical adequacy of three methods for the identification of specific learning disabilities via patterns of strengths and weaknesses in cognitive processing. Latent and observed data were generated and the decision-making process of each method was applied to assess concordance in…

Investigating Gender and Racial/Ethnic Invariance in Use of a Course Management System in Higher Education

ERIC Educational Resources Information Center

Li, Yi; Wang, Qiu; Campbell, John

2015-01-01

This study focused on learning equity in colleges and universities where teaching and learning depends heavily on computer technologies. The study used the Structural Equation Modeling (SEM) to investigate gender and racial/ethnic heterogeneity in the use of a computer based course management system (CMS). Two latent variables (CMS usage and…

Automatic Evaluation for E-Learning Using Latent Semantic Analysis: A Use Case

ERIC Educational Resources Information Center

Farrus, Mireia; Costa-jussa, Marta R.

2013-01-01

Assessment in education allows for obtaining, organizing, and presenting information about how much and how well the student is learning. The current paper aims at analysing and discussing some of the most state-of-the-art assessment systems in education. Later, this work presents a specific use case developed for the Universitat Oberta de…

Locus of Control Orientations in Students with Intellectual Disability, Learning Disabilities, and No Disabilities: A Latent Growth Curve Analysis

ERIC Educational Resources Information Center

Shogren, Karrie A.; Bovaird, James A.; Palmer, Susan B.; Wehmeyer, Michael L.

2010-01-01

Previous research has suggested differences in the locus of control (LOC) orientations of students with intellectual disability, learning disabilities, and no disabilities, although this research has been characterized by methodological limitations. The purpose of this study was to examine the development of LOC orientations in students with…

Object-based implicit learning in visual search: perceptual segmentation constrains contextual cueing.

PubMed

Conci, Markus; Müller, Hermann J; von Mühlenen, Adrian

2013-07-09

In visual search, detection of a target is faster when it is presented within a spatial layout of repeatedly encountered nontarget items, indicating that contextual invariances can guide selective attention (contextual cueing; Chun & Jiang, 1998). However, perceptual regularities may interfere with contextual learning; for instance, no contextual facilitation occurs when four nontargets form a square-shaped grouping, even though the square location predicts the target location (Conci & von Mühlenen, 2009). Here, we further investigated potential causes for this interference-effect: We show that contextual cueing can reliably occur for targets located within the region of a segmented object, but not for targets presented outside of the object's boundaries. Four experiments demonstrate an object-based facilitation in contextual cueing, with a modulation of context-based learning by relatively subtle grouping cues including closure, symmetry, and spatial regularity. Moreover, the lack of contextual cueing for targets located outside the segmented region was due to an absence of (latent) learning of contextual layouts, rather than due to an attentional bias towards the grouped region. Taken together, these results indicate that perceptual segmentation provides a basic structure within which contextual scene regularities are acquired. This in turn argues that contextual learning is constrained by object-based selection.

Picturing academic learning: salutogenic and health promoting perspectives on drawings.

PubMed

Garista, Patrizia; Pocetta, Giancarlo; Lindström, Bengt

2018-05-25

More than 20 years ago an article about the use of drawings in higher education appeared in a medical journal. After that, other papers explored the possible contribution of drawings in adult education, while only very few in the field of health promotion and education. This article aims to introduce the use of drawing in this field using the salutogenic lens to think, plan and reflect on academic learning. Reflections on what salutogenesis is and what we can consider a clear application of salutogenic principles to the learning process answer a hypothetical question for the reader concerning the relationship between drawings and health promotion theories. They appear as communication tools capable of exploring meaning-making processes, capturing data that is flexible to dynamic systems, power relations, as well as emotional and latent aspects of human experience. This article proposes a connection between salutogenesis and drawings through: a theoretical framework on salutogenic learning and drawings; a teacher practice and its tools focusing the critical point on visual data analysis in a learning environment; a learner case example for knowledge and capacity building through the drawing process; and a health promotion competency-based analysis. Our case example illustrates how drawings were introduced in a post-graduate course in Health Promotion and Education and argues their strengths and weaknesses.

Study on Latent Heat of Fusion of Ice in Aqueous Solutions

NASA Astrophysics Data System (ADS)

Kumano, Hiroyuki; Asaoka, Tatsunori; Saito, Akio; Okawa, Seiji

In this study, latent heat of fusion of ice in aqueous solutions was measured to understand latent heat of fusion of ice slurries. Propylene glycol, ethylene glycol, ethanol, NaCl and NaNO3 solutions were examined as the aqueous solutions. In the measurement, pure ice was put into the solution, and the temperature variation of the solution due to the melting of the ice was measured. Then, the effective latent heat of fusion was calculated from energy balance equation. When ice melts in solution, the concentration of the solution varies due to the melting of the ice, and dilution heat must be considered. Therefore, the latent heat of fusion of ice in aqueous solutions was predicted by considering the effects of dilution and freezing-point depression. The latent heat of fusion was also measured by differential scanning calorimetry(DSC) to compare the results obtained from the experiments with that obtained by DSC. As the result, it was found that the effective latent heat of fusion of ice decreased with the increase of the concentration of solution, and the effective latent heat of fusion was calculated from latent heat of fusion of pure ice and the effects of freezing-point depression and the dilution heat.

Being a team leader: newly registered nurses relate their experiences.

PubMed

Ekström, Louise; Idvall, Ewa

2015-01-01

This paper presents a study that explores how newly qualified registered nurses experience their leadership role in the ward-based nursing care team. A nurse's clinical leadership affects the quality of care provided. Newly qualified nurses experience difficulties during the transition period from student to qualified professional and find it challenging to lead nursing care. Twelve nurses were interviewed and the transcribed texts analysed using qualitative content analysis to assess both manifest and latent content. Five themes were identified: feeling stranded; forming well-functioning teams; learning to lead; having the courage, strength, and desire to lead; and ensuring appropriate care. The findings indicate that many factors limit nurses' leadership but some circumstances are supportive. The leadership prerequisites for newly registered nurses need to improve, emphasizing different ways to create a supportive atmosphere that promotes professional development and job satisfaction. To increase nurse retention and promote quality of care, nurse managers need to clarify expectations and guide and support newly qualified nurses in a planned way. © 2013 John Wiley & Sons Ltd.

Effects of dietary choline availability on latent inhibition of flavor aversion learning.

PubMed

Gámiz, Fernando; Recio, Sergio Andrés; Iliescu, Adela Florentina; Gallo, Milagros; de Brugada, Isabel

2015-08-01

It has been previously reported that dietary choline supplementation might affect latent inhibition (LI) using a conditioned suppression procedure in rats. We have assessed the effect of dietary choline on LI of flavor aversion learning. Adult male Wistar rats received a choline supplemented (5 g/kg), deficient (0 g/kg), or standard (1.1 g/kg) diet for 3 months. After this supplementation period, all rats went through a conditioned taste aversion (CTA) procedure, half of them being pre-exposed to the conditioned stimulus before the conditioning. The results indicated that choline deficiency prevents LI of conditioned flavor aversion to cider vinegar (3%) induced by a LiCl (0.15 M; 2% body weight) intraperitoneal injection, while choline supplementation enhances CTA leading to slower extinction. The role of the brain systems modulating attentional processes is discussed.

Correlates of individual, and age-related, differences in short-term learning.

PubMed

Zhang, Zhiyong; Davis, Hasker P; Salthouse, Timothy A; Tucker-Drob, Elliot M

2007-07-01

Latent growth models were applied to data on multitrial verbal and spatial learning tasks from two independent studies. Although significant individual differences in both initial level of performance and subsequent learning were found in both tasks, age differences were found only in mean initial level, and not in mean learning. In neither task was fluid or crystallized intelligence associated with learning. Although there were moderate correlations among the level parameters across the verbal and spatial tasks, the learning parameters were not significantly correlated with one another across task modalities. These results are inconsistent with the existence of a general (e.g., material-independent) learning ability.

The Latent Structure of Secure Base Script Knowledge

ERIC Educational Resources Information Center

Waters, Theodore E. A.; Fraley, R. Chris; Groh, Ashley M.; Steele, Ryan D.; Vaughn, Brian E.; Bost, Kelly K.; Veríssimo, Manuela; Coppola, Gabrielle; Roisman, Glenn I.

2015-01-01

There is increasing evidence that attachment representations abstracted from childhood experiences with primary caregivers are organized as a cognitive script describing secure base use and support (i.e., the "secure base script"). To date, however, the latent structure of secure base script knowledge has gone unexamined--this despite…

The influence of the tropics upon the prediction of the Southern Hemisphere circulation within the GLAS GCM. [Goddard Laboratory for Atmospheric Sciences General Circulation Model

NASA Technical Reports Server (NTRS)

Baker, W. E.; Paegle, J.

1983-01-01

An examination is undertaken of the sensitivity of short term Southern Hemisphere circulation prediction to tropical wind data and tropical latent heat release. The data assimilation experiments employ the Goddard Laboratory for Atmospheric Sciences' fourth-order general circulation model. Two of the experiments are identical, but for the fact that one uses tropical wind data while the other does not. A third experiment contains the identical initial conditions of forecasts with tropical winds, while suppressing tropical latent heat release.

Influence of Latent Heating over the Asian and Western Pacific Monsoon Region on Sahel Summer Rainfall.

PubMed

He, Shan; Yang, Song; Li, Zhenning

2017-08-09

There has been an interdecadal shift towards a less humid state in Sahel summer rainfall since the 1960s. The decreased Sahel summer rainfall was associated with enhanced summer latent heating over the South Asian and western Pacific summer monsoon region and anomalous zonal-vertical cell of the Asian summer monsoon circulation, indicating that the latent heating plays a significant role in the change in Sahel rainfall. The effects of the latent heating over different monsoon domains on the Sahel rainfall are investigated through several model experiments. Results show that the remote monsoon heating mainly affects Sahel rainfall by generating changes in the zonal-vertical atmospheric circulation.

Social behavior of bacteria: from physics to complex organization

NASA Astrophysics Data System (ADS)

Ben-Jacob, E.

2008-10-01

I describe how bacteria develop complex colonial patterns by utilizing intricate communication capabilities, such as quorum sensing, chemotactic signaling and exchange of genetic information (plasmids) Bacteria do not store genetically all the information required for generating the patterns for all possible environments. Instead, additional information is cooperatively generated as required for the colonial organization to proceed. Each bacterium is, by itself, a biotic autonomous system with its own internal cellular informatics capabilities (storage, processing and assessments of information). These afford the cell certain plasticity to select its response to biochemical messages it receives, including self-alteration and broadcasting messages to initiate alterations in other bacteria. Hence, new features can collectively emerge during self-organization from the intra-cellular level to the whole colony. Collectively bacteria store information, perform decision make decisions (e.g. to sporulate) and even learn from past experience (e.g. exposure to antibiotics)-features we begin to associate with bacterial social behavior and even rudimentary intelligence. I also take Schrdinger’s’ “feeding on negative entropy” criteria further and propose that, in addition organisms have to extract latent information embedded in the environment. By latent information we refer to the non-arbitrary spatio-temporal patterns of regularities and variations that characterize the environmental dynamics. In other words, bacteria must be able to sense the environment and perform internal information processing for thriving on latent information embedded in the complexity of their environment. I then propose that by acting together, bacteria can perform this most elementary cognitive function more efficiently as can be illustrated by their cooperative behavior.

A Confirmatory Factor Analysis of the California Verbal Learning Test-Second Edition (CVLT-II) in the Standardization Sample

ERIC Educational Resources Information Center

Donders, Jacobus

2008-01-01

The purpose of this study is to determine the latent structure of the California Verbal Learning Test-Second Edition (CVLT-II; Delis, Kramer, Kaplan, & Ober, 2000) at three different age levels, using the standardization sample. Maximum likelihood confirmatory factor analyses are performed to test four competing hypothetical models for fit and…

Investigation Gender/Ethnicity Heterogeneity in Course Management System Use in Higher Education by Utilizing the MIMIC Model

ERIC Educational Resources Information Center

Li, Yi

2012-01-01

This study focuses on the issue of learning equity in colleges and universities where teaching and learning have come to depend heavily on computer technologies. The study uses the Multiple Indicators Multiple Causes (MIMIC) latent variable model to quantitatively investigate whether there is a gender /ethnicity difference in using computer based…

Self-Report Measures of the Home Learning Environment in Large Scale Research: Measurement Properties and Associations with Key Developmental Outcomes

ERIC Educational Resources Information Center

Niklas, Frank; Nguyen, Cuc; Cloney, Daniel S.; Tayler, Collette; Adams, Raymond

2016-01-01

Favourable home learning environments (HLEs) support children's literacy, numeracy and social development. In large-scale research, HLE is typically measured by self-report survey, but there is little consistency between studies and many different items and latent constructs are observed. Little is known about the stability of these items and…

American and Chinese Students' Profiles Based on Spanish-Learning Strategies: A Transcultural Comparison

ERIC Educational Resources Information Center

Bernardo, Aránzazu; Amérigo, María; García, Juan A.

2017-01-01

This paper presents a study on the use of learning strategies in foreign languages, and more specifically Spanish. The study was conducted with 376 Chinese and American students who were studying Spanish in their countries of origin. The results obtained from a latent class cluster analysis identified five groups of participants based on the…

Examining EFL Teachers' Technological Pedagogical Content Knowledge and the Adoption of Mobile-Assisted Language Learning: A Partial Least Square Approach

ERIC Educational Resources Information Center

Hsu, Liwei

2016-01-01

This study examines EFL (English as a foreign Language) teachers' technological pedagogical content knowledge (TPACK) and how such knowledge affects the adoption of mobile-assisted language learning (MALL). A total of 158 in-service Taiwanese English teachers were surveyed. Two frameworks were employed to examine latent constructs: TPACK and the…

Construct Validity of the California Verbal Learning Test--Children's Version (CVLT-C) after Pediatric Traumatic Brain Injury

ERIC Educational Resources Information Center

Mottram, Lisa; Donders, Jacobus

2005-01-01

The purpose of this study was to determine the latent structure of the California Verbal Learning Test--Children's Version (CVLT-C; D. Delis, J. Kramer, E.Kaplan, & B. Ober, 1994) in a sample of 175 children with traumatic brain injury (TBI). Maximum-likelihood confirmatory factor analyses were performed to test 6 competing hypothetical models…

Clusters of Word Properties as Predictors of Elementary School Children's Performance on Two Word Tasks

ERIC Educational Resources Information Center

Tellings, Agnes; Coppens, Karien; Gelissen, John; Schreuder, Rob

2013-01-01

Often, the classification of words does not go beyond "difficult" (i.e., infrequent, late-learned, nonimageable, etc.) or "easy" (i.e., frequent, early-learned, imageable, etc.) words. In the present study, we used a latent cluster analysis to divide 703 Dutch words with scores for eight word properties into seven clusters of words. Each cluster…

The Learning and Study Strategies Inventory-High School Version: Issues of Factorial Invariance Across Gender and Ethnicity

ERIC Educational Resources Information Center

Stevens, Tara; Tallent-Runnels, Mary K.

2004-01-01

The purpose of this study was to investigate the latent structure of the Learning and Study Strategies Inventory-High School (LASSI-HS) through confirmatory factor analysis and factorial invariance models. A simple modification of the three-factor structure was considered. Using a larger sample, cross-validation was completed and the equality of…

Modelling the Success of Learning Management Systems: Application of Latent Class Segmentation Using FIMIX-PLS

ERIC Educational Resources Information Center

Arenas-Gaitán, Jorge; Rondán-Cataluña, Francisco Javier; Ramírez-Correa, Patricio E.

2018-01-01

There is not a unique attitude towards the implementation of digital technology in educational sceneries. This paper aims to validate an adaptation of the DeLone and McLean information systems success model in the context of a learning management system. Furthermore, this study means to prove (1) the necessity of segmenting students in order to…

A Confirmatory Factor Analysis of the California Verbal Learning Test-Second Edition (CVLT-II) in a Traumatic Brain Injury Sample

ERIC Educational Resources Information Center

DeJong, Joy; Donders, Jacobus

2009-01-01

The latent structure of the California Verbal Learning Test-Second Edition (CVLT-II) was examined in a clinical sample of 223 persons with traumatic brain injury that had been screened to remove individuals with complicating premorbid (e.g., psychiatric) or comorbid (e.g., financial compensation seeking) histories. Analyses incorporated the…

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.

PubMed

Yoo, Youngjin; Tang, Lisa Y W; Brosch, Tom; Li, David K B; Kolind, Shannon; Vavasour, Irene; Rauscher, Alexander; MacKay, Alex L; Traboulsee, Anthony; Tam, Roger C

2018-01-01

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t -test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD = 8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD = 13.7%) and T1w measurements (66.7%, SD = 10.6%), or deep-learned features in either the myelin (83.8%, SD = 11.0%) or T1w (70.1%, SD = 13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

Origin of temperature plateaus in laser-heated diamond anvil cell experiments

NASA Astrophysics Data System (ADS)

Geballe, Zachary M.; Jeanloz, Raymond

2012-06-01

Many high-pressure high-temperature studies using laser-heated diamond cells have documented plateaus in the increase of temperature with increasing laser power or with time. By modeling heat transfer in typical laser-heated diamond anvil cell experiments, we demonstrate that latent heat due to melting or other phase transformation is unlikely to be the source of observed plateaus in any previously published studies, regardless of whether pulsed or continuous lasers were used. Rather, large increases (˜10-fold) in thermal conductivity can explain some of the plateaus, and modest increases in reflectivity (tens of percent) can explain any or all of them. Modeling also shows that the sub-microsecond timescale of heating employed in recent pulsed heating experiments is fast enough compared to heat transport into and through typical insulations, but too slow compared to heat transport into metallic laser absorbers themselves to allow the detection of a large plateau due to latent heat of fusion. Four new designs are suggested for future experiments that could use the simple observation of a latent heat-induced plateau to provide reliable high-pressure melting data.

Reciprocal Relations Between Cognitive Neuroscience and Cognitive Models: Opposites Attract?

PubMed Central

Forstmann, Birte U.; Wagenmakers, Eric-Jan; Eichele, Tom; Brown, Scott; Serences, John T.

2012-01-01

Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal models of cognition can decompose observed behavioral data into multiple latent cognitive processes, allowing brain measurements to be associated with a particular cognitive process more precisely and more confidently. Second, cognitive neuroscience can provide additional data to inform the development of cognitive models, providing greater constraint than behavioral data alone. We argue that these fields are mutually dependent: not only can models guide neuroscientific endeavors, but understanding neural mechanisms can provide critical insights into formal models of cognition. PMID:21612972

The complexity of team training: what we have learned from aviation and its applications to medicine

PubMed Central

Hamman, W

2004-01-01

Errors in health care that compromise patient safety are tied to latent failures in the structure and function of systems. Teams of people perform most care delivered today, yet training often remains focused on individual responsibilities. Training programmes for all healthcare workers need to increase the educational experience of working in interdisciplinary teams. The complexities of team training require a multifunctional (systems) approach, which crosses organisational divisions to allow communication, accountability, and creation and maintenance of interdisciplinary teams. This report identifies challenges for medical education in performing the research, identifying performance measurements, and modifying educational curricula for the advancement of interdisciplinary teams, based on the complexity of team training identified in commercial aviation. PMID:15465959

Evaluation of a Risk Awareness Perception Training Program on Novice Teen Driver Behavior at Left-Turn Intersections.

PubMed

McDonald, Catherine C; Kandadai, Venk; Loeb, Helen; Seacrist, Thomas; Lee, Yi-Ching; Bonfiglio, Dana; Fisher, Donald L; Winston, Flaura K

Collisions at left turn intersections are among the most prevalent types of teen driver serious crashes, with inadequate surveillance as a key factor. Risk awareness perception training (RAPT) has shown effectiveness in improving hazard anticipation for latent hazards. The goal of this study was to determine if RAPT version 3 (RAPT-3) improved intersection turning behaviors among novice teen drivers when the hazards were not latent and frequent glancing to multiple locations at the intersection was needed. Teens aged 16-18 with ≤180 days of licensure were randomly assigned to: 1) an intervention group (n=18) that received RAPT-3 (Trained); or 2) a control group (n=19) that received no training (Untrained). Both groups completed RAPT-3 Baseline Assessment and the Trained group completed RAPT-3 Training and RAPT-3 Post Assessment. Training effects were evaluated on a driving simulator. Simulator ( gap selection errors and collisions ) and eye tracker ( traffic check errors) metrics from six left-turn stop sign controlled intersections in the Simulated Driving Assessment (SDA) were analyzed. The Trained group scored significantly higher in RAPT-3 Post Assessment than RAPT-3 Baseline Assessment (p< 0.0001). There were no significant differences in either traffic check and gap selection errors or collisions among Trained and Untrained teens in the SDA. Though Trained teens learned about hazard anticipation related to latent hazards, learning did not translate to performance differences in left-turn stop sign controlled intersections where the hazards were not latent. Our findings point to further research to better understand the challenges teens have with left turn intersections.

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences

PubMed Central

Stamovlasis, Dimitrios; Papageorgiou, George; Tsitsipis, Georgios; Tsikalas, Themistoklis; Vaiopoulou, Julie

2018-01-01

This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues. PMID:29713300

Latent curing systems stabilized by reaction equilibrium in homogeneous mixtures of benzoxazine and amine

PubMed Central

Wang, Jun; Xu, Ya Zhen; Fu, Ya Fei; Liu, Xiang Dong

2016-01-01

Latent curing systems are widely used in industrial thermosets in applications such as adhesion, coating, and composites. Despite many attempts to improve the practicality of this dormant reaction system, the majority of commercially available latent products still use particulate hardeners or liquid compounds with blocked active groups. These formulations generally lack fluidity or rapid reaction characteristics and thus are problematic in some industry applications. Here we describe a novel concept that stabilizes highly reactive benzoxazine/amine mixtures by reaction equilibrium. These new latent benzoxazine curing systems have a long storable lifetime but very short gel time at 150 °C. The reversible reaction between benzoxazine and amine is further demonstrated by FT-IR spectral measurements and rheological experiments, and it is shown that the overall characteristics of the latent system are promising for many industrial applications. PMID:27917932

Application of Generative Autoencoder in De Novo Molecular Design.

PubMed

Blaschke, Thomas; Olivecrona, Marcus; Engkvist, Ola; Bajorath, Jürgen; Chen, Hongming

2018-01-01

A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the trainings set were identified. © 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Paternal Work Stress and Latent Profiles of Father-Infant Parenting Quality

ERIC Educational Resources Information Center

Goodman, W. Benjamin; Crouter, Ann C.; Lanza, Stephanie T.; Cox, Martha J.; Vernon-Feagans, Lynne

2011-01-01

The current study used latent profile analysis (LPA) to examine the implications of fathers' experiences of work stress for paternal behaviors with infants across multiple dimensions of parenting in a sample of fathers living in nonmetropolitan communities (N = 492). LPA revealed five classes of fathers based on levels of social-affective…

Latent Classes of Adolescent Sexual and Romantic Relationship Experiences: Implications for Adult Sexual Health and Relationship Outcomes

PubMed Central

Vasilenko, Sara A.; Kugler, Kari C.; Lanza, Stephanie T.

2015-01-01

Adolescents’ sexual and romantic relationship experiences are multidimensional, but often studied as single constructs. Thus, it is not clear how different patterns of sexual and relationship experience may interact to differentially predict later outcomes. In this study we used latent class analysis to model patterns (latent classes) of adolescent sexual and romantic experiences, and then examined how these classes are associated with young adult sexual health and relationship outcomes in data from the National Longitudinal Study of Adolescent to Adult Health. We identified six adolescent relationship classes: No Relationship (33%), Waiting (22%), Intimate (38%), Private (3%), Low Involvement (3%), and Physical (2%). Adolescents in the Waiting and Intimate classes were more likely to have married by young adulthood than those in other classes, and those in the Physical class had a greater number of sexual partners and higher rates of STIs. Some gender differences were found; for example, women in the Low-involvement and Physical classes in adolescence had average or high odds of marriage, whereas men in these classes had relatively low odds of marriage. Our findings identify more and less normative patterns of romantic and sexual experiences in late adolescence, and elucidate associations between adolescent experiences and adult outcomes. PMID:26445133

Adolescent cigarette smoking: health-related behavior or normative transgression?

PubMed

Turbin, M S; Jessor, R; Costa, F M

2000-09-01

Relations among measures of adolescent behavior were examined to determine whether cigarette smoking fits into a structure of problem behaviors-behaviors that involve normative transgression-or a structure of health-related behaviors, or both. In an ethnically and socioeconomically diverse sample of 1782 male and female high school adolescents, four first-order problem behavior latent variables-sexual intercourse experience, alcohol abuse, illicit drug use, and delinquency-were established and together were shown to reflect a second-order latent variable of problem behavior. Four first-order latent variables of health-related behaviors-unhealthy dietary habits, sedentary behavior, unsafe behavior, and poor dental hygiene-were also established and together were shown to reflect a second-order latent variable of health-compromising behavior. The structure of relations among those latent variables was modeled. Cigarette smoking had a significant and substantial loading only on the problem-behavior latent variable; its loading on the health-compromising behavior latent variable was essentially zero. Adolescent cigarette smoking relates strongly and directly to problem behaviors and only indirectly, if at all, to health-compromising behaviors. Interventions to prevent or reduce adolescent smoking should attend more to factors that influence problem behaviors.

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

PubMed

Zimprich, Daniel; Kurtz, Tanja

2013-01-01

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

Distributional Properties of the PIRLS-Home Resource for Learning Scale and Observed Effects on Reading Achievement: Are Measurements of Educational Inequalities by Latent Indices without Bias?

ERIC Educational Resources Information Center

Walzebug, Anke; Kasper, Daniel

2018-01-01

In "Progress in International Reading Literacy Study" (PIRLS) educational inequalities are measured, amongst others, through the relationship between students' reading achievements and the home resource for learning (HRL) scale. By applying the partial credit model and using the WLE estimates for the person parameters it is accepted that…

Home and Preschool Learning Environments and Their Relations to the Development of Early Numeracy Skills

ERIC Educational Resources Information Center

Anders, Yvonne; Rossbach, Hans-Gunther; Weinert, Sabine; Ebert, Susanne; Kuger, Susanne; Lehrl, Simone; von Maurice, Jutta

2012-01-01

This study examined the influence of the quality of home and preschool learning environments on the development of early numeracy skills in Germany, drawing on a sample of 532 children in 97 preschools. Latent growth curve models were used to investigate early numeracy skills and their development from the first (average age: 3 years) to the third…

Use of Latent Growth Curve Modeling for Assessing the Effects of Summer and After-School Learning on Adolescent Students' Achievement Gap

ERIC Educational Resources Information Center

Lin, Chunn-Ying; Hsieh, Ya-Heng; Chen, Cheng-Hung

2015-01-01

Many Western researchers have found that the gaps in the learning progress between students from different socioeconomic statuses primarily occur due to the accumulated effects of long summer vacations, rather than during the school years. However, it remains to be seen whether these findings can be cross-culturally applied to children in Taiwan.…

Longitudinal Analysis of the Role of Perceived Self-Efficacy for Self-Regulated Learning in Academic Continuance and Achievement

ERIC Educational Resources Information Center

Caprara, Gian Vittorio; Fida, Roberta; Vecchione, Michele; Del Bove, Giannetta; Vecchio, Giovanni Maria; Barbaranelli, Claudio; Bandura, Albert

2008-01-01

The present study examined the developmental course of perceived efficacy for self-regulated learning and its contribution to academic achievement and likelihood of remaining in school in a sample of 412 Italian students (48% males and 52% females ranging in age from 12 to 22 years). Latent growth curve analysis revealed a progressive decline in…

Information-driven self-organization: the dynamical system approach to autonomous robot behavior.

PubMed

Ay, Nihat; Bernigau, Holger; Der, Ralf; Prokopenko, Mikhail

2012-09-01

In recent years, information theory has come into the focus of researchers interested in the sensorimotor dynamics of both robots and living beings. One root for these approaches is the idea that living beings are information processing systems and that the optimization of these processes should be an evolutionary advantage. Apart from these more fundamental questions, there is much interest recently in the question how a robot can be equipped with an internal drive for innovation or curiosity that may serve as a drive for an open-ended, self-determined development of the robot. The success of these approaches depends essentially on the choice of a convenient measure for the information. This article studies in some detail the use of the predictive information (PI), also called excess entropy or effective measure complexity, of the sensorimotor process. The PI of a process quantifies the total information of past experience that can be used for predicting future events. However, the application of information theoretic measures in robotics mostly is restricted to the case of a finite, discrete state-action space. This article aims at applying the PI in the dynamical systems approach to robot control. We study linear systems as a first step and derive exact results for the PI together with explicit learning rules for the parameters of the controller. Interestingly, these learning rules are of Hebbian nature and local in the sense that the synaptic update is given by the product of activities available directly at the pertinent synaptic ports. The general findings are exemplified by a number of case studies. In particular, in a two-dimensional system, designed at mimicking embodied systems with latent oscillatory locomotion patterns, it is shown that maximizing the PI means to recognize and amplify the latent modes of the robotic system. This and many other examples show that the learning rules derived from the maximum PI principle are a versatile tool for the self-organization of behavior in complex robotic systems.

The Pressure Cooker: A Module on the Properties of Matter. Tech Physics Series.

ERIC Educational Resources Information Center

Technical Education Research Center, Cambridge, MA.

Experiments to provide an understanding of the principles related to the pressure cooker are presented. Objectives included are designed to provide the learner with the ability to calibrate a thermistor for measuring temperature; explain the meaning of latent and specific heat; calculate latent and specific heat; use a Bourdon tube pressure gauge…

Disentangling the Effects of Context Change and Context Familiarity on Latent Inhibition with a Conditioned Taste Aversion Procedure

ERIC Educational Resources Information Center

De la Casa, L. G.; Mena, A.; Orgaz, A.; Fernandez, A.

2013-01-01

Contextual specificity of Latent Inhibition (LI) has been demonstrated using an ample range of experimental procedures. Context dependence has not been consistently obtained, however, when LI has been induced using a Conditioned Taste Aversion (CTA) procedure. This paper presents two experiments designed to analyze whether the context plays the…

Hierarchical Discriminant Analysis.

PubMed

Lu, Di; Ding, Chuntao; Xu, Jinliang; Wang, Shangguang

2018-01-18

The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorithms to learn a latent subspace to preserve the intrinsic structure of the high-dimensional data, and abandon the least useful information in the subsequent processing. In this context, many subspace learning algorithms have been presented. However, in the process of transforming the high-dimensional data into the low-dimensional space, the huge difference between the sum of inter-class distance and the sum of intra-class distance for distinct data may cause a bias problem. That means that the impact of intra-class distance is overwhelmed. To address this problem, we propose a novel algorithm called Hierarchical Discriminant Analysis (HDA). It minimizes the sum of intra-class distance first, and then maximizes the sum of inter-class distance. This proposed method balances the bias from the inter-class and that from the intra-class to achieve better performance. Extensive experiments are conducted on several benchmark face datasets. The results reveal that HDA obtains better performance than other dimensionality reduction algorithms.

Feature and Region Selection for Visual Learning.

PubMed

Zhao, Ji; Wang, Liantao; Cabral, Ricardo; De la Torre, Fernando

2016-03-01

Visual learning problems, such as object classification and action recognition, are typically approached using extensions of the popular bag-of-words (BoWs) model. Despite its great success, it is unclear what visual features the BoW model is learning. Which regions in the image or video are used to discriminate among classes? Which are the most discriminative visual words? Answering these questions is fundamental for understanding existing BoW models and inspiring better models for visual recognition. To answer these questions, this paper presents a method for feature selection and region selection in the visual BoW model. This allows for an intermediate visualization of the features and regions that are important for visual learning. The main idea is to assign latent weights to the features or regions, and jointly optimize these latent variables with the parameters of a classifier (e.g., support vector machine). There are four main benefits of our approach: 1) our approach accommodates non-linear additive kernels, such as the popular χ(2) and intersection kernel; 2) our approach is able to handle both regions in images and spatio-temporal regions in videos in a unified way; 3) the feature selection problem is convex, and both problems can be solved using a scalable reduced gradient method; and 4) we point out strong connections with multiple kernel learning and multiple instance learning approaches. Experimental results in the PASCAL VOC 2007, MSR Action Dataset II and YouTube illustrate the benefits of our approach.

Learning Latent Variable and Predictive Models of Dynamical Systems

DTIC Science & Technology

2009-10-01

stable over the full 1000 frame image sequence without significant damping. C. Sam- ples drawn from a least squares synthesized sequences (top), and...LDS stabilizing algorithms, LB-1 and LB-2. Bars at every 20 timesteps denote variance in the results. CG provides the best stable short term predictions...observations. This thesis contributes (1) novel learning algorithms for existing dynamical system models that overcome significant limitations of previous

Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling.

PubMed

Ding, Meng; Fan, Guolian

2015-11-01

We present new multilayer joint gait-pose manifolds (multilayer JGPMs) for complex human gait motion modeling, where three latent variables are defined jointly in a low-dimensional manifold to represent a variety of body configurations. Specifically, the pose variable (along the pose manifold) denotes a specific stage in a walking cycle; the gait variable (along the gait manifold) represents different walking styles; and the linear scale variable characterizes the maximum stride in a walking cycle. We discuss two kinds of topological priors for coupling the pose and gait manifolds, i.e., cylindrical and toroidal, to examine their effectiveness and suitability for motion modeling. We resort to a topologically-constrained Gaussian process (GP) latent variable model to learn the multilayer JGPMs where two new techniques are introduced to facilitate model learning under limited training data. First is training data diversification that creates a set of simulated motion data with different strides. Second is the topology-aware local learning to speed up model learning by taking advantage of the local topological structure. The experimental results on the Carnegie Mellon University motion capture data demonstrate the advantages of our proposed multilayer models over several existing GP-based motion models in terms of the overall performance of human gait motion modeling.

Heterogeneity in patterns of DSM-5 posttraumatic stress disorder and depression symptoms: Latent profile analyses.

PubMed

Contractor, Ateka A; Roley-Roberts, Michelle E; Lagdon, Susan; Armour, Cherie

2017-04-01

Posttraumatic stress disorder (PTSD) and depression co-occur frequently following the experience of potentially traumatizing events (PTE; Morina et al., 2013). A person-centered approach to discern heterogeneous patterns of such co-occurring symptoms is recommended (Galatzer-Levy and Bryant, 2013). We assessed heterogeneity in PTSD and depression symptomatology; and subsequently assessed relations between class membership with psychopathology constructs (alcohol use, distress tolerance, dissociative experiences). The sample consisted of 268 university students who had experienced a PTE and susequently endorsed clinical levels of PTSD or depression severity. Latent profile analyses (LPA) was used to identify the best-fitting class solution accouring to recommended fit indices (Nylund et al., 2007a); and the effects of covariates was analyzed using a 3-step approach (Vermunt, 2010). Results of the LPA indicated an optimal 3-class solutions: high severity (Class 2), lower PTSD-higher depression (Class 1), and higher PTSD-lower depression (Class 3). Covariates of distress tolerance, and different kinds of dissociative experiences differentiated the latent classes. Use of self-report measure could lead to response biases; and the specific nature of the sample limits generalizability of results. We found evidence for a depressive subtype of PTSD differentiated from other classes in terms of lower distress tolerance and greater dissociative experiences. Thus, transdiagnostic treatment protocols may be most beneficial for these latent class members. Further, the distinctiveness of PTSD and depression at comparatively lower levels of PTSD severity was supported (mainly in terms of distress tolerance abilities); hence supporting the current classification system placement of these disorders. Copyright © 2017 Elsevier B.V. All rights reserved.

Subgraph augmented non-negative tensor factorization (SANTF) for modeling clinical narrative text

PubMed Central

Xin, Yu; Hochberg, Ephraim; Joshi, Rohit; Uzuner, Ozlem; Szolovits, Peter

2015-01-01

Objective Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability. Methods The authors introduce a novel framework named subgraph augmented non-negative tensor factorization (SANTF). In addition to relying on atomic features (e.g., words in clinical narrative text), SANTF automatically mines higher-order features (e.g., relations of lymphoid cells expressing antigens) from clinical narrative text by converting sentences into a graph representation and identifying important subgraphs. The authors compose a tensor using patients, higher-order features, and atomic features as its respective modes. We then apply non-negative tensor factorization to cluster patients, and simultaneously identify latent groups of higher-order features that link to patient clusters, as in clinical guidelines where a panel of immunophenotypic features and laboratory results are used to specify diagnostic criteria. Results and Conclusion SANTF demonstrated over 10% improvement in averaged F-measure on patient clustering compared to widely used non-negative matrix factorization (NMF) and k-means clustering methods. Multiple baselines were established by modeling patient data using patient-by-features matrices with different feature configurations and then performing NMF or k-means to cluster patients. Feature analysis identified latent groups of higher-order features that lead to medical insights. We also found that the latent groups of atomic features help to better correlate the latent groups of higher-order features. PMID:25862765

Incident learning in pursuit of high reliability: implementing a comprehensive, low-threshold reporting program in a large, multisite radiation oncology department.

PubMed

Gabriel, Peter E; Volz, Edna; Bergendahl, Howard W; Burke, Sean V; Solberg, Timothy D; Maity, Amit; Hahn, Stephen M

2015-04-01

Incident learning programs have been recognized as cornerstones of safety and quality assurance in so-called high reliability organizations in industries such as aviation and nuclear power. High reliability organizations are distinguished by their drive to continuously identify and proactively address a broad spectrum of latent safety issues. Many radiation oncology institutions have reported on their experience in tracking and analyzing adverse events and near misses but few have incorporated the principles of high reliability into their programs. Most programs have focused on the reporting and retrospective analysis of a relatively small number of significant adverse events and near misses. To advance a large, multisite radiation oncology department toward high reliability, a comprehensive, cost-effective, electronic condition reporting program was launched to enable the identification of a broad spectrum of latent system failures, which would then be addressed through a continuous quality improvement process. A comprehensive program, including policies, work flows, and information system, was designed and implemented, with use of a low reporting threshold to focus on precursors to adverse events. In a 46-month period from March 2011 through December 2014, a total of 8,504 conditions (average, 185 per month, 1 per patient treated, 3.9 per 100 fractions [individual treatments]) were reported. Some 77.9% of clinical staff members reported at least 1 condition. Ninety-eight percent of conditions were classified in the lowest two of four severity levels, providing the opportunity to address conditions before they contribute to adverse events. Results after approximately four years show excellent employee engagement, a sustained rate of reporting, and a focus on low-level issues leading to proactive quality improvement interventions.

Fire in operating theatres: DaSH-ing to the rescue.

PubMed

Wilson, Liam; Farooq, Omer

2018-01-01

Operating theatres are dynamic environments that require multi professional team interactions. Effective team working is essential for efficient delivery of safe patient care. A fire in the operating theatre is a rare but potentially life threatening event for both patients and staff. A rapid and cohesive response from theatre and allied staff including porters, fire safety officer etc is paramount. We delivered a training session that utilised in situ simulation (simulation in workplace). After conducting needs analysis, learning objectives were agreed. After thorough planning, the date and location of the training session were identified. Contingency plans were put in place to ensure that patient care was not compromised at any point. To ensure success, checklists for faculty were devised and adhered to. A medium fidelity manikin with live monitoring was used. The first part of the scenario involved management of a surgical emergency by theatre staff. The second part involved management of a fire in the operating theatre while an emergency procedure was being undertaken. To achieve maximum learning potential, debriefing was provided immediately after each part of the scenario. A fire safety officer was present as a content expert. Latent errors (hidden errors in the workplace, staff knowledge etc) were identified. Malfunctioning of theatre floor windows and staff unawareness about the location of an evacuation site were some of the identified latent errors. Thorough feedback to address these issues was provided to the participants on the day. A detailed report of the training session was given to the relevant departments. This resulted in the equipment faults being rectified. The training session was a very positive experience and helped not only in improving participants' knowledge, behaviour and confidence but also it made system and environment better equipped.

Using Learning Strategies to Inhibit the Nocebo Effect.

PubMed

Quinn, Veronica F; Colagiuri, Ben

2018-01-01

Learning is a key mechanism underpinning the development of the nocebo effect. The learning literature has cataloged and explored numerous ways in which the environment can be manipulated to prevent, reduce, or eradicate learning. Knowledge of these processes could be used to both inhibit the development of nocebo effects and reduce already established nocebo learning. This review describes the available evidence on how such learning strategies have, or could be, applied to reduce the nocebo effect in both healthy participants and patients to date. These learning strategies include overshadowing, latent inhibition, extinction, and contingency degradation. These strategies represent important new avenues for investigation and should be used by researchers to design and test interventions to reduce nocebo effects. © 2018 Elsevier Inc. All rights reserved.

Impaired acquisition of novel grapheme-color correspondences in synesthesia

PubMed Central

Brang, David; Ghiam, Michael; Ramachandran, Vilayanur S.

2013-01-01

Grapheme-color synesthesia is a neurological phenomenon in which letters and numbers (graphemes) consistently evoke particular colors (e.g., A may be experienced as red). These sensations are thought to arise through the cross-activation of grapheme processing regions in the fusiform gyrus and color area V4, supported by anatomical and functional imaging. However, the developmental onset of grapheme-color synesthesia remains elusive as research in this area has largely relied on self-report of these experiences in children. One possible account suggests that synesthesia is present at or near birth and initially binds basic shapes and forms to colors, which are later refined to grapheme-color associations through experience. Consistent with this view, studies show that similarly shaped letters and numbers tend to elicit similar colors in synesthesia and that some synesthetes consciously associate basic shapes with colors; research additionally suggests that synesthetic colors can emerge for newly learned characters with repeated presentation. This model further predicts that the initial shape-color correspondences in synesthesia may persist as implicit associations, driving the acquisition of colors for novel characters. To examine the presence of latent color associations for novel characters, synesthetes and controls were trained on pre-defined associations between colors and complex shapes, on the assumption that the prescribed shape-color correspondences would on average differ from implicit synesthetic associations. Results revealed synesthetes were less accurate than controls to learn novel shape-color associations, consistent with our suggestion that implicit form-color associations conflicted with the learned pairings. PMID:24198775

Impaired acquisition of novel grapheme-color correspondences in synesthesia.

PubMed

Brang, David; Ghiam, Michael; Ramachandran, Vilayanur S

2013-01-01

Grapheme-color synesthesia is a neurological phenomenon in which letters and numbers (graphemes) consistently evoke particular colors (e.g., A may be experienced as red). These sensations are thought to arise through the cross-activation of grapheme processing regions in the fusiform gyrus and color area V4, supported by anatomical and functional imaging. However, the developmental onset of grapheme-color synesthesia remains elusive as research in this area has largely relied on self-report of these experiences in children. One possible account suggests that synesthesia is present at or near birth and initially binds basic shapes and forms to colors, which are later refined to grapheme-color associations through experience. Consistent with this view, studies show that similarly shaped letters and numbers tend to elicit similar colors in synesthesia and that some synesthetes consciously associate basic shapes with colors; research additionally suggests that synesthetic colors can emerge for newly learned characters with repeated presentation. This model further predicts that the initial shape-color correspondences in synesthesia may persist as implicit associations, driving the acquisition of colors for novel characters. To examine the presence of latent color associations for novel characters, synesthetes and controls were trained on pre-defined associations between colors and complex shapes, on the assumption that the prescribed shape-color correspondences would on average differ from implicit synesthetic associations. Results revealed synesthetes were less accurate than controls to learn novel shape-color associations, consistent with our suggestion that implicit form-color associations conflicted with the learned pairings.

Scientific evaluation of an intra-curricular educational kit to foster inquiry-based learning (IBL)

NASA Astrophysics Data System (ADS)

Debaes, Nathalie; Cords, Nina; Prasad, Amrita; Fischer, Robert; Euler, Manfred; Thienpont, Hugo

2014-07-01

Society becomes increasingly dependent on photonics technologies; however there is an alarming lack of technological awareness among secondary school students. They associate photonics with experiments and components in the class room that seem to bear little relevance to their daily life. The Rocard Report [5] highlights the need for fostering students' scientific skills and technological awareness and identifies inquiry based learning (IBL) as a means to achieve this. Students need to actively do science rather than be silent spectators. The `Photonics Explorer' kit was developed as an EU funded project to equip teachers, free-of-charge, with educational material designed to excite, engage and educate European secondary school students using guided inquiry based learning techniques. Students put together their own experiments using up-to-date versatile components, critically interpret results and relate the conclusions to relevant applications in their daily life. They work hands-on with the material, thus developing and honing their scientific and analytical skills that are otherwise latent in a typical class room situation. A qualitative and quantitative study of the impact of the kit in the classroom was undertaken with 50 kits tested in 7 EU countries with over 1500 students in the local language. This paper reports on the results of the EU wide field tests that show the positive impact of the kit in raising the self-efficacy, scientific skills and interest in science among students and the effectiveness of the kit in implementing IBL strategies in classrooms across EU.

National Profiles of Classroom Quality and Family Involvement: A Multilevel Examination of Proximal Influences on Head Start Children's School Readiness

ERIC Educational Resources Information Center

Bulotsky-Shearer, Rebecca J.; Wen, Xiaoli; Faria, Ann-Marie; Hahs-Vaughn, Debbie L.; Korfmacher, Jon

2012-01-01

Guided by a developmental and ecological model, the study employed latent profile analysis to identify a multilevel typology of family involvement and Head Start classroom quality. Using the nationally representative Head Start Family and Child Experiences Survey (FACES 1997; N = 1870), six multilevel latent profiles were estimated, characterized…

Multidimensional Latent Markov Models in a Developmental Study of Inhibitory Control and Attentional Flexibility in Early Childhood

ERIC Educational Resources Information Center

Bartolucci, Francesco; Solis-Trapala, Ivonne L.

2010-01-01

We demonstrate the use of a multidimensional extension of the latent Markov model to analyse data from studies with repeated binary responses in developmental psychology. In particular, we consider an experiment based on a battery of tests which was administered to pre-school children, at three time periods, in order to measure their inhibitory…

Latent class analysis of diagnostic science assessment data using Bayesian networks

NASA Astrophysics Data System (ADS)

Steedle, Jeffrey Thomas

2008-10-01

Diagnostic science assessments seek to draw inferences about student understanding by eliciting evidence about the mental models that underlie students' reasoning about physical systems. Measurement techniques for analyzing data from such assessments embody one of two contrasting assessment programs: learning progressions and facet-based assessments. Learning progressions assume that students have coherent theories that they apply systematically across different problem contexts. In contrast, the facet approach makes no such assumption, so students should not be expected to reason systematically across different problem contexts. A systematic comparison of these two approaches is of great practical value to assessment programs such as the National Assessment of Educational Progress as they seek to incorporate small clusters of related items in their tests for the purpose of measuring depth of understanding. This dissertation describes an investigation comparing learning progression and facet models. Data comprised student responses to small clusters of multiple-choice diagnostic science items focusing on narrow aspects of understanding of Newtonian mechanics. Latent class analysis was employed using Bayesian networks in order to model the relationship between students' science understanding and item responses. Separate models reflecting the assumptions of the learning progression and facet approaches were fit to the data. The technical qualities of inferences about student understanding resulting from the two models were compared in order to determine if either modeling approach was more appropriate. Specifically, models were compared on model-data fit, diagnostic reliability, diagnostic certainty, and predictive accuracy. In addition, the effects of test length were evaluated for both models in order to inform the number of items required to obtain adequately reliable latent class diagnoses. Lastly, changes in student understanding over time were studied with a longitudinal model in order to provide educators and curriculum developers with a sense of how students advance in understanding over the course of instruction. Results indicated that expected student response patterns rarely reflected the assumptions of the learning progression approach. That is, students tended not to systematically apply a coherent set of ideas across different problem contexts. Even those students expected to express scientifically-accurate understanding had substantial probabilities of reporting certain problematic ideas. The learning progression models failed to make as many substantively-meaningful distinctions among students as the facet models. In statistical comparisons, model-data fit was better for the facet model, but the models were quite comparable on all other statistical criteria. Studying the effects of test length revealed that approximately 8 items are needed to obtain adequate diagnostic certainty, but more items are needed to obtain adequate diagnostic reliability. The longitudinal analysis demonstrated that students either advance in their understanding (i.e., switch to the more advanced latent class) over a short period of instruction or stay at the same level. There was no significant relationship between the probability of changing latent classes and time between testing occasions. In all, this study is valuable because it provides evidence informing decisions about modeling and reporting on student understanding, it assesses the quality of measurement available from short clusters of diagnostic multiple-choice items, and it provides educators with knowledge of the paths that student may take as they advance from novice to expert understanding over the course of instruction.

Investigation of Sensible and Latent Heat Storage System using various HTF

NASA Astrophysics Data System (ADS)

Beemkumar, N.; Karthikeyan, A.; Manoj, A.; Keerthan, J. S.; Stallan, Joseph Paul; Amithkishore, P.

2017-05-01

The objective of the work is investigating the latent heat storage system by varying heat transfer fluid (HTF). In this experiment, the effect of using different heat transfer fluids on the combined system is studied while using a low melting phase change material (PCM) i.e., paraffin wax. The heat transfer fluids chosen are water (low boiling fluid) and Therminol-66 (High boiling fluid). A comparison is made between the heat transfers by employing both the Heat transfer fluids. In the beginning, water is made to flow as the HTF and the charging process is undertaken followed by the discharging process by utilizing the different encapsulation materials namely, copper, aluminium and brass. These processes are then repeated for therminol-66 as HTF. At the end of the experiment it was concluded that even though therminol-66 enhances the latent heat storage capacity, water offers a higher sensible heat storage capacity, making it a better HTF for low melting PCM. Similar to above said process the experiments can be conducted for high and medium range melting point PCM with variation of HTF.

Reciprocal relations between cognitive neuroscience and formal cognitive models: opposites attract?

PubMed

Forstmann, Birte U; Wagenmakers, Eric-Jan; Eichele, Tom; Brown, Scott; Serences, John T

2011-06-01

Cognitive neuroscientists study how the brain implements particular cognitive processes such as perception, learning, and decision-making. Traditional approaches in which experiments are designed to target a specific cognitive process have been supplemented by two recent innovations. First, formal cognitive models can decompose observed behavioral data into multiple latent cognitive processes, allowing brain measurements to be associated with a particular cognitive process more precisely and more confidently. Second, cognitive neuroscience can provide additional data to inform the development of formal cognitive models, providing greater constraint than behavioral data alone. We argue that these fields are mutually dependent; not only can models guide neuroscientific endeavors, but understanding neural mechanisms can provide key insights into formal models of cognition. Copyright © 2011 Elsevier Ltd. All rights reserved.

The association between cognition and academic performance in Ugandan children surviving malaria with neurological involvement.

PubMed

Bangirana, Paul; Menk, Jeremiah; John, Chandy C; Boivin, Michael J; Hodges, James S

2013-01-01

The contribution of different cognitive abilities to academic performance in children surviving cerebral insult can guide the choice of interventions to improve cognitive and academic outcomes. This study's objective was to identify which cognitive abilities are associated with academic performance in children after malaria with neurological involvement. 62 Ugandan children with a history of malaria with neurological involvement were assessed for cognitive ability (working memory, reasoning, learning, visual spatial skills, attention) and academic performance (reading, spelling, arithmetic) three months after the illness. Linear regressions were fit for each academic score with the five cognitive outcomes entered as predictors. Adjusters in the analysis were age, sex, education, nutrition, and home environment. Exploratory factor analysis (EFA) and structural equation models (SEM) were used to determine the nature of the association between cognition and academic performance. Predictive residual sum of squares was used to determine which combination of cognitive scores was needed to predict academic performance. In regressions of a single academic score on all five cognitive outcomes and adjusters, only Working Memory was associated with Reading (coefficient estimate = 0.36, 95% confidence interval = 0.10 to 0.63, p<0.01) and Spelling (0.46, 0.13 to 0.78, p<0.01), Visual Spatial Skills was associated with Arithmetic (0.15, 0.03 to 0.26, p<0.05), and Learning was associated with Reading (0.06, 0.00 to 0.11, p<0.05). One latent cognitive factor was identified using EFA. The SEM found a strong association between this latent cognitive ability and each academic performance measure (P<0.0001). Working memory, visual spatial ability and learning were the best predictors of academic performance. Academic performance is strongly associated with the latent variable labelled "cognitive ability" which captures most of the variation in the individual specific cognitive outcome measures. Working memory, visual spatial skills, and learning together stood out as the best combination to predict academic performance.

Ecology-centered experiences among children and adolescents: A qualitative and quantitative analysis

NASA Astrophysics Data System (ADS)

Orton, Judy

The present research involved two studies that considered ecology-centered experiences (i.e., experiences with living things) as a factor in children's environmental attitudes and behaviors and adolescents' ecological understanding. The first study (Study 1) examined how a community garden provides children in an urban setting the opportunity to learn about ecology through ecology-centered experiences. To do this, I carried out a yearlong ethnographic study at an urban community garden located in a large city in the Southeastern United States. Through participant observations and informal interviews of community garden staff and participants, I found children had opportunities to learn about ecology through ecology-centered experiences (e.g., interaction with animals) along with other experiences (e.g., playing games, reading books). In light of previous research that shows urban children have diminished ecological thought---a pattern of thought that privileges the relationship between living things---because of their lack of ecology-centered experiences (Coley, 2012), the present study may have implications for urban children to learn about ecology. As an extension of Study 1, I carried out a second study (Study 2) to investigate how ecology-centered experiences contribute to adolescents' environmental attitudes and behaviors in light of other contextual factors, namely environmental responsibility support, ecological thought, age and gender. Study 2 addressed three research questions. First, does ecological thought---a pattern of thought that privileges the relationship between living things---predict environmental attitudes and behaviors (EAB)? Results showed ecological thought did not predict EAB, an important finding considering the latent assumptions of previous research about the relationship between these two factors (e.g., Brugger, Kaiser, & Roczen, 2011). Second, do two types of contextual support, ecology-centered experiences (i.e., experiences with living things) and environmental responsibility support (i.e., support through the availability of environmentally responsible models) predict EAB? As predicted, results showed that ecology-centered experiences predicted EAB; yet, when environmental responsibility support was taken into consideration, ecology-centered experiences no longer predicted EAB. These findings suggested environmental responsibility support was a stronger predictor than ecology-centered experiences. Finally, do age and gender predict EAB? Consistent with previous research (e.g., Alp, Ertepiner, Tekkaya, & Yilmaz, 2006), age and gender significantly predicted EAB.

Atmospheric responses to sensible and latent heating fluxes over the Gulf Stream

NASA Astrophysics Data System (ADS)

Minobe, S.; Ida, T.; Takatama, K.

2016-12-01

Air-sea interaction over mid-latitude oceanic fronts such as the Gulf Stream attracted large attention in the last decade. Observational analyses and modelling studies revealed that atmospheric responses over the Gulf Stream including surface wind convergence, enhanced precipitation and updraft penetrating to middle-to-upper troposphere roughly on the Gulf Stream current axis or on the warmer flank of sea-surface temperature (SST) front of the Gulf Stream . For these atmospheric responses, oceanic information should be transmitted to the atmosphere via turbulent heat fluxes, and thus the mechanisms for atmospheric responses can be understood better by examining latent and sensible air-sea heat fluxes more closely. Thus, the roles of the sensible and latent heat fluxes are examined by conducting a series of numerical experiments using the IPRC Regional Atmospheric Model over the Gulf Stream by applying SST smoothing for latent and sensible heating separately. The results indicate that the sensible and latent heat fluxes affect the atmosphere differently. Sensible heat flux intensifies surface wind convergence to produce sea-level pressure (SLP) anomaly. Latent heat flux supplies moistures and maintains enhanced precipitation. The different heat flux components cause upward wind velocity at different levels.

[Experience in using the latent activity of leukocytic lactate dehydrogenase isoenzymes for the integral estimate of the level of free radical oxidation in patients with neurotic disorders].

PubMed

Kuskov, M V

2006-06-01

The aggregatory properties of a leukocytic homogenate were studied by analyzing the activity of its lactate dehydrogenase (LDH) isoenzymes from patients with neurotic disorders on admission and during treatment. As a parameter reflecting the aggregatory properties of the leukocytic homogenate, the latent activity of LDH isoenzymes was studied. On admission, the patients were shown to have a lower latent activity, which restored during treatment to the control values, than in the control group. There was also a synchronous pattern of a change in the osmotic stability of red blood cells with the latent activity of leukocytic LDH isoenzymes in the treated patients. It is obvious that latent activity values reflect the level of free radical oxidation in the body. For detailed testing of the aggregatory properties of a cellular lysate, the trends in the latent activity of LDH isoenzymes were examined, which failed to reveal an unambiguous recovery of the observed parameters during therapy. Based on the findings, the author discusses whether this method can be used to analyze the time course of changes in a psychopathological process and to predict its outcome.

"Detached concern" of medical students in a cadaver dissection course: A phenomenological study.

PubMed

Tseng, Wei-Ting; Lin, Ya-Ping

2016-05-06

The cadaver dissection course remains a time-honored tradition in medical education, partly because of its importance in cultivating professional attitudes in students. This study aims to investigate students' attitudes-specifically characterized as "detached concern"-in a cadaver dissection course. An interpretative phenomenological analysis was performed with semi-structured, focus group interviews among 12 third-year medical students from a Taiwanese medical school to reveal their perceptions and learning experiences regarding human cadaver dissection. Based on these interviews, four relevant categories of perspectives were delineated: (1) initial emotional impact, (2) human referents, (3) coping strategies, and (4) ways of perceiving cadavers. Students were divided into two groups based on these categories. Students in Group 1 developed mechanisms described as "detachment" to cope with their initial emotional reactions to cadaveric dissection, which was noted to have disruptive effects on their learning. They considered human referents to be learning obstacles and avoided contact with or thinking about the human referents while performing dissections. Some of them faced a conflict between perceiving the cadaver as a learning tool versus as a human being. This impasse could be resolved if they latently adopted a "perspective switch" between the concept of a learning tool (rational aspect) and a human being (sensitive aspect). The students in Group 2 had no obvious initial emotional reaction. For them, the human referents functioned as learning supports, and the cadavers were consistently perceived as humans. These students held the notion that "cadaver dissection is an act of love"; therefore, they did not experience any need to detach themselves from their feelings during dissection. This alternative attitude revealed that detached concern alone is not sufficient to describe the entire range of medical students' attitudes toward cadaver dissection. Anat Sci Educ 9: 265-271. © 2015 American Association of Anatomists. © 2015 American Association of Anatomists.

Robust Bayesian clustering.

PubMed

Archambeau, Cédric; Verleysen, Michel

2007-01-01

A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide-and-conquer approach. They are commonly used for density estimation and clustering tasks, but are sensitive to outliers. The Student-t distribution has heavier tails than the Gaussian distribution and is therefore less sensitive to any departure of the empirical distribution from Gaussianity. As a consequence, the Student-t distribution is suitable for constructing robust mixture models. In this work, we formalize the Bayesian Student-t mixture model as a latent variable model in a different way from Svensén and Bishop [Svensén, M., & Bishop, C. M. (2005). Robust Bayesian mixture modelling. Neurocomputing, 64, 235-252]. The main difference resides in the fact that it is not necessary to assume a factorized approximation of the posterior distribution on the latent indicator variables and the latent scale variables in order to obtain a tractable solution. Not neglecting the correlations between these unobserved random variables leads to a Bayesian model having an increased robustness. Furthermore, it is expected that the lower bound on the log-evidence is tighter. Based on this bound, the model complexity, i.e. the number of components in the mixture, can be inferred with a higher confidence.

Emergent latent symbol systems in recurrent neural networks

NASA Astrophysics Data System (ADS)

Monner, Derek; Reggia, James A.

2012-12-01

Fodor and Pylyshyn [(1988). Connectionism and cognitive architecture: A critical analysis. Cognition, 28(1-2), 3-71] famously argued that neural networks cannot behave systematically short of implementing a combinatorial symbol system. A recent response from Frank et al. [(2009). Connectionist semantic systematicity. Cognition, 110(3), 358-379] claimed to have trained a neural network to behave systematically without implementing a symbol system and without any in-built predisposition towards combinatorial representations. We believe systems like theirs may in fact implement a symbol system on a deeper and more interesting level: one where the symbols are latent - not visible at the level of network structure. In order to illustrate this possibility, we demonstrate our own recurrent neural network that learns to understand sentence-level language in terms of a scene. We demonstrate our model's learned understanding by testing it on novel sentences and scenes. By paring down our model into an architecturally minimal version, we demonstrate how it supports combinatorial computation over distributed representations by using the associative memory operations of Vector Symbolic Architectures. Knowledge of the model's memory scheme gives us tools to explain its errors and construct superior future models. We show how the model designs and manipulates a latent symbol system in which the combinatorial symbols are patterns of activation distributed across the layers of a neural network, instantiating a hybrid of classical symbolic and connectionist representations that combines advantages of both.

Epstein-Barr virus (EBV)-encoded dUTPase and chronic restraint induce impaired learning and memory and sickness responses.

PubMed

Aubrecht, Taryn G; Weil, Zachary M; Ariza, Maria Eugenia; Williams, Marshall; Reader, Brenda F; Glaser, Ronald; Sheridan, John F; Nelson, Randy J

2014-10-01

Most adult humans have been infected with Epstein-Barr virus (EBV) and carry the latent virus. The EBV genome codes for several proteins that form an early antigen complex important for viral replication; one of these proteins is deoxyuridine triphosphate nucleotidohydrolase (dUTPase). The EBV-encoded dUTPase can induce sickness responses in mice. Because stress can increase latent virus reactivation, we hypothesized that chronic restraint would exacerbate sickness behaviors elicited by EBV-encoded dUTPase. Male Swiss-Webster mice were injected daily for 15 days with either saline or EBV-encoded dUTPase. Additionally, half of the mice from each condition were either restrained for 3h daily or left undisturbed. Restraint stress impaired learning and memory in the passive avoidance chamber; impaired learning and memory was due to EBV-encoded dUTPase injected into restrained mice. EBV-encoded dUTPase induced sickness responses and restraint stress interacts with EBV-encoded dUTPase to exacerbate the sickness response. These data support a role for EBV-encoded dUTPase and restraint stress in altering the pathophysiology of EBV independent of viral replication. Copyright © 2014 Elsevier Inc. All rights reserved.

The effects of estrogens on learning in rats with chronic brain cholinergic deficiency in a Morris water test. Identification of the "passive swimming" component.

PubMed

Mukhina, T V; Lermontova, N N; Van'kin, G I; Oettel, M; P'chev, V K; Bachurin, S O

2004-03-01

Chronic decreases in brain cholinergic functions due to intraventricular administration of the neurotoxin AF64A were accompanied by increases in the latent period of locating an invisible platform during training of rats in a Morris water test, as compared with control sham-operated animals. Recordings of the animals' movement trajectories using a video camera along with an original computer program (Behavioral Vision) showed that administration of 17beta-estradiol and its synthetic analog J-861 (0.2 mg/kg p.o. daily for seven days before and 10 days after single intraventricular injections of AF64A) improved learning. The directivity of platform search trajectories was assessed quantitatively using a new parameter--trajectory straightness. Introduction of the "passive swimming" parameter allowed periods of immobility in water to be identified within the total latent period in animals after administration of AF64A; 17beta-estradiol but not J-861 "eliminated" these periods. The new parameters (especially trajectory straightness) allowed the ability to learn to be discriminated from decreases in mobility, including mobility losses due to study agents, in the Morris water test.

Post-traumatic stress symptoms and structure among orphan and vulnerable children and adolescents in Zambia.

PubMed

Familiar, Itziar; Murray, Laura; Gross, Alden; Skavenski, Stephanie; Jere, Elizabeth; Bass, Judith

2014-11-01

Scant information exists on PTSD symptoms and structure in youth from developing countries. We describe the symptom profile and exposure to trauma experiences among 343 orphan and vulnerable children and adolescents from Zambia. We distinguished profiles of post-traumatic stress symptoms using latent class analysis. Average number of trauma-related symptoms (21.6; range 0-38) was similar across sex and age. Latent class model suggested 3 classes varying by level of severity: low (31% of the sample), medium (45% of the sample), and high (24% of the sample) symptomatology. Results suggest that PTSD is a continuously distributed latent trait.

Latent Heating Retrievals Using the TRMM Precipitation Radar: A Multi-Seasonal Study

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Meneghini, B.; Halverson, J.; Johnson, R.; Simpson, J.; Einaudi, Franco (Technical Monitor)

2000-01-01

The Goddard Convective-Stratiform Heating (CSH) algorithm is used to retrieve profiles of latent heating over the global tropics for a period of several months using TRMM precipitation radar data. The seasonal variation of heating over the tropics is then examined. The period of interest also coincides with several TRMM field campaigns that recently occurred over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and in the central Pacific in 1999 (KWAJEX). Sounding diagnosed Q1 budgets from these experiments could provide a means of validating the retrieved profiles of latent heating from the CSH algorithm.

Altered intrinsic functional connectivity in the latent period of epileptogenesis in a temporal lobe epilepsy model.

PubMed

Lee, Hyoin; Jung, Seungmoon; Lee, Peter; Jeong, Yong

2017-10-01

The latent period, a seizure-free phase, is the duration between brain injury and the onset of spontaneous recurrent seizures (SRSs) during epileptogenesis. The latent period is thought to involve several progressive pathophysiological events that lead to the evolution of the chronic epilepsy phase. Hence, it is vital to investigate the changes in the latent period during epileptogenesis in order to better understand temporal lobe epilepsy (TLE), and to achieve early diagnosis and appropriate management of the condition. Accordingly, recent studies with patients with TLE using resting-state functional magnetic resonance imaging (rs-fMRI) have reported that alterations of resting-state functional connectivity (rsFC) during the chronic period are associated with some clinical manifestations, including learning and memory impairments, emotional instability, and social behavior deficits, in addition to repetitive seizure episodes. In contrast, the changes in the intrinsic rsFC during epileptogenesis, particularly during the latent period, remain unclear. In this study, we investigated the alterations in intrinsic rsFC during the latent and chronic periods in a pilocarpine-induced TLE mouse model using intrinsic optical signal imaging (IOSI). This technique can monitor the changes in the local hemoglobin concentration according to neuronal activity and can help investigate large-scale brain intrinsic networks. After seeding on the anatomical regions of interest (ROIs) and calculating the correlation coefficients between each ROI, we established and compared functional correlation matrices and functional connectivity maps during the latent and chronic periods of epilepsy. We found a decrease in the interhemispheric rsFC at the frontal and temporal regions during both the latent and chronic periods. Furthermore, a significant decrease in the interhemispheric rsFC was observed in the somatosensory area during the chronic period. Changes in network configurations during epileptogenesis were examined by graph theoretical network analysis. Interestingly, increase in the power of low frequency oscillations was observed during the latent period. These results suggest that, even if there are no apparent ictal seizure events during the latent period, there are ongoing changes in the rsFC in the epileptic brain. Furthermore, these results suggest that the pathophysiology of epilepsy may be related to widespread altered intrinsic functional connectivity. These findings can help enhance our understanding of epileptogenesis, and accordingly, changes in intrinsic functional connectivity can serve as an early diagnosis. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions.

PubMed

Drouard, Vincent; Horaud, Radu; Deleforge, Antoine; Ba, Sileye; Evangelidis, Georgios

2017-03-01

Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth. Head-pose estimation is challenging, because it must cope with changing illumination conditions, variabilities in face orientation and in appearance, partial occlusions of facial landmarks, as well as bounding-box-to-face alignment errors. We propose to use a mixture of linear regressions with partially-latent output. This regression method learns to map high-dimensional feature vectors (extracted from bounding boxes of faces) onto the joint space of head-pose angles and bounding-box shifts, such that they are robustly predicted in the presence of unobservable phenomena. We describe in detail the mapping method that combines the merits of unsupervised manifold learning techniques and of mixtures of regressions. We validate our method with three publicly available data sets and we thoroughly benchmark four variants of the proposed algorithm with several state-of-the-art head-pose estimation methods.

State and trait effects on individual differences in children's mathematical development.

PubMed

Bailey, Drew H; Watts, Tyler W; Littlefield, Andrew K; Geary, David C

2014-11-01

Substantial longitudinal relations between children's early mathematics achievement and their much later mathematics achievement are firmly established. These findings are seemingly at odds with studies showing that early educational interventions have diminishing effects on children's mathematics achievement across time. We hypothesized that individual differences in children's later mathematical knowledge are more an indicator of stable, underlying characteristics related to mathematics learning throughout development than of direct effects of early mathematical competency on later mathematical competency. We tested this hypothesis in two longitudinal data sets, by simultaneously modeling effects of latent traits (stable characteristics that influence learning across time) and states (e.g., prior knowledge) on children's mathematics achievement over time. Latent trait effects on children's mathematical development were substantially larger than state effects. Approximately 60% of the variance in trait mathematics achievement was accounted for by commonly used control variables, such as working memory, but residual trait effects remained larger than state effects. Implications for research and practice are discussed. © The Author(s) 2014.

First-Grade Predictors of Mathematical Learning Disability: A Latent Class Trajectory Analysis

PubMed Central

Geary, David C.; Bailey, Drew H.; Littlefield, Andrew; Wood, Phillip; Hoard, Mary K.; Nugent, Lara

2009-01-01

Kindergarten to 3rd grade mathematics achievement scores from a prospective study of mathematical development were subjected to latent growth trajectory analyses (n = 306). The four corresponding classes included children with mathematical learning disability (MLD, 6% of sample), and low (LA, 50%), typically (TA, 39%) and high (HA, 5%) achieving children. The groups were administered a battery of intelligence (IQ), working memory, and mathematical-cognition measures in 1st grade. The children with MLD had general deficits in working memory and IQ, and potentially more specific deficits on measures of number sense. The LA children did not have working memory or IQ deficits, but showed moderate deficits on these number sense measures and for addition fact retrieval. The distinguishing features of the HA children were a strong visuospatial working memory, a strong number sense, and frequent use of memory-based processes to solve addition problems. Implications for the early identification of children at risk for poor mathematics achievement are discussed. PMID:20046817

State and Trait Effects on Individual Differences in Children's Mathematical Development

PubMed Central

Bailey, Drew H.; Watts, Tyler W.; Littlefield, Andrew K.; Geary, David C.

2015-01-01

Substantial longitudinal relations between children's early mathematics achievement and their much later mathematics achievement are firmly established. These findings are seemingly at odds with studies showing that early educational interventions have diminishing effects on children's mathematics achievement across time. We hypothesized that individual differences in children's later mathematical knowledge are more an indicator of stable, underlying characteristics related to mathematics learning throughout development than of direct effects of early mathematical competency on later mathematical competency. We tested this hypothesis in two longitudinal data sets, by simultaneously modeling effects of latent traits (stable characteristics that influence learning across time) and states (e.g., prior knowledge) on children's mathematics achievement over time. Latent trait effects on children's mathematical development were substantially larger than state effects. Approximately 60% of the variance in trait mathematics achievement was accounted for by commonly used control variables, such as working memory, but residual trait effects remained larger than state effects. Implications for research and practice are discussed. PMID:25231900

Multi-label learning with fuzzy hypergraph regularization for protein subcellular location prediction.

PubMed

Chen, Jing; Tang, Yuan Yan; Chen, C L Philip; Fang, Bin; Lin, Yuewei; Shang, Zhaowei

2014-12-01

Protein subcellular location prediction aims to predict the location where a protein resides within a cell using computational methods. Considering the main limitations of the existing methods, we propose a hierarchical multi-label learning model FHML for both single-location proteins and multi-location proteins. The latent concepts are extracted through feature space decomposition and label space decomposition under the nonnegative data factorization framework. The extracted latent concepts are used as the codebook to indirectly connect the protein features to their annotations. We construct dual fuzzy hypergraphs to capture the intrinsic high-order relations embedded in not only feature space, but also label space. Finally, the subcellular location annotation information is propagated from the labeled proteins to the unlabeled proteins by performing dual fuzzy hypergraph Laplacian regularization. The experimental results on the six protein benchmark datasets demonstrate the superiority of our proposed method by comparing it with the state-of-the-art methods, and illustrate the benefit of exploiting both feature correlations and label correlations.

An instance theory of associative learning.

PubMed

Jamieson, Randall K; Crump, Matthew J C; Hannah, Samuel D

2012-03-01

We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.

The Effect of Reform-Based Science Teaching on SES-Associated Achievement Gap on PISA 2006: A Comparative Study of the United States and Taiwan

NASA Astrophysics Data System (ADS)

Tang, Nai-En

The goal of this study is to examine how reform-based science teaching has been implemented and whether reform-based science teaching has promoted education equity through being available and beneficial for students from different socioeconomic status (SES) family backgrounds in the U.S. and Taiwan. No existing study used large-scale assessment to investigate the implementation and outcomes of the science reform movement in the U.S. and Taiwan. This study was developed to fill this gap using the Program of International Student Assessment (PISA) 2006 data including 5,611 students in the United States and 5995 students in Taiwan. A Latent Profile Analysis (LPA) was used to classify students into different science learning subgroups to understand how broadly reform-based science learning has been implemented in classrooms. The results showed that students in the U.S. had more opportunity to learn science through the reform-based learning activities than students in Taiwan. Latent Class Regression (LCR) and Structural Equation Modeling (SEM) were used for examining the availability of reform-based science teaching in both countries. The results showed that in the U.S., higher SES students had more opportunity to learn science reform-based learning activities. On the other hand, students' SES had no association with reform-based science learning in Taiwan. Regression Mixture Modeling and SEM were used to examine whether there was an association between reform-based science teaching and SES-associated achievement gaps. The results found no evidence to support the claim that reform-based science teaching helps to minimize SES-associated achievement gaps in both countries.

Dissociable Hippocampal and Amygdalar D1-like receptor contribution to Discriminated Pavlovian conditioned approach learning

PubMed Central

Andrzejewski, Matthew E; Ryals, Curtis

2016-01-01

Pavlovian conditioning is an elementary form of reward-related behavioral adaptation. The mesolimbic dopamine system is widely considered to mediate critical aspects of reward-related learning. For example, initial acquisition of positively-reinforced operant behavior requires dopamine (DA) D1 receptor (D1R) activation in the basolateral amygdala (BLA), central nucleus of the amygdala (CeA), and the ventral subiculum (vSUB). However, the role of D1R activation in these areas on appetitive, non-drug-related, Pavlovian learning is not currently known. In separate experiments, microinfusions of the D1-like receptor antagonist SCH-23390 (3.0 nmol/0.5 μL per side) into the amygdala and subiculum preceded discriminated Pavlovian conditioned approach (dPCA) training sessions. D1-like antagonism in all three structures impaired the acquisition of discriminated approach, but had no effect on performance after conditioning was asymptotic. Moreover, dissociable effects of D1-like antagonism in the three structures on components of discriminated responding were obtained. Lastly, the lack of latent inhibition in drug-treated groups may elucidate the role of D1-like in reward-related Pavlovian conditioning. The present data suggest a role for the D1 receptors in the amygdala and hippocampus in learning the significance of conditional stimuli, but not in the expression of conditional responses. PMID:26632336

[Effect of serotonin-modulated anticonsolidation protein on formation of long-term memory in carps Cyprinus carpio in the model of active avoidance learning].

PubMed

Garina, D V; Mekhtiev, A A

2014-01-01

Effect of serotonin-modulated anticonsolidation protein (SMAP) that has property of disturbing formation of memory trace in mammals and of learning and memory in teleost fish was studied in the model of active avoidance learning. The experiment was performed in three stages: (1) fry of carps Cyprinus carpio L. was injected intracerebrovenricularly with the SMAP protein at a dose of 0.3 μg/g; control individuals were administered with equal amount of the buffered saline for poikilothermic animals; (2) 24 h after the injection, fish were learnt during 8 sèances for 2 days the conditioned reflex of active avoidance; (3) 48 h after the learning the testing of the skill was performed. The administration of the protein was shown to lead to disturbance of reproduction of the skill in the fish: the latent time of the skill reproduction in experimental individuals exceeded that in control fish more than two times, while the number of individuals succeeding the task in the experimental group was non-significantly lower than in the control group. However, unlike mammals, injection of the SMAP protein in this model produced no effect on the process of learning in carps. Thus, there was first demonstrated the inhibiting effect of the SMAP protein whose concentration correlated positively with the content of the neurotransmitter serotonin in brain on consolidation of memory traces in teleost fish.

Scaling water and energy fluxes in climate systems - Three land-atmospheric modeling experiments

NASA Technical Reports Server (NTRS)

Wood, Eric F.; Lakshmi, Venkataraman

1993-01-01

Three numerical experiments that investigate the scaling of land-surface processes - either of the inputs or parameters - are reported, and the aggregated processes are compared to the spatially variable case. The first is the aggregation of the hydrologic response in a catchment due to rainfall during a storm event and due to evaporative demands during interstorm periods. The second is the spatial and temporal aggregation of latent heat fluxes, as calculated from SiB. The third is the aggregation of remotely sensed land vegetation and latent and sensible heat fluxes using TM data from the FIFE experiment of 1987 in Kansas. In all three experiments it was found that the surface fluxes and land characteristics can be scaled, and that macroscale models based on effective parameters are sufficient to account for the small-scale heterogeneities investigated.

Modeling conductive heat transfer during high-pressure thawing processes: determination of latent heat as a function of pressure.

PubMed

Denys, S; Van Loey, A M; Hendrickx, M E

2000-01-01

A numerical heat transfer model for predicting product temperature profiles during high-pressure thawing processes was recently proposed by the authors. In the present work, the predictive capacity of the model was considerably improved by taking into account the pressure dependence of the latent heat of the product that was used (Tylose). The effect of pressure on the latent heat of Tylose was experimentally determined by a series of freezing experiments conducted at different pressure levels. By combining a numerical heat transfer model for freezing processes with a least sum of squares optimization procedure, the corresponding latent heat at each pressure level was estimated, and the obtained pressure relation was incorporated in the original high-pressure thawing model. Excellent agreement with the experimental temperature profiles for both high-pressure freezing and thawing was observed.

Serotonin Transporter Knockout Rats Show Improved Strategy Set-Shifting and Reduced Latent Inhibition

ERIC Educational Resources Information Center

Nonkes, Lourens J. P.; van de Vondervoort, Ilse I. G. M.; de Leeuw, Mark J. C.; Wijlaars, Linda P.; Maes, Joseph H. R.; Homberg, Judith R.

2012-01-01

Behavioral flexibility is a cognitive process depending on prefrontal areas allowing adaptive responses to environmental changes. Serotonin transporter knockout (5-HTT[superscript -/-]) rodents show improved reversal learning in addition to orbitofrontal cortex changes. Another form of behavioral flexibility, extradimensional strategy set-shifting…

Lessons Learned in Part-of-Speech Tagging of Conversational Speech

DTIC Science & Technology

2010-10-01

for conversational speech recognition. In Plenary Meeting and Symposium on Prosody and Speech Processing. Slav Petrov and Dan Klein. 2007. Improved...inference for unlexicalized parsing. In HLT-NAACL. Slav Petrov. 2010. Products of random latent variable grammars. In HLT-NAACL. Brian Roark, Yang Liu

Auto-Relevancy Baseline: A Hybrid System Without Human Feedback

DTIC Science & Technology

2010-11-01

classical Bayes algorithm upon the pseudo-hybridization of SemanticA and Latent Semantic IndexingBC systems should smooth out historically high yet...black box emulated a machine learning topic expert. Similar to some Web methods, the initial topics within the legal document were expanded upon

Self-Explanations: How Students Study and Use Examples in Learning to Solve Problems.

DTIC Science & Technology

1987-11-03

the conversion of the declarativ ;? knowledge Into the procedural knowledge, whereas the encoding of the declarative knowledge is taken to be a...self-explanations during studying examples may make other latent or implicit components more accessible. Our data cannot discriminate between the

Latent Inhibition as a Function of US Intensity in a Two-Stage CER Procedure

ERIC Educational Resources Information Center

Rodriguez, Gabriel; Alonso, Gumersinda

2004-01-01

An experiment is reported in which the effect of unconditioned stimulus (US) intensity on latent inhibition (LI) was examined, using a two-stage conditioned emotional response (CER) procedure in rats. A tone was used as the pre-exposed and conditioned stimulus (CS), and a foot-shock of either a low (0.3 mA) or high (0.7 mA) intensity was used as…

Experimental investigation of the latent heat of vaporization in aqueous nanofluids

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

Lee, Soochan; Phelan, Patrick E., E-mail: phelan@asu.edu; Dai, Lenore

2014-04-14

This paper reports an experimental investigation of the latent heat of vaporization (h{sub fg}) in nanofluids. Two different types of nanoparticles, graphite and silver, suspended in deionized water were exposed to a continuous laser beam (130 mW, 532 nm) to generate boiling. The latent heat of vaporization in the nanofluids was determined by the measured vapor mass generation and the heat input. To ensure that the measured h{sub fg} values are independent of heating method, the experiments were repeated with an electrically heated hot wire as a primary heat input. These experiments show considerable variation in the h{sub fg} of nanofluids.more » That is, graphite nanofluid exhibits an increased h{sub fg} and silver nanofluid shows a decrease in h{sub fg} compared to the value for pure water. As such, these results indicate that relatively low mass fractions of nanoparticles can apparently create large changes in h{sub fg}.« less

The missing link: Predicting connectomes from noisy and partially observed tract tracing data

PubMed Central

Hinne, Max; Meijers, Annet; Tiesinga, Paul H. E.; Mørup, Morten

2017-01-01

Our understanding of the wiring map of the brain, known as the connectome, has increased greatly in the last decade, mostly due to technological advancements in neuroimaging techniques and improvements in computational tools to interpret the vast amount of available data. Despite this, with the exception of the C. elegans roundworm, no definitive connectome has been established for any species. In order to obtain this, tracer studies are particularly appealing, as these have proven highly reliable. The downside of tract tracing is that it is costly to perform, and can only be applied ex vivo. In this paper, we suggest that instead of probing all possible connections, hitherto unknown connections may be predicted from the data that is already available. Our approach uses a ‘latent space model’ that embeds the connectivity in an abstract physical space. Regions that are close in the latent space have a high chance of being connected, while regions far apart are most likely disconnected in the connectome. After learning the latent embedding from the connections that we did observe, the latent space allows us to predict connections that have not been probed previously. We apply the methodology to two connectivity data sets of the macaque, where we demonstrate that the latent space model is successful in predicting unobserved connectivity, outperforming two baselines and an alternative model in nearly all cases. Furthermore, we show how the latent spatial embedding may be used to integrate multimodal observations (i.e. anterograde and retrograde tracers) for the mouse neocortex. Finally, our probabilistic approach enables us to make explicit which connections are easy to predict and which prove difficult, allowing for informed follow-up studies. PMID:28141820

Examining the Association between Patient-Reported Symptoms of Attention and Memory Dysfunction with Objective Cognitive Performance: A Latent Regression Rasch Model Approach.

PubMed

Li, Yuelin; Root, James C; Atkinson, Thomas M; Ahles, Tim A

2016-06-01

Patient-reported cognition generally exhibits poor concordance with objectively assessed cognitive performance. In this article, we introduce latent regression Rasch modeling and provide a step-by-step tutorial for applying Rasch methods as an alternative to traditional correlation to better clarify the relationship of self-report and objective cognitive performance. An example analysis using these methods is also included. Introduction to latent regression Rasch modeling is provided together with a tutorial on implementing it using the JAGS programming language for the Bayesian posterior parameter estimates. In an example analysis, data from a longitudinal neurocognitive outcomes study of 132 breast cancer patients and 45 non-cancer matched controls that included self-report and objective performance measures pre- and post-treatment were analyzed using both conventional and latent regression Rasch model approaches. Consistent with previous research, conventional analysis and correlations between neurocognitive decline and self-reported problems were generally near zero. In contrast, application of latent regression Rasch modeling found statistically reliable associations between objective attention and processing speed measures with self-reported Attention and Memory scores. Latent regression Rasch modeling, together with correlation of specific self-reported cognitive domains with neurocognitive measures, helps to clarify the relationship of self-report with objective performance. While the majority of patients attribute their cognitive difficulties to memory decline, the Rash modeling suggests the importance of processing speed and initial learning. To encourage the use of this method, a step-by-step guide and programming language for implementation is provided. Implications of this method in cognitive outcomes research are discussed. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Domain adaptation via transfer component analysis.

PubMed

Pan, Sinno Jialin; Tsang, Ivor W; Kwok, James T; Yang, Qiang

2011-02-01

Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.

Discovering latent commercial networks from online financial news articles

NASA Astrophysics Data System (ADS)

Xia, Yunqing; Su, Weifeng; Lau, Raymond Y. K.; Liu, Yi

2013-08-01

Unlike most online social networks where explicit links among individual users are defined, the relations among commercial entities (e.g. firms) may not be explicitly declared in commercial Web sites. One main contribution of this article is the development of a novel computational model for the discovery of the latent relations among commercial entities from online financial news. More specifically, a CRF model which can exploit both structural and contextual features is applied to commercial entity recognition. In addition, a point-wise mutual information (PMI)-based unsupervised learning method is developed for commercial relation identification. To evaluate the effectiveness of the proposed computational methods, a prototype system called CoNet has been developed. Based on the financial news articles crawled from Google finance, the CoNet system achieves average F-scores of 0.681 and 0.754 in commercial entity recognition and commercial relation identification, respectively. Our experimental results confirm that the proposed shallow natural language processing methods are effective for the discovery of latent commercial networks from online financial news.

Post-traumatic stress symptoms and structure among orphan and vulnerable children and adolescents in Zambia

PubMed Central

Familiar, Itziar; Murray, Laura; Gross, Alden; Skavenski, Stephanie; Jere, Elizabeth; Bass, Judith

2014-01-01

Background Scant information exists on PTSD symptoms and structure in youth from developing countries. Methods We describe the symptom profile and exposure to trauma experiences among 343 orphan and vulnerable children and adolescents from Zambia. We distinguished profiles of post-traumatic stress symptoms using latent class analysis. Results Average number of trauma-related symptoms (21.6; range 0-38) was similar across sex and age. Latent class model suggested 3 classes varying by level of severity: low (31% of the sample), medium (45% of the sample), and high (24% of the sample) symptomatology. Conclusions Results suggest that PTSD is a continuously distributed latent trait. PMID:25382359

An Application of Latent Variable Structural Equation Modeling for Experimental Research in Educational Technology

ERIC Educational Resources Information Center

Lee, Hyeon Woo

2011-01-01

As the technology-enriched learning environments and theoretical constructs involved in instructional design become more sophisticated and complex, a need arises for equally sophisticated analytic methods to research these environments, theories, and models. Thus, this paper illustrates a comprehensive approach for analyzing data arising from…

Grounding Collaborative Learning in Semantics-Based Critiquing

ERIC Educational Resources Information Center

Cheung, William K.; Mørch, Anders I.; Wong, Kelvin C.; Lee, Cynthia; Liu, Jiming; Lam, Mason H.

2007-01-01

In this article we investigate the use of latent semantic analysis (LSA), critiquing systems, and knowledge building to support computer-based teaching of English composition. We have built and tested an English composition critiquing system that makes use of LSA to analyze student essays and compute feedback by comparing their essays with…

Dynamic Cognitive Tracing: Towards Unified Discovery of Student and Cognitive Models

ERIC Educational Resources Information Center

Gonzalez-Brenes, Jose P.; Mostow, Jack

2012-01-01

This work describes a unified approach to two problems previously addressed separately in Intelligent Tutoring Systems: (i) Cognitive Modeling, which factorizes problem solving steps into the latent set of skills required to perform them; and (ii) Student Modeling, which infers students' learning by observing student performance. The practical…

A Latent Class Analysis of Online Sexual Experiences and Offline Sexual Behaviors Among Female Adolescents.

PubMed

Maas, Megan K; Bray, Bethany C; Noll, Jennie G

2017-11-20

This study used latent class analysis to identify patterns (i.e., classes) across a broad range of online sexual experiences among female adolescents (n = 312) and to explore offline sexual behavior and substance use correlates of as well as maltreatment differences in class membership. The following four classes were identified: Online Abstinent, Online Inclusive, Attractors, and Seekers. Maltreated female adolescents were more likely to be members of the Online Inclusive class and less likely to be members of the Online Abstinent class than nonmaltreated female adolescents. Offline sexual behaviors and substance use differentially predicted class membership. These results suggest online sexual experiences vary greatly and should not be aggregated together as a global risk factor for all female adolescents. © 2017 Society for Research on Adolescence.

Vertical Profiles of Latent Heat Release over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2002

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.

2003-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs. S. America ) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model. Review of other latent heating algorithms will be discussed in the workshop.

Latent Herpes Simplex Virus Infection of Sensory Neurons Alters Neuronal Gene Expression

PubMed Central

Kramer, Martha F.; Cook, W. James; Roth, Frederick P.; Zhu, Jia; Holman, Holly; Knipe, David M.; Coen, Donald M.

2003-01-01

The persistence of herpes simplex virus (HSV) and the diseases that it causes in the human population can be attributed to the maintenance of a latent infection within neurons in sensory ganglia. Little is known about the effects of latent infection on the host neuron. We have addressed the question of whether latent HSV infection affects neuronal gene expression by using microarray transcript profiling of host gene expression in ganglia from latently infected versus mock-infected mouse trigeminal ganglia. 33P-labeled cDNA probes from pooled ganglia harvested at 30 days postinfection or post-mock infection were hybridized to nylon arrays printed with 2,556 mouse genes. Signal intensities were acquired by phosphorimager. Mean intensities (n = 4 replicates in each of three independent experiments) of signals from mock-infected versus latently infected ganglia were compared by using a variant of Student's t test. We identified significant changes in the expression of mouse neuronal genes, including several with roles in gene expression, such as the Clk2 gene, and neurotransmission, such as genes encoding potassium voltage-gated channels and a muscarinic acetylcholine receptor. We confirmed the neuronal localization of some of these transcripts by using in situ hybridization. To validate the microarray results, we performed real-time reverse transcriptase PCR analyses for a selection of the genes. These studies demonstrate that latent HSV infection can alter neuronal gene expression and might provide a new mechanism for how persistent viral infection can cause chronic disease. PMID:12915567

Sparsity-aware tight frame learning with adaptive subspace recognition for multiple fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yang, Boyuan

2017-09-01

It is a challenging problem to design excellent dictionaries to sparsely represent diverse fault information and simultaneously discriminate different fault sources. Therefore, this paper describes and analyzes a novel multiple feature recognition framework which incorporates the tight frame learning technique with an adaptive subspace recognition strategy. The proposed framework consists of four stages. Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition. Secondly, the noises are effectively eliminated through transform sparse coding techniques. Thirdly, the denoised signal is decoupled into discriminative feature subspaces by each tight frame filter. Finally, in guidance of elaborately designed fault related sensitive indexes, latent fault feature subspaces can be adaptively recognized and multiple faults are diagnosed simultaneously. Extensive numerical experiments are sequently implemented to investigate the sparsifying capability of the learned tight frame as well as its comprehensive denoising performance. Most importantly, the feasibility and superiority of the proposed framework is verified through performing multiple fault diagnosis of motor bearings. Compared with the state-of-the-art fault detection techniques, some important advantages have been observed: firstly, the proposed framework incorporates the physical prior with the data-driven strategy and naturally multiple fault feature with similar oscillation morphology can be adaptively decoupled. Secondly, the tight frame dictionary directly learned from the noisy observation can significantly promote the sparsity of fault features compared to analytical tight frames. Thirdly, a satisfactory complete signal space description property is guaranteed and thus weak feature leakage problem is avoided compared to typical learning methods.

Using Explanatory Item Response Models to Evaluate Complex Scientific Tasks Designed for the Next Generation Science Standards

NASA Astrophysics Data System (ADS)

Chiu, Tina

This dissertation includes three studies that analyze a new set of assessment tasks developed by the Learning Progressions in Middle School Science (LPS) Project. These assessment tasks were designed to measure science content knowledge on the structure of matter domain and scientific argumentation, while following the goals from the Next Generation Science Standards (NGSS). The three studies focus on the evidence available for the success of this design and its implementation, generally labelled as "validity" evidence. I use explanatory item response models (EIRMs) as the overarching framework to investigate these assessment tasks. These models can be useful when gathering validity evidence for assessments as they can help explain student learning and group differences. In the first study, I explore the dimensionality of the LPS assessment by comparing the fit of unidimensional, between-item multidimensional, and Rasch testlet models to see which is most appropriate for this data. By applying multidimensional item response models, multiple relationships can be investigated, and in turn, allow for a more substantive look into the assessment tasks. The second study focuses on person predictors through latent regression and differential item functioning (DIF) models. Latent regression models show the influence of certain person characteristics on item responses, while DIF models test whether one group is differentially affected by specific assessment items, after conditioning on latent ability. Finally, the last study applies the linear logistic test model (LLTM) to investigate whether item features can help explain differences in item difficulties.

Variational learning and bits-back coding: an information-theoretic view to Bayesian learning.

PubMed

Honkela, Antti; Valpola, Harri

2004-07-01

The bits-back coding first introduced by Wallace in 1990 and later by Hinton and van Camp in 1993 provides an interesting link between Bayesian learning and information-theoretic minimum-description-length (MDL) learning approaches. The bits-back coding allows interpreting the cost function used in the variational Bayesian method called ensemble learning as a code length in addition to the Bayesian view of misfit of the posterior approximation and a lower bound of model evidence. Combining these two viewpoints provides interesting insights to the learning process and the functions of different parts of the model. In this paper, the problem of variational Bayesian learning of hierarchical latent variable models is used to demonstrate the benefits of the two views. The code-length interpretation provides new views to many parts of the problem such as model comparison and pruning and helps explain many phenomena occurring in learning.

Behavioral techniques for attenuating the expression of fear associations in an animal model of anxiety.

PubMed

Laborda, Mario A; Polack, Cody W; Miguez, Gonzalo; Miller, Ralph R

2014-09-01

Recent data indicate that extinguished fear often returns when the testing conditions differ from those of treatment. Several manipulations including extensive extinction training, extinction in multiple contexts, and spacing the extinction trials and sessions reduce the return of fear. Moreover, extensive extinction and extinction in multiple contexts summate in reducing return of fear, and the spacing of the extinction trials and the spacing of extinction sessions summate in reducing return of fear. Here we evaluated whether these techniques also attenuate the context specificity of latent inhibition, and whether they summate to further decrease fear responding at test. In two experiments, with rats as subjects in a lick suppression preparation, we assessed the effects of massive CS preexposure, CS preexposure in multiple contexts, and of spacing the CS-preexposure trials and sessions, in reducing the context specificity of latent inhibition. Fear responding was attenuated by all four manipulations. Moreover, extensive CS preexposure in multiple contexts, and conjoint spacing of the CS-preexposure trials and sessions, were more effective in reducing the context specificity of latent inhibition than each manipulation alone. Our experimental designs evaluated degrees of context specificity of latent inhibition but omitted groups in which latent inhibition was assessed without a context shift away from the context of latent inhibition treatment. This precluded us from drawing conclusions concerning absolute (as opposed to relative) levels of recovery from latent inhibition. Techniques effective in decreasing the return of conditioned fear following extinction are also effective in decreasing the context specificity of latent inhibition in an animal model of anxiety. Fear and anxiety disorders might be prevented in anxious human participants with the same techniques used here, but that is still an empirical question. Copyright © 2014 Elsevier Ltd. All rights reserved.

Latent Heating Retrievals Using the TRMM Precipitation Radar: A Multi-Seasonal Study

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Lang, S.; Meneghini, R.; Halverson, J.; Johnson, R.; Simpson, J.; Einaudi, Franco (Technical Monitor)

2001-01-01

Rainfall is a key link in the hydrologic cycle and is a primary heat source for the atmosphere. The vertical distribution of latent heat release, which is accompanied by rainfall, modulates the large-scale circulations of the tropics and in turn can impact midlatitude weather. This latent heat release is a consequence of phase changes between vapor, liquid, and solid water. Present largescale weather and climate models can simulate latent heat release only crudely, thus reducing their confidence in predictions on both global and regional scales. This paper represents the first attempt to use NASA Tropical Rainfall Measuring Mission (TRMM) rainfall information to estimate the four-dimensional structure of global monthly latent heating profiles over the global tropics from December 1997 to October 2000. The Goddard Convective-Stratiform. Heating (CSH) algorithm and TRMM precipitation radar data are used for this study. We will examine and compare the latent heating structures between 1997-1998 (winter) ENSO and 1998-2000 (non-ENSO). We will also examine over the tropics. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental; Indian oceans vs west Pacific; Africa vs S. America) will be also examined and compared. In addition, we will examine the relationship between latent heating (max heating level) and SST. The period of interest also coincides with several TRMM field campaigns that recently occurred over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and in the central Pacific in 1999 (KWAJEX). Sounding diagnosed Q1 budgets from these experiments could provide a means of validating the retrieved profiles of latent heating from the CSH algorithm.

Reward devaluation disrupts latent inhibition in fear conditioning.

PubMed

De la Casa, Luís Gonzalo; Mena, Auxiliadora; Ruiz-Salas, Juán Carlos; Quintero, Esperanza; Papini, Mauricio R

2018-03-01

Three experiments explored the link between reward shifts and latent inhibition (LI). Using consummatory procedures, rewards were either downshifted from 32% to 4% sucrose (Experiments 1-2), or upshifted from 4% to 32% sucrose (Experiment 3). In both cases, appropriate unshifted controls were also included. LI was implemented in terms of fear conditioning involving a single tone-shock pairing after extensive tone-only preexposure. Nonpreexposed controls were also included. Experiment 1 demonstrated a typical LI effect (i.e., disruption of fear conditioning after preexposure to the tone) in animals previously exposed only to 4% sucrose. However, the LI effect was eliminated by preexposure to a 32%-to-4% sucrose devaluation. Experiment 2 replicated this effect when the LI protocol was administered immediately after the reward devaluation event. However, LI was restored when preexposure was administered after a 60-min retention interval. Finally, Experiment 3 showed that a reward upshift did not affect LI. These results point to a significant role of negative emotion related to reward devaluation in the enhancement of stimulus processing despite extensive nonreinforced preexposure experience.

A Robust Adaptive Autonomous Approach to Optimal Experimental Design

NASA Astrophysics Data System (ADS)

Gu, Hairong

Experimentation is the fundamental tool of scientific inquiries to understand the laws governing the nature and human behaviors. Many complex real-world experimental scenarios, particularly in quest of prediction accuracy, often encounter difficulties to conduct experiments using an existing experimental procedure for the following two reasons. First, the existing experimental procedures require a parametric model to serve as the proxy of the latent data structure or data-generating mechanism at the beginning of an experiment. However, for those experimental scenarios of concern, a sound model is often unavailable before an experiment. Second, those experimental scenarios usually contain a large number of design variables, which potentially leads to a lengthy and costly data collection cycle. Incompetently, the existing experimental procedures are unable to optimize large-scale experiments so as to minimize the experimental length and cost. Facing the two challenges in those experimental scenarios, the aim of the present study is to develop a new experimental procedure that allows an experiment to be conducted without the assumption of a parametric model while still achieving satisfactory prediction, and performs optimization of experimental designs to improve the efficiency of an experiment. The new experimental procedure developed in the present study is named robust adaptive autonomous system (RAAS). RAAS is a procedure for sequential experiments composed of multiple experimental trials, which performs function estimation, variable selection, reverse prediction and design optimization on each trial. Directly addressing the challenges in those experimental scenarios of concern, function estimation and variable selection are performed by data-driven modeling methods to generate a predictive model from data collected during the course of an experiment, thus exempting the requirement of a parametric model at the beginning of an experiment; design optimization is performed to select experimental designs on the fly of an experiment based on their usefulness so that fewest designs are needed to reach useful inferential conclusions. Technically, function estimation is realized by Bayesian P-splines, variable selection is realized by Bayesian spike-and-slab prior, reverse prediction is realized by grid-search and design optimization is realized by the concepts of active learning. The present study demonstrated that RAAS achieves statistical robustness by making accurate predictions without the assumption of a parametric model serving as the proxy of latent data structure while the existing procedures can draw poor statistical inferences if a misspecified model is assumed; RAAS also achieves inferential efficiency by taking fewer designs to acquire useful statistical inferences than non-optimal procedures. Thus, RAAS is expected to be a principled solution to real-world experimental scenarios pursuing robust prediction and efficient experimentation.

Learning Multisensory Integration and Coordinate Transformation via Density Estimation

PubMed Central

Sabes, Philip N.

2013-01-01

Sensory processing in the brain includes three key operations: multisensory integration—the task of combining cues into a single estimate of a common underlying stimulus; coordinate transformations—the change of reference frame for a stimulus (e.g., retinotopic to body-centered) effected through knowledge about an intervening variable (e.g., gaze position); and the incorporation of prior information. Statistically optimal sensory processing requires that each of these operations maintains the correct posterior distribution over the stimulus. Elements of this optimality have been demonstrated in many behavioral contexts in humans and other animals, suggesting that the neural computations are indeed optimal. That the relationships between sensory modalities are complex and plastic further suggests that these computations are learned—but how? We provide a principled answer, by treating the acquisition of these mappings as a case of density estimation, a well-studied problem in machine learning and statistics, in which the distribution of observed data is modeled in terms of a set of fixed parameters and a set of latent variables. In our case, the observed data are unisensory-population activities, the fixed parameters are synaptic connections, and the latent variables are multisensory-population activities. In particular, we train a restricted Boltzmann machine with the biologically plausible contrastive-divergence rule to learn a range of neural computations not previously demonstrated under a single approach: optimal integration; encoding of priors; hierarchical integration of cues; learning when not to integrate; and coordinate transformation. The model makes testable predictions about the nature of multisensory representations. PMID:23637588

Isolation of virus from brain after immunosuppression of mice with latent herpes simplex

NASA Astrophysics Data System (ADS)

Kastrukoff, Lorne; Long, Carol; Doherty, Peter C.; Wroblewska, Zofia; Koprowski, Hilary

1981-06-01

Herpes simplex virus (HSV) is usually present in a latent form in the trigeminal ganglion of man1-3. Various stress factors may induce virus reactivation, which is manifest by a lip lesion (innervated from the trigeminal ganglion) and the production of infectious virus. The considerable experimental efforts to define the conditions that lead to the reactivation of latent HSV have concentrated on isolating virus either from the original extraneural site of virus inoculation, or from cell-free homogenates of sensory ganglia from latently infected animals4-15. Recent DNA hybridization experiments resulted in the demonstration of the presence of HSV genomes in the brain tissue of both latently infected mice, and of humans who showed no clinical symptoms of HSV (ref. 16 and N. Fraser, personal communication). This led us to consider the possibility that HSV may be present in brain tissue as the result of either reactivation of the virus in brain cells or the passage of reactivated virus from trigeminal ganglia through the brain stem to the brain. The presence of infectious HSV in brain tissue has not previously been demonstrated; yet this could be a factor in chronic, relapsing neurological diseases such as multiple sclerosis. We have now shown experimentally that mice carrying latent HSV in their trigeminal ganglia may, following massive immunosuppression, express infectious virus in the central nervous system (CNS).

Analyzing Hierarchical Relationships Among Modes of Cognitive Reasoning and Integrated Science Process Skills.

ERIC Educational Resources Information Center

Yeany, Russell H.; And Others

1986-01-01

Searched for a learning hierarchy among skills comprising formal operations and integrated science processes. Ordering, theoretic, and probabilistic latent structure methods were used to analyze data collected from 700 science students. Both linear and branching relationships were identified within and across the two sets of skills. (Author/JN)

From Comprehensive to Singular: A Latent Class Analysis of College Teaching Practices

ERIC Educational Resources Information Center

Campbell, Corbin M.; Cabrera, Alberto F.; Ostrow Michel, Jessica; Patel, Shikha

2017-01-01

While decades of research on college teaching has investigated several forms of classroom practices, much of this research approaches teaching as falling into mutually exclusive paradigms (e.g., active learning vs. lecturing). This paper enters inside the college classroom using external raters to understand patterns of pedagogical practices…

Multivariate Latent Change Modeling of Developmental Decline in Academic Intrinsic Math Motivation and Achievement: Childhood through Adolescence

ERIC Educational Resources Information Center

Gottfried, Adele Eskeles; Marcoulides, George A.; Gottfried, Allen W.; Oliver, Pamella H.; Guerin, Diana Wright

2007-01-01

Research has established that academic intrinsic motivation, enjoyment of school learning without receipt of external rewards, significantly declines across childhood through adolescence. Math intrinsic motivation evidences the most severe decline compared with other subject areas. This study addresses this developmental decline in math intrinsic…

A Multilevel Latent Growth Curve Approach to Predicting Student Proficiency

ERIC Educational Resources Information Center

Choi, Kilchan; Goldschmidt, Pete

2012-01-01

Value-added models and growth-based accountability aim to evaluate school's performance based on student growth in learning. The current focus is on linking the results from value-added models to the ones from growth-based accountability systems including Adequate Yearly Progress decisions mandated by No Child Left Behind. We present a new…

An Empirical Typology of the Latent Programmatic Structure of Community College Student Success Programs

ERIC Educational Resources Information Center

Hatch, Deryl K.; Bohlig, E. Michael

2016-01-01

The definition and description of student success programs in the literature (e.g., orientation, first-year seminars, learning communities, etc.) suggest underlying programmatic similarities. Yet researchers to date typically depend on ambiguous labels to delimit studies, resulting in loosely related but separate research lines and few…

Mind Wandering and Online Learning: A Latent Variable Analysis

ERIC Educational Resources Information Center

Hollis, R. Benjamin

2013-01-01

Thoughts drift in everyday life and in the classroom. The goal of this study was to investigate how often students reported off-task thinking while watching online lectures. These findings were related to working memory capacity, topic interest, and achievement goal orientations. Structural equation modeling was used to evaluate how all of these…

Measuring and Advancing Experimental Design Ability in an Introductory Course without Altering Existing Lab Curriculum.

PubMed

Shanks, Ryan A; Robertson, Chuck L; Haygood, Christian S; Herdliksa, Anna M; Herdliska, Heather R; Lloyd, Steven A

2017-01-01

Introductory biology courses provide an important opportunity to prepare students for future courses, yet existing cookbook labs, although important in their own way, fail to provide many of the advantages of semester-long research experiences. Engaging, authentic research experiences aid biology students in meeting many learning goals. Therefore, overlaying a research experience onto the existing lab structure allows faculty to overcome barriers involving curricular change. Here we propose a working model for this overlay design in an introductory biology course and detail a means to conduct this lab with minimal increases in student and faculty workloads. Furthermore, we conducted exploratory factor analysis of the Experimental Design Ability Test (EDAT) and uncovered two latent factors which provide valid means to assess this overlay model's ability to increase advanced experimental design abilities. In a pre-test/post-test design, we demonstrate significant increases in both basic and advanced experimental design abilities in an experimental and comparison group. We measured significantly higher gains in advanced experimental design understanding in students in the experimental group. We believe this overlay model and EDAT factor analysis contribute a novel means to conduct and assess the effectiveness of authentic research experiences in an introductory course without major changes to the course curriculum and with minimal increases in faculty and student workloads.

Differential implication of dorsolateral and dorsomedial srtiatum in encoding and recovery processes of latent inhibition.

PubMed

Díaz, Estrella; Vargas, Juan Pedro; Quintero, Esperanza; Gonzalo de la Casa, Luis; O'Donnell, Patricio; Lopez, Juan Carlos

2014-05-01

The dorsal striatum has been ascribed to different behavioral roles. While the lateral area (dls) is implicated in habitual actions, its medial part (dms) is linked to goal expectancy. According to this model, dls function includes representation of stimulus-response associations, but not of goals. Dls function has been typically analyzed with regard to movement, and there is no data indicating whether this region could processes specific stimulus-outcome associations. To test this possibility, we analyzed the effects of dls and dms inactivation on the retrieval phase, and dms lesion on the acquisition phase of a latent inhibition procedure using two conditions, long and short presentations of the future conditioned stimulus. Contrary to current theories of basal ganglia function, we report evidence in favor of the dls involvement in cognitive processes of learning and retrieval. Moreover, we provide data about the sequential relationship between dms and dls, in which the dms could be involved, but it would not be critical, in new learning and the dls could be subsequently involved in consolidating cognitive routines. Copyright © 2014 Elsevier Inc. All rights reserved.

Computational neuroscience across the lifespan: Promises and pitfalls.

PubMed

van den Bos, Wouter; Bruckner, Rasmus; Nassar, Matthew R; Mata, Rui; Eppinger, Ben

2017-10-13

In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan development. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A LATENT PERIOD IN THE ACTION OF COPPER ON RESPIRATION

PubMed Central

Cook, S. F.

1926-01-01

1. When copper chloride is allowed to act on Aspergillus niger there is at first a period during which there is no change in the rate of the production of carbon dioxide, following which the rate of respiration falls. The interval of no change is called the latent period. 2. When the copper is removed from the external solution before the end of the latent period this interval is prolonged. The rate of respiration then falls to a new level below the normal level. 3. Experiments on Nitella and on Valonia indicate that the copper penetrates the cell almost immediately. 4. The length of the latent period varies inversely as a constant power of the concentration of the copper. 5. These results are explained by assuming that the copper is made active in the respiration system by means of a reversible reaction. By using appropriate velocity constants the experimental curves can be duplicated by calculated curves. PMID:19872281

Deep learning for neuroimaging: a validation study.

PubMed

Plis, Sergey M; Hjelm, Devon R; Salakhutdinov, Ruslan; Allen, Elena A; Bockholt, Henry J; Long, Jeffrey D; Johnson, Hans J; Paulsen, Jane S; Turner, Jessica A; Calhoun, Vince D

2014-01-01

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.

What Can We Learn from Hugoniot Temperature as a Function of Shock Velocity?

NASA Astrophysics Data System (ADS)

LI, M.; Jeanloz, R.

2015-12-01

Shock-wave experiments traditionally rely on impact techniques, whereby measured shock velocity (US) can be related to material velocity (up), determined from the impact velocity (= 2up for a symmetric impact), and resulting in the empirically observed linear US-up equation of state: US = c0 + s up. Modern experiments relying on laser-driven compression have the advantage of reaching higher pressures than laboratory impact experiments, but up is typically not determined; instead, Hugoniot temperature (TH) and shock velocity are more readily measured. Assuming a linear US-up equation of state and that the Grüneisen parameter has the volume dependence g(V) = g0 (V/V0), measurements of the Hugoniot temperature as a function of shock velocity provide constraints on the specific heat along the Hugoniot CVH(US) = V0 f(US)[c0 g0 TH - s US dTH/dUS]-1 where the Walsh-Christian (1955) function f(US) = - (US - c0)2 US/(V0 s c0) = TH dSH/dVH gives the entropy change along the Hugoniot (subscripts 0 and H indicate zero-pressure and Hugoniot states, respectively). In this sense, TH(US) measurements are similar to calorimetry experiments. If specific heat and Grüneisen parameter are determined independently (e.g., from wave-velocity measurements and experiments on porous samples), the TH(US) analog to the linear US-up equation of state is TH(US) = {T0 exp(g0 /s) - ò[V0 c0 f(x)/(s x CV)] exp[c0 g0 /(s x)] dx} exp[- c0 g0 /(s US)] where the integration is from x = c0 to x = US. In addition, experiments can be considered with: 1) different initial volume, as in a porous sample; 2) different initial internal energy, as in a sample heated at constant volume; and 3) different initial volume and internal energy, as in a sample initially heated at ambient pressure. From these four initial states, we get four different Hugoniot curves, and can also consider the effect of phase transition latent heat. Temperature as a function of shock velocity may thus be benefit the analysis of melting and other phase transitions with small volume change and finite latent heat.

Efficient and robust model-to-image alignment using 3D scale-invariant features.

PubMed

Toews, Matthew; Wells, William M

2013-04-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

PubMed Central

Toews, Matthew; Wells, William M.

2013-01-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. PMID:23265799

Nursing students' experiences of being video-recorded during examination in a fictive emergency care situation.

PubMed

Nyström, Anita; Pålsson, Ylva; Hofsten, Anna; Häggström, Elisabeth

2014-10-01

Promoting undergraduate nursing students' learning in simulated care can be achieved through dynamic scenario-based training sessions that are documented using simple video equipment. One valuable aspect of this kind of training is the subsequent reflective dialogue that takes place between the teacher and the students during the examination. The aim of the present paper is to describe bachelor nursing students' experiences of being video-recorded during an examination with a simulated patient in emergency care. The study was descriptive in design and used a qualitative approach with written answers to open-ended questions; 44 undergraduate nursing students participated. A latent content analysis resulted in three themes: (i) visualization might cause nervousness at first; (ii) visualization promotes dialogue and acknowledgement; and (iii) visualization promotes increased self-knowledge and professional growth. The conclusion is that video-recording is a good way for undergraduate nursing students to develop skills in emergency care situations and to understand their own actions; it might also help them increase their self-knowledge. © 2013 Wiley Publishing Asia Pty Ltd.

Young-age gender differences in mathematics mediated by independent control or uncontrollability.

PubMed

Zirk-Sadowski, Jan; Lamptey, Charlotte; Devine, Amy; Haggard, Mark; Szűcs, Dénes

2014-05-01

We studied whether the origins of math anxiety can be related to a biologically supported framework of stress induction: (un)controllability perception, here indicated by self-reported independent efforts in mathematics. Math anxiety was tested in 182 children (8- to 11-year-olds). Latent factor modeling was used to test hypotheses on plausible causal processes and mediations within competing models in quasi-experimental contrasts. Uncontrollability perception in mathematics, or (in)dependence of efforts, best fit the data as an antecedent of math anxiety. In addition, the relationship of math anxiety with gender was fully mediated by adaptive perception of control (i.e. controllability). That is, young boys differ from girls in terms of their experience of control in mathematics learning. These differences influence math anxiety. Our findings are consistent with recent suggestions in clinical literature according to which uncontrollability makes women more susceptible to fear and anxiety disorders. © 2014 John Wiley & Sons Ltd.

Latent topic discovery of clinical concepts from hospital discharge summaries of a heterogeneous patient cohort.

PubMed

Lehman, Li-Wei; Long, William; Saeed, Mohammed; Mark, Roger

2014-01-01

Patients in critical care often exhibit complex disease patterns. A fundamental challenge in clinical research is to identify clinical features that may be characteristic of adverse patient outcomes. In this work, we propose a data-driven approach for phenotype discovery of patients in critical care. We used Hierarchical Dirichlet Process (HDP) as a non-parametric topic modeling technique to automatically discover the latent "topic" structure of diseases, symptoms, and findings documented in hospital discharge summaries. We show that the latent topic structure can be used to reveal phenotypic patterns of diseases and symptoms shared across subgroups of a patient cohort, and may contain prognostic value in stratifying patients' post hospital discharge mortality risks. Using discharge summaries of a large patient cohort from the MIMIC II database, we evaluate the clinical utility of the discovered topic structure in identifying patients who are at high risk of mortality within one year post hospital discharge. We demonstrate that the learned topic structure has statistically significant associations with mortality post hospital discharge, and may provide valuable insights in defining new feature sets for predicting patient outcomes.

Protein subcellular location pattern classification in cellular images using latent discriminative models.

PubMed

Li, Jieyue; Xiong, Liang; Schneider, Jeff; Murphy, Robert F

2012-06-15

Knowledge of the subcellular location of a protein is crucial for understanding its functions. The subcellular pattern of a protein is typically represented as the set of cellular components in which it is located, and an important task is to determine this set from microscope images. In this article, we address this classification problem using confocal immunofluorescence images from the Human Protein Atlas (HPA) project. The HPA contains images of cells stained for many proteins; each is also stained for three reference components, but there are many other components that are invisible. Given one such cell, the task is to classify the pattern type of the stained protein. We first randomly select local image regions within the cells, and then extract various carefully designed features from these regions. This region-based approach enables us to explicitly study the relationship between proteins and different cell components, as well as the interactions between these components. To achieve these two goals, we propose two discriminative models that extend logistic regression with structured latent variables. The first model allows the same protein pattern class to be expressed differently according to the underlying components in different regions. The second model further captures the spatial dependencies between the components within the same cell so that we can better infer these components. To learn these models, we propose a fast approximate algorithm for inference, and then use gradient-based methods to maximize the data likelihood. In the experiments, we show that the proposed models help improve the classification accuracies on synthetic data and real cellular images. The best overall accuracy we report in this article for classifying 942 proteins into 13 classes of patterns is about 84.6%, which to our knowledge is the best so far. In addition, the dependencies learned are consistent with prior knowledge of cell organization. http://murphylab.web.cmu.edu/software/.

Identifying Learning Patterns of Children at Risk for Specific Reading Disability

PubMed Central

Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E.; Grigorenko, Elena L.

2016-01-01

Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural Chitonga-speaking Zambia. A multi-group Latent Growth Curve Model (LGCM) was implemented to study interindividual differences in intraindividual change across trials. Results showed that the +SRD group recalled fewer words correctly in the first trial, learned at a slower rate during the subsequent trials, and demonstrated a more linear learning pattern compared to the SRD group. This study illustrates the promise of LGCM applied to multi-trial learning tasks, by isolating three components of the learning process (initial recall, rate of learning, and functional pattern of learning). Implications of this microdevelopmental approach to SRD research in low-to-middle income countries are discussed. PMID:26037654

Identifying learning patterns of children at risk for Specific Reading Disability.

PubMed

Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E; Grigorenko, Elena L

2016-05-01

Differences in learning patterns of vocabulary acquisition in children at risk (+SRD) and not at risk (-SRD) for Specific Reading Disability (SRD) were examined using a microdevelopmental paradigm applied to the multi-trial Foreign Language Learning Task (FLLT; Baddeley et al., 1995). The FLLT was administered to 905 children from rural Chitonga-speaking Zambia. A multi-group Latent Growth Curve Model (LGCM) was implemented to study interindividual differences in intraindividual change across trials. Results showed that the +SRD group recalled fewer words correctly in the first trial, learned at a slower rate during the subsequent trials, and demonstrated a more linear learning pattern compared to the -SRD group. This study illustrates the promise of LGCM applied to multi-trial learning tasks, by isolating three components of the learning process (initial recall, rate of learning, and functional pattern of learning). Implications of this microdevelopmental approach to SRD research in low-to-middle income countries are discussed. © 2015 John Wiley & Sons Ltd.

Discriminative latent models for recognizing contextual group activities.

PubMed

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N; Mori, Greg

2012-08-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.

Discriminative Latent Models for Recognizing Contextual Group Activities

PubMed Central

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N.; Mori, Greg

2012-01-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities. PMID:22144516

Cross-domain latent space projection for person re-identification

NASA Astrophysics Data System (ADS)

Pu, Nan; Wu, Song; Qian, Li; Xiao, Guoqiang

2018-04-01

In this paper, we research the problem of person re-identification and propose a cross-domain latent space projection (CDLSP) method to address the problems of the absence or insufficient labeled data in the target domain. Under the assumption that the visual features in the source domain and target domain share the similar geometric structure, we transform the visual features from source domain and target domain to a common latent space by optimizing the object function defined in the manifold alignment method. Moreover, the proposed object function takes into account the specific knowledge in the re-id with the aim to improve the performance of re-id under complex situations. Extensive experiments conducted on four benchmark datasets show the proposed CDLSP outperforms or is competitive with stateof- the-art methods for person re-identification.

Variational Bayesian Learning for Wavelet Independent Component Analysis

NASA Astrophysics Data System (ADS)

Roussos, E.; Roberts, S.; Daubechies, I.

2005-11-01

In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or "sources" via a generally unknown mapping. For the noisy overcomplete case, where we have more sources than observations, the problem becomes extremely ill-posed. Solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian methodology, allowing us to incorporate "soft" constraints in a natural manner. The work described in this paper is mainly driven by problems in functional magnetic resonance imaging of the brain, for the neuro-scientific goal of extracting relevant "maps" from the data. This can be stated as a `blind' source separation problem. Recent experiments in the field of neuroscience show that these maps are sparse, in some appropriate sense. The separation problem can be solved by independent component analysis (ICA), viewed as a technique for seeking sparse components, assuming appropriate distributions for the sources. We derive a hybrid wavelet-ICA model, transforming the signals into a domain where the modeling assumption of sparsity of the coefficients with respect to a dictionary is natural. We follow a graphical modeling formalism, viewing ICA as a probabilistic generative model. We use hierarchical source and mixing models and apply Bayesian inference to the problem. This allows us to perform model selection in order to infer the complexity of the representation, as well as automatic denoising. Since exact inference and learning in such a model is intractable, we follow a variational Bayesian mean-field approach in the conjugate-exponential family of distributions, for efficient unsupervised learning in multi-dimensional settings. The performance of the proposed algorithm is demonstrated on some representative experiments.

A Computational Framework for Understanding Decision Making through Integration of Basic Learning Rules

PubMed Central

Bazhenov, Maxim; Huerta, Ramon; Smith, Brian H.

2013-01-01

Nonassociative and associative learning rules simultaneously modify neural circuits. However, it remains unclear how these forms of plasticity interact to produce conditioned responses. Here we integrate nonassociative and associative conditioning within a uniform model of olfactory learning in the honeybee. Honeybees show a fairly abrupt increase in response after a number of conditioning trials. The occurrence of this abrupt change takes many more trials after exposure to nonassociative trials than just using associative conditioning. We found that the interaction of unsupervised and supervised learning rules is critical for explaining latent inhibition phenomenon. Associative conditioning combined with the mutual inhibition between the output neurons produces an abrupt increase in performance despite smooth changes of the synaptic weights. The results show that an integrated set of learning rules implemented using fan-out connectivities together with neural inhibition can explain the broad range of experimental data on learning behaviors. PMID:23536082

Filtering Essays by Means of a Software Tool: Identifying Poor Essays

ERIC Educational Resources Information Center

Seifried, Eva; Lenhard, Wolfgang; Spinath, Birgit

2017-01-01

Writing essays and receiving feedback can be useful for fostering students' learning and motivation. When faced with large class sizes, it is desirable to identify students who might particularly benefit from feedback. In this article, we tested the potential of Latent Semantic Analysis (LSA) for identifying poor essays. A total of 14 teaching…

Statistical Test for Latent Growth Nonlinearity with Three Time Points. Research Brief 8

ERIC Educational Resources Information Center

Nese, Joseph F. T.

2013-01-01

Curriculum-based measurement (CBM) is a system of assessment used to screen for students at risk for poor learning. CBM benchmark screening assessments are typically administered to all students in the fall, winter, and spring, and these data are frequently used by researchers to model and perhaps explain within-year growth. Modeling growth with…

Efficient Inference for Trees and Alignments: Modeling Monolingual and Bilingual Syntax with Hard and Soft Constraints and Latent Variables

ERIC Educational Resources Information Center

Smith, David Arthur

2010-01-01

Much recent work in natural language processing treats linguistic analysis as an inference problem over graphs. This development opens up useful connections between machine learning, graph theory, and linguistics. The first part of this dissertation formulates syntactic dependency parsing as a dynamic Markov random field with the novel…

The Influence of Maternal Employment on Children's Learning Growth and the Role of Parental Involvement

ERIC Educational Resources Information Center

Youn, M. J.; Leon, J.; Lee, K. J.

2012-01-01

Using data from the Early Childhood Longitudinal Study, this study employed a latent growth curve model to examine how parental involvement explains the association between maternal employment status and children's math and reading achievement growth from kindergarten through the third grade. To address this issue, three types of parental…

Viewing How STEM Project-Based Learning Influences Students' Science Achievement through the Implementation Lens: A Latent Growth Modeling

ERIC Educational Resources Information Center

Erdogan, Niyazi; Navruz, Bilgin; Younes, Rayya; Capraro, Robert M.

2016-01-01

Recent studies on professional development programs indicate these programs, when sustained, have a positive impact on student achievement; however, many of these studies have failed to use longitudinal data. The purpose of this study is to understand how one particular instructional practice (STEM PBL) used consistently influences student…

Literacy Profiles of At-Risk Young Adults Enrolled in Career and Technical Education

ERIC Educational Resources Information Center

Mellard, Daryl F.; Woods, Kari L.; Lee, Jae Hoon

2016-01-01

A latent profile analysis of 323 economically and academically at-risk adolescent and young adult learners yielded two classes: an average literacy class (92%) and a low literacy class (8%). The class profiles significantly differed in their word reading and math skills, and in their processing speeds and self-reported learning disabilities. The…

Developing General Literacy Ability and Intercultural Sensitivity through English Literacy Instruction: Using Global Literature for Korean EFL Learners

ERIC Educational Resources Information Center

Bae, Jiyoung

2012-01-01

This study explored L2 literacy ability and intercultural sensitivity of Korean late elementary to early middle school students learning English as a foreign language. This study investigated the latent variable structure of L2 literacy abilities, including fluency, vocabulary, reading comprehension, and writing abilities, and intercultural…

Moderation of Cognitive-Achievement Relations for Children with Specific Learning Disabilities: A Multi-Group Latent Variable Analysis Using CHC Theory

ERIC Educational Resources Information Center

Niileksela, Christopher R.

2012-01-01

Recent advances in the understanding of the relations between cognitive abilities and academic skills have helped shape a better understanding of which cognitive processes may underlie different types of SLD (Flanagan, Fiorello, & Ortiz, 2010). Similarities and differences in cognitive-achievement relations for children with and without SLDs…

Automated LSA Assessment of Summaries in Distance Education: Some Variables to Be Considered

ERIC Educational Resources Information Center

Jorge-Botana, Guillermo; Luzón, José M.; Gómez-Veiga, Isabel; Martín-Cordero, Jesús I.

2015-01-01

A latent semantic analysis-based automated summary assessment is described; this automated system is applied to a real learning from text task in a Distance Education context. We comment on the use of automated content, plagiarism, text coherence measures, and word weights average and their impact on predicting human judges summary scoring. A…

A Latent Curve Model of Parental Motivational Practices and Developmental Decline in Math and Science Academic Intrinsic Motivation

ERIC Educational Resources Information Center

Gottfried, Adele Eskeles; Marcoulides, George A.; Gottfried, Allen W.; Oliver, Pamella H.

2009-01-01

A longitudinal approach was used to examine the effects of parental task-intrinsic and task-extrinsic motivational practices on academic intrinsic motivation in the subject areas of math and science. Parental task-intrinsic practices comprise encouragement of children's pleasure and engagement in the learning process, whereas task-extrinsic…

Changes to the Student Loan Experience: Psychological Predictors and Outcomes

ERIC Educational Resources Information Center

Mueller, Thomas

2014-01-01

This study builds on the work of scholars who have explored psychological perceptions of the student loan experience. Survey analysis ("N" = 175) revealed a multidimensional model was developed through factor analysis and testing, which revealed four latent variables: "Duress," "Mandatory," "Financial," and…

How does consumer knowledge affect environmentally sustainable choices? Evidence from a cross-country latent class analysis of food labels.

PubMed

Peschel, Anne O; Grebitus, Carola; Steiner, Bodo; Veeman, Michele

2016-11-01

This paper examines consumers' knowledge and lifestyle profiles and preferences regarding two environmentally labeled food staples, potatoes and ground beef. Data from online choice experiments conducted in Canada and Germany are analyzed through latent class choice modeling to identify the influence of consumer knowledge (subjective and objective knowledge as well as usage experience) on environmentally sustainable choices. We find that irrespective of product or country under investigation, high subjective and objective knowledge levels drive environmentally sustainable food choices. Subjective knowledge was found to be more important in this context. Usage experience had relatively little impact on environmentally sustainable choices. Our results suggest that about 20% of consumers in both countries are ready to adopt footprint labels in their food choices. Another 10-20% could be targeted by enhancing subjective knowledge, for example through targeted marketing campaigns. Copyright © 2016 Elsevier Ltd. All rights reserved.

Vertical Profiles of Latent Heat Release over the Global Tropics using TRMM rainfall products from December 1997 to November 2001

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.

2002-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2001. Rainfall, latent heating and radar reflectivity structures between El Nino (DE 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs. west Pacific, Africa vs. S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in strtaiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

Vertical Profiles of Latent Heat Release Over the Global Tropics using TRMM Rainfall Products from December 1997 to November 2001

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Lang, S.; Simpson, J.; Meneghini, R.; Halverson, J.; Johnson, R.; Adler, R.; Starr, David (Technical Monitor)

2002-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in stratiform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMEX), Brazil in 1999 (TRMM-LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

Vertical Profiles of Latent Heat Release over the Global Tropics Using TRMM Rainfall Products from December 1997 to November 2002

NASA Technical Reports Server (NTRS)

Tao, W.-K.

2003-01-01

NASA Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) derived rainfall information will be used to estimate the four-dimensional structure of global monthly latent heating and rainfall profiles over the global tropics from December 1997 to November 2000. Rainfall, latent heating and radar reflectivity structures between El Nino (DJF 1997-98) and La Nina (DJF 1998-99) will be examined and compared. The seasonal variation of heating over various geographic locations (i.e., oceanic vs continental, Indian ocean vs west Pacific, Africa vs S. America) will also be analyzed. In addition, the relationship between rainfall, latent heating (maximum heating level), radar reflectivity and SST is examined and will be presented in the meeting. The impact of random error and bias in straitform percentage estimates from PR on latent heating profiles is studied and will also be presented in the meeting. The Goddard Cumulus Ensemble Model is being used to simulate various mesoscale convective systems that developed in different geographic locations. Specifically, the model estimated rainfall, radar reflectivity and latent heating profiles will be compared to observational data collected from TRMM field campaigns over the South China Sea in 1998 (SCSMXX), Brazil in 1999 (TRMM- LBA), and the central Pacific in 1999 (KWAJEX). Sounding diagnosed heating budgets and radar reflectivity from these experiments can provide the means to validate (heating product) as well as improve the GCE model.

A latent transition model of the effects of a teen dating violence prevention initiative.

PubMed

Williams, Jason; Miller, Shari; Cutbush, Stacey; Gibbs, Deborah; Clinton-Sherrod, Monique; Jones, Sarah

2015-02-01

Patterns of physical and psychological teen dating violence (TDV) perpetration, victimization, and related behaviors were examined with data from the evaluation of the Start Strong: Building Healthy Teen Relationships initiative, a dating violence primary prevention program targeting middle school students. Latent class and latent transition models were used to estimate distinct patterns of TDV and related behaviors of bullying and sexual harassment in seventh grade students at baseline and to estimate transition probabilities from one pattern of behavior to another at the 1-year follow-up. Intervention effects were estimated by conditioning transitions on exposure to Start Strong. Latent class analyses suggested four classes best captured patterns of these interrelated behaviors. Classes were characterized by elevated perpetration and victimization on most behaviors (the multiproblem class), bullying perpetration/victimization and sexual harassment victimization (the bully-harassment victimization class), bullying perpetration/victimization and psychological TDV victimization (bully-psychological victimization), and experience of bully victimization (bully victimization). Latent transition models indicated greater stability of class membership in the comparison group. Intervention students were less likely to transition to the most problematic pattern and more likely to transition to the least problem class. Although Start Strong has not been found to significantly change TDV, alternative evaluation models may find important differences. Latent transition analysis models suggest positive intervention impact, especially for the transitions at the most and the least positive end of the spectrum. Copyright © 2015. Published by Elsevier Inc.

Models of Latent Tuberculosis: Their Salient Features, Limitations, and Development

PubMed Central

Patel, Kamlesh; Jhamb, Sarbjit Singh; Singh, Prati Pal

2011-01-01

Latent tuberculosis is a subclinical condition caused by Mycobacterium tuberculosis, which affects about one-third of the population across the world. To abridge the chemotherapy of tuberculosis, it is necessary to have active drugs against latent form of M. tuberculosis. Therefore, it is imperative to devise in vitro and models of latent tuberculosis to explore potential drugs. In vitro models such as hypoxia, nutrient starvation, and multiple stresses are based on adverse conditions encountered by bacilli in granuloma. Bacilli experience oxygen depletion condition in hypoxia model, whereas the nutrient starvation model is based on deprivation of total nutrients from a culture medium. In the multiple stress model dormancy is induced by more than one type of stress. In silico mathematical models have also been developed to predict the interactions of bacilli with the host immune system and to propose structures for potential anti tuberculosis compounds. Besides these in vitro and in silico models, there are a number of in vivo animal models like mouse, guinea pig, rabbit, etc. Although they simulate human latent tuberculosis up to a certain extent but do not truly replicate human infection. All these models have their inherent merits and demerits. However, there is no perfect model for latent tuberculosis. Therefore, it is imperative to upgrade and refine existing models or develop a new model. However, battery of models will always be a better alternative to any single model as they will complement each other by overcoming their limitations. PMID:22219558

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

PubMed

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Exploring context and content links in social media: a latent space method.

PubMed

Qi, Guo-Jun; Aggarwal, Charu; Tian, Qi; Ji, Heng; Huang, Thomas S

2012-05-01

Social media networks contain both content and context-specific information. Most existing methods work with either of the two for the purpose of multimedia mining and retrieval. In reality, both content and context information are rich sources of information for mining, and the full power of mining and processing algorithms can be realized only with the use of a combination of the two. This paper proposes a new algorithm which mines both context and content links in social media networks to discover the underlying latent semantic space. This mapping of the multimedia objects into latent feature vectors enables the use of any off-the-shelf multimedia retrieval algorithms. Compared to the state-of-the-art latent methods in multimedia analysis, this algorithm effectively solves the problem of sparse context links by mining the geometric structure underlying the content links between multimedia objects. Specifically for multimedia annotation, we show that an effective algorithm can be developed to directly construct annotation models by simultaneously leveraging both context and content information based on latent structure between correlated semantic concepts. We conduct experiments on the Flickr data set, which contains user tags linked with images. We illustrate the advantages of our approach over the state-of-the-art multimedia retrieval techniques.

Reconceptualizing the classification of PNAS articles

PubMed Central

Airoldi, Edoardo M.; Erosheva, Elena A.; Fienberg, Stephen E.; Joutard, Cyrille; Love, Tanzy; Shringarpure, Suyash

2010-01-01

PNAS article classification is rooted in long-standing disciplinary divisions that do not necessarily reflect the structure of modern scientific research. We reevaluate that structure using latent pattern models from statistical machine learning, also known as mixed-membership models, that identify semantic structure in co-occurrence of words in the abstracts and references. Our findings suggest that the latent dimensionality of patterns underlying PNAS research articles in the Biological Sciences is only slightly larger than the number of categories currently in use, but it differs substantially in the content of the categories. Further, the number of articles that are listed under multiple categories is only a small fraction of what it should be. These findings together with the sensitivity analyses suggest ways to reconceptualize the organization of papers published in PNAS. PMID:21078953

Latent Heat Thermal Energy Storage: Effect of Metallic Mesh Size on Storage Time and Capacity

NASA Astrophysics Data System (ADS)

Shuja, S. Z.; Yilbas, B. S.

2015-11-01

Use of metallic meshes in latent heat thermal storage system shortens the charging time (total melting of the phase change material), which is favorable in practical applications. In the present study, effect of metallic mesh size on the thermal characteristics of latent heat thermal storage system is investigated. Charging time is predicted for various mesh sizes, and the influence of the amount of mesh material on the charging capacity is examined. An experiment is carried out to validate the numerical predictions. It is found that predictions of the thermal characteristics of phase change material with presence of metallic meshes agree well with the experimental data. High conductivity of the metal meshes enables to transfer heat from the edges of the thermal system towards the phase change material while forming a conduction tree in the system. Increasing number of meshes in the thermal system reduces the charging time significantly due to increased rate of conduction heat transfer in the thermal storage system; however, increasing number of meshes lowers the latent heat storage capacity of the system.

Annealing kinetics of latent particle tracks in Durango apatite

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

Afra, B.; Rodriguez, M. D.; Giulian, R.

2011-02-01

Using synchrotron small-angle x-ray scattering we determine the ''latent'' track morphology and the track annealing kinetics in the Durango apatite. The latter, measured during ex situ and in situ annealing experiments, suggests structural relaxation followed by recrystallization of the damaged material. The resolution of fractions of a nanometer with which the track radii are determined, as well as the nondestructive, artefact-free measurement methodology shown here, provides an effective means for in-depth studies of ion-track formation in natural minerals under a wide variety of geological conditions.

Morphofunctional Experience-Dependent Plasticity in the Honeybee Brain

ERIC Educational Resources Information Center

Andrione, Mara; Timberlake, Benjamin F.; Vallortigara, Giorgio; Antolini, Renzo; Haase, Albrecht

2017-01-01

Repeated or prolonged exposure to an odorant without any positive or negative reinforcement produces experience-dependent plasticity, which results in habituation and latent inhibition. In the honeybee ("Apis mellifera"), it has been demonstrated that, even if the absolute neural representation of an odor in the primary olfactory center,…

Identifying Transfer Student Subgroups by Academic and Social Adjustment: A Latent Class Analysis

ERIC Educational Resources Information Center

Fematt, Veronica Lavenant

2017-01-01

Community college transfer students often experience "transfer shock" at receiving four-year institutions, which includes a variety of academic, social, and institutional challenges associated with the first-year transfer experience. Research has demonstrated that first-year program interventions can facilitate the transition of…

ADVERSE CHILDHOOD EXPERIENCES AMONG YOUTH AGING OUT OF FOSTER CARE: A LATENT CLASS ANALYSIS

PubMed Central

Rebbe, Rebecca; Nurius, Paula S.; Ahrens, Kym R.; Courtney, Mark E.

2017-01-01

Research has demonstrated that youth who age out, or emancipate, from foster care face deleterious outcomes across a variety of domains in early adulthood. This article builds on this knowledge base by investigating the role of adverse childhood experience accumulation and composition on these outcomes. A latent class analysis was performed to identify three subgroups: Complex Adversity, Environmental Adversity, and Lower Adversity. Differences are found amongst the classes in terms of young adult outcomes in terms of socio-economic outcomes, psychosocial problems, and criminal behaviors. The results indicate that not only does the accumulation of adversity matter, but so does the composition of the adversity. These results have implications for policymakers, the numerous service providers and systems that interact with foster youth, and for future research. PMID:28458409

A Latent Variable Analysis of Continuing Professional Development Constructs Using PLS-SEM Modeling

ERIC Educational Resources Information Center

Yazdi, Mona Tabatabaee; Motallebzadeh, Khalil; Ashraf, Hamid; Baghaei, Purya

2017-01-01

Continuing Professional Development (CPD), in the area of teacher education, refers to the procedures, programs or strategies that help teachers encounter the challenges of their work and accomplish their own and their learning center's goals. To this aim, the purpose of this study is to propose and validate an appropriate model of EFL teachers'…

Construct Validation of the Self-Efficacy Teaching and Knowledge Instrument for Science Teachers-Revised (SETAKIST-R): Lessons Learned

ERIC Educational Resources Information Center

Pruski, Linda A.; Blanco, Sharon L.; Riggs, Rosemary A.; Grimes, Kandi K.; Fordtran, Chase W.; Barbola, Gina M.; Cornell, John E.; Lichtenstein, Michael J.

2013-01-01

Described herein is the academic lineage and independent validation of the Self-Efficacy Teaching and Knowledge Instrument for Science Teachers-Revised (SETAKIST-R). Data from 334 K-12 science teachers were analyzed using Partial Credit Rasch models. Principal components analysis on the person-item residuals suggest two latent dimensions:…

A Comparison of Narrative and Expository Text Comprehension for Students at Varying Levels of SES: A Latent Growth Curve Analysis

ERIC Educational Resources Information Center

Briggs, Laura Clark

2017-01-01

Research on secondary student reading comprehension performance is scant, yet demands for improved literacy at college and career levels indicate that an understanding of trends and growth patterns is necessary to better inform teaching and learning for high school students. To improve understanding of reading performance at the secondary level,…

A latent discriminative model-based approach for classification of imaginary motor tasks from EEG data.

PubMed

Saa, Jaime F Delgado; Çetin, Müjdat

2012-04-01

We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be used to model different brain states in the signal; and (3) involve learned statistical models matched to the classification task, avoiding some of the limitations of generative models. Our approach involves spatial filtering of the EEG signals and estimation of power spectra based on autoregressive modeling of temporal segments of the EEG signals. Given this time-frequency representation, we select certain frequency bands that are known to be associated with execution of motor tasks. These selected features constitute the data that are fed to the HCRF, parameters of which are learned from training data. Inference algorithms on the HCRFs are used for the classification of motor tasks. We experimentally compare this approach to the best performing methods in BCI competition IV as well as a number of more recent methods and observe that our proposed method yields better classification accuracy.

Predicting language outcomes for children learning AAC: Child and environmental factors

PubMed Central

Brady, Nancy C.; Thiemann-Bourque, Kathy; Fleming, Kandace; Matthews, Kris

2014-01-01

Purpose To investigate a model of language development for nonverbal preschool age children learning to communicate with AAC. Method Ninety-three preschool children with intellectual disabilities were assessed at Time 1, and 82 of these children were assessed one year later at Time 2. The outcome variable was the number of different words the children produced (with speech, sign or SGD). Children’s intrinsic predictor for language was modeled as a latent variable consisting of cognitive development, comprehension, play, and nonverbal communication complexity. Adult input at school and home, and amount of AAC instruction were proposed mediators of vocabulary acquisition. Results A confirmatory factor analysis revealed that measures converged as a coherent construct and an SEM model indicated that the intrinsic child predictor construct predicted different words children produced. The amount of input received at home but not at school was a significant mediator. Conclusions Our hypothesized model accurately reflected a latent construct of Intrinsic Symbolic Factor (ISF). Children who evidenced higher initial levels of ISF and more adult input at home produced more words one year later. Findings support the need to assess multiple child variables, and suggest interventions directed to the indicators of ISF and input. PMID:23785187

Families as Natural Experiments: A Procedure for Estimating the Potentially Biasing Influence of Families on Relationships Between Variables.

ERIC Educational Resources Information Center

Teachman, Jay D.

1995-01-01

Argues that data on siblings provide a way to account for the impact of unmeasured, omitted variables on relationships of interest because families form a sort of natural experiment, with similar experiences and common genetic heritage. Proposes a latent-variable structural equation approach to the problem, which provides estimates of both within-…

Measuring and Advancing Experimental Design Ability in an Introductory Course without Altering Existing Lab Curriculum†

PubMed Central

Shanks, Ryan A.; Robertson, Chuck L.; Haygood, Christian S.; Herdliksa, Anna M.; Herdliska, Heather R.; Lloyd, Steven A.

2017-01-01

Introductory biology courses provide an important opportunity to prepare students for future courses, yet existing cookbook labs, although important in their own way, fail to provide many of the advantages of semester-long research experiences. Engaging, authentic research experiences aid biology students in meeting many learning goals. Therefore, overlaying a research experience onto the existing lab structure allows faculty to overcome barriers involving curricular change. Here we propose a working model for this overlay design in an introductory biology course and detail a means to conduct this lab with minimal increases in student and faculty workloads. Furthermore, we conducted exploratory factor analysis of the Experimental Design Ability Test (EDAT) and uncovered two latent factors which provide valid means to assess this overlay model’s ability to increase advanced experimental design abilities. In a pre-test/post-test design, we demonstrate significant increases in both basic and advanced experimental design abilities in an experimental and comparison group. We measured significantly higher gains in advanced experimental design understanding in students in the experimental group. We believe this overlay model and EDAT factor analysis contribute a novel means to conduct and assess the effectiveness of authentic research experiences in an introductory course without major changes to the course curriculum and with minimal increases in faculty and student workloads. PMID:28904647

Early predictors of helpless thoughts and behaviors in children: developmental precursors to depressive cognitions.

PubMed

Cole, David A; Warren, Dana E; Dallaire, Danielle H; Lagrange, Beth; Travis, Rebekah; Ciesla, Jeffrey A

2007-04-01

Learned helplessness behavior and cognitions were assessed in 95 kindergarten-age children during a series of impossible puzzle trials followed by a solvable puzzle trial. Latent growth curve analysis revealed reliable individual differences in the trajectories of children's affect, motivation, and self-cognitions over time. Parents' reports of negative life events, harsh/negative parenting, and warm/positive parenting were associated with their children's learned helplessness behavioral trajectories and outcomes over the course of the puzzle trials. Results support speculations about the developmental origins of depressive explanatory or attributional style in children.

RUTI: a new chance to shorten the treatment of latent tuberculosis infection.

PubMed

Cardona, Pere-Joan

2006-01-01

Treatment of latent tuberculosis infection (LTBI) requires a long period of chemotherapy (9 months), which makes treatment-compliance extremely difficult. Current knowledge of latent bacilli and of the lesions with which they are associated suggests that these bacilli survive in granulomas with a central necrotic core and an outermost layer of foamy macrophages (FM) that represent an important immunosuppressive barrier. The presence of FM, which is especially strong in mice, explains not only the kinetics of the drainage of dead bacilli, debris and surfactant, but also how latent bacilli can escape from the granuloma and re-grow in the periphery, particularly in the alveolar spaces where they can disseminate easily. RUTI, a therapeutic vaccine made of detoxified, fragmented Mycobacterium tuberculosis cells, delivered in liposomes, was used to assess its effectiveness in a short period of chemotherapy (1 month). The rationale of this therapy was first to take advantage of the bactericidal properties of chemotherapy to kill active growing bacilli, eliminate the outermost layer of FM and reduce local inflammatory responses so as to avoid the predictable Koch phenomenon caused by M. tuberculosis antigens when given therapeutically. After chemotherapy, RUTI can be inoculated to reduce the probability of regrowth of the remaining latent bacilli. RUTI has already demonstrated its efficacy in controlling LTBI in experimental models of mice and guinea-pigs after a short period of chemotherapy; these experiments in animals showed the induction of a mixed Th1/Th2/Th3, polyantigenic response with no local or systemic toxicity. Local accumulation of specific CD8 T cells and a strong humoral response are characteristic features of RUTI that explain its protective properties; these are particular improvements when compared with BCG, although the regulatory response to RUTI may also be an important advantage. Further experiments using bigger animals (goats and mini-pigs) will provide more data on the efficacy of RUTI before starting phase I clinical trials.

The Blind Men and the Elephant: Identification of a Latent Maltreatment Construct for Youth in Foster Care

PubMed Central

Gabrielli, Joy; Jackson, Yo; Tunno, Angela M.; Hambrick, Erin P.

2017-01-01

Child maltreatment is a major public health concern due to its impact on developmental trajectories and consequences across mental and physical health outcomes. Operationalization of child maltreatment has been complicated, as research has used simple dichotomous counts to identification of latent class profiles. This study examines a latent measurement model assessed within foster youth inclusive of indicators of maltreatment chronicity and severity across four maltreatment types: physical, sexual, and psychological abuse, and neglect. Participants were 500 foster youth with a mean age of 12.99 years (SD = 2.95 years). Youth completed survey questions through a confidential audio computer-assisted self-interview program. A two-factor model with latent constructs of chronicity and severity of maltreatment revealed excellent fit across fit indices; however, the latent constructs were correlated .972. A one-factor model also demonstrated excellent model fit to the data (χ2 (16, n = 500) =28.087, p =.031, RMSEA (0.012 – 0.062) =.039, TLI =.990, CFI =.994, SRMR =.025) with a nonsignificant chi-square difference test comparing the one- and two-factor models. Invariance tests across age, gender, and placement type also were conducted with recommendations provided. Results suggest a single-factor latent model of maltreatment severity and chronicity can be attained. Thus, the maltreatment experiences reported by foster youth, though varied and complex, were captured in a model that may prove useful in later predictions of outcome behaviors. Appropriate identification of both the chronicity and severity of maltreatment inclusive of the range of maltreatment types remains a high priority for future research. PMID:28254690

Learning disease relationships from clinical drug trials.

PubMed

Haslam, Bryan; Perez-Breva, Luis

2017-01-01

Our objective is to test the limits of the assumption that better learning from data in medicine requires more granular data. We hypothesize that clinical trial metadata contains latent scientific, clinical, and regulatory expert knowledge that can be accessed to draw conclusions about the underlying biology of diseases. We seek to demonstrate that this latent information can be uncovered from the whole body of clinical trials. We extract free-text metadata from 93 654 clinical drug trials and introduce a representation that allows us to compare different trials. We then construct a network of diseases using only the trial metadata. We view each trial as the summation of expert knowledge of biological mechanisms and medical evidence linking a disease to a drug believed to modulate the pathways of that disease. Our network representation allows us to visualize disease relationships based on this underlying information. Our disease network shows surprising agreement with another disease network based on genetic data and on the Medical Subject Headings (MeSH) taxonomy, yet also contains unique disease similarities. The agreement of our results with other sources indicates that our premise regarding latent expert knowledge holds. The disease relationships unique to our network may be used to generate hypotheses for future biological and clinical research as well as drug repurposing and design. Our results provide an example of using experimental data on humans to generate biologically useful information and point to a set of new and promising strategies to link clinical outcomes data back to biological research. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Differential effects of two types of formative assessment in predicting performance of first-year medical students.

PubMed

Krasne, Sally; Wimmers, Paul F; Relan, Anju; Drake, Thomas A

2006-05-01

Formative assessments are systematically designed instructional interventions to assess and provide feedback on students' strengths and weaknesses in the course of teaching and learning. Despite their known benefits to student attitudes and learning, medical school curricula have been slow to integrate such assessments into the curriculum. This study investigates how performance on two different modes of formative assessment relate to each other and to performance on summative assessments in an integrated, medical-school environment. Two types of formative assessment were administered to 146 first-year medical students each week over 8 weeks: a timed, closed-book component to assess factual recall and image recognition, and an un-timed, open-book component to assess higher order reasoning including the ability to identify and access appropriate resources and to integrate and apply knowledge. Analogous summative assessments were administered in the ninth week. Models relating formative and summative assessment performance were tested using Structural Equation Modeling. Two latent variables underlying achievement on formative and summative assessments could be identified; a "formative-assessment factor" and a "summative-assessment factor," with the former predicting the latter. A latent variable underlying achievement on open-book formative assessments was highly predictive of achievement on both open- and closed-book summative assessments, whereas a latent variable underlying closed-book assessments only predicted performance on the closed-book summative assessment. Formative assessments can be used as effective predictive tools of summative performance in medical school. Open-book, un-timed assessments of higher order processes appeared to be better predictors of overall summative performance than closed-book, timed assessments of factual recall and image recognition.

Vegetation Monitoring with Gaussian Processes and Latent Force Models

NASA Astrophysics Data System (ADS)

Camps-Valls, Gustau; Svendsen, Daniel; Martino, Luca; Campos, Manuel; Luengo, David

2017-04-01

Monitoring vegetation by biophysical parameter retrieval from Earth observation data is a challenging problem, where machine learning is currently a key player. Neural networks, kernel methods, and Gaussian Process (GP) regression have excelled in parameter retrieval tasks at both local and global scales. GP regression is based on solid Bayesian statistics, yield efficient and accurate parameter estimates, and provides interesting advantages over competing machine learning approaches such as confidence intervals. However, GP models are hampered by lack of interpretability, that prevented the widespread adoption by a larger community. In this presentation we will summarize some of our latest developments to address this issue. We will review the main characteristics of GPs and their advantages in vegetation monitoring standard applications. Then, three advanced GP models will be introduced. First, we will derive sensitivity maps for the GP predictive function that allows us to obtain feature ranking from the model and to assess the influence of examples in the solution. Second, we will introduce a Joint GP (JGP) model that combines in situ measurements and simulated radiative transfer data in a single GP model. The JGP regression provides more sensible confidence intervals for the predictions, respects the physics of the underlying processes, and allows for transferability across time and space. Finally, a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical models of the system is presented. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Empirical evidence of the performance of these models will be presented through illustrative examples.

Capturing latent fingerprints from metallic painted surfaces using UV-VIS spectroscope

NASA Astrophysics Data System (ADS)

Makrushin, Andrey; Scheidat, Tobias; Vielhauer, Claus

2015-03-01

In digital crime scene forensics, contactless non-destructive detection and acquisition of latent fingerprints by means of optical devices such as a high-resolution digital camera, confocal microscope, or chromatic white-light sensor is the initial step prior to destructive chemical development. The applicability of an optical sensor to digitalize latent fingerprints primarily depends on reflection properties of a substrate. Metallic painted surfaces, for instance, pose a problem for conventional sensors which make use of visible light. Since metallic paint is a semi-transparent layer on top of the surface, visible light penetrates it and is reflected off of the metallic flakes randomly disposed in the paint. Fingerprint residues do not impede light beams making ridges invisible. Latent fingerprints can be revealed, however, using ultraviolet light which does not penetrate the paint. We apply a UV-VIS spectroscope that is capable of capturing images within the range from 163 to 844 nm using 2048 discrete levels. We empirically show that latent fingerprints left behind on metallic painted surfaces become clearly visible within the range from 205 to 385 nm. Our proposed streakiness score feature determining the proportion of a ridge-valley pattern in an image is applied for automatic assessment of a fingerprint's visibility and distinguishing between fingerprint and empty regions. The experiments are carried out with 100 fingerprint and 100 non-fingerprint samples.

Multi-task learning with group information for human action recognition

NASA Astrophysics Data System (ADS)

Qian, Li; Wu, Song; Pu, Nan; Xu, Shulin; Xiao, Guoqiang

2018-04-01

Human action recognition is an important and challenging task in computer vision research, due to the variations in human motion performance, interpersonal differences and recording settings. In this paper, we propose a novel multi-task learning framework with group information (MTL-GI) for accurate and efficient human action recognition. Specifically, we firstly obtain group information through calculating the mutual information according to the latent relationship between Gaussian components and action categories, and clustering similar action categories into the same group by affinity propagation clustering. Additionally, in order to explore the relationships of related tasks, we incorporate group information into multi-task learning. Experimental results evaluated on two popular benchmarks (UCF50 and HMDB51 datasets) demonstrate the superiority of our proposed MTL-GI framework.

Exploring cluster Monte Carlo updates with Boltzmann machines

NASA Astrophysics Data System (ADS)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Comparison of measured and modeled radiation, heat and water vapor fluxes: FIFE pilot study

NASA Technical Reports Server (NTRS)

Blad, Blaine L.; Hubbard, Kenneth G.; Verma, Shashi B.; Starks, Patrick; Norman, John M.; Walter-Shea, Elizabeth

1987-01-01

The feasibility of using radio frequency receivers to collect data from automated weather stations to model fluxes of latent heat, sensible heat, and radiation using routine weather data collected by automated weather stations was tested and the estimated fluxes were compared with fluxes measured over wheat. The model Cupid was used to model the fluxes. Two or more automated weather stations, interrogated by radio frequency and other means, were utilized to examine some of the climatic variability of the First ISLSCP (International Satellite Land-Surface Climatology Project) Field Experiment (FIFE) site, to measure and model reflected and emitted radiation streams from various locations at the site and to compare modeled latent and sensible heat fluxes with measured values. Some bidirectional reflected and emitted radiation data were collected from 23 locations throughout the FIFE site. Analysis of these data along with analysis of the measured sensible and latent heat fluxes is just beginning.

Silent films and strange stories: theory of mind, gender, and social experiences in middle childhood.

PubMed

Devine, Rory T; Hughes, Claire

2013-01-01

In this study of two hundred and thirty 8- to 13-year-olds, a new "Silent Films" task is introduced, designed to address the dearth of research on theory of mind in older children by providing a film-based analogue of F. G. E. Happé's (1994) Strange Stories task. Confirmatory factor analysis showed that all items from both tasks loaded onto a single theory-of-mind latent factor. With effects of verbal ability and family affluence controlled, theory-of-mind latent factor scores increased significantly with age, indicating that mentalizing skills continue to develop through middle childhood. Girls outperformed boys on the theory-of-mind latent factor, and the correlates of individual differences in theory of mind were gender specific: Low scores were related to loneliness in girls and to peer rejection in boys. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Behavior Based Social Dimensions Extraction for Multi-Label Classification

PubMed Central

Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin

2016-01-01

Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849

Global scale diagnoses of FGGE data

NASA Technical Reports Server (NTRS)

Paegle, J.

1985-01-01

Descriptive global scale diagnoses of the First Global Atmospheric Research Experiment SOP-1 analyses were made and compared against controlled, real data integrations of the Goddard Laboratory of Atmospheric Science (GLAS) general circulation model (GCM) as well as other data sets. The effects of critical latitudes were studied; the influence of tropical wind data and latent heating upon the GLAS GCM was diagnosed; planetary wave structure on various time scales from the diurnal to the monthly was studied; and the GLAS analyses were compared with other analyses. Short term controlled GLAS GCM integrations show that: (1) the inclusion of tropical wind data in real data integrations has an important influence in the mid-latitude prediction in both hemispheres; and (2) the tropical divergent wind reacts almost immediately to alteration of the tropical latent heating. The presence or absence of zonally averaged easterlies depends strongly upon the presence of tropical latent heating.

Lay Americans' views of why scientists disagree with each other.

PubMed

Johnson, Branden B; Dieckmann, Nathan F

2017-10-01

A survey experiment assessed response to five explanations of scientific disputes: problem complexity, self-interest, values, competence, and process choices (e.g. theories and methods). A US lay sample ( n = 453) did not distinguish interests from values, nor competence from process, as explanations of disputes. Process/competence was rated most likely and interests/values least; all, on average, were deemed likely to explain scientific disputes. Latent class analysis revealed distinct subgroups varying in their explanation preferences, with a more complex latent class structure for participants who had heard of scientific disputes in the past. Scientific positivism and judgments of science's credibility were the strongest predictors of latent class membership, controlling for scientific reasoning, political ideology, confidence in choice, scenario, education, gender, age, and ethnicity. The lack of distinction observed overall between different explanations, as well as within classes, raises challenges for further research on explanations of scientific disputes people find credible and why.

Spectral Retrieval of Latent Heating Profiles from TRMM PR Data: Comparison of Look-Up Tables

NASA Technical Reports Server (NTRS)

Shige, Shoichi; Takayabu, Yukari N.; Tao, Wei-Kuo; Johnson, Daniel E.; Shie, Chung-Lin

2003-01-01

The primary goal of the Tropical Rainfall Measuring Mission (TRMM) is to use the information about distributions of precipitation to determine the four dimensional (i.e., temporal and spatial) patterns of latent heating over the whole tropical region. The Spectral Latent Heating (SLH) algorithm has been developed to estimate latent heating profiles for the TRMM Precipitation Radar (PR) with a cloud- resolving model (CRM). The method uses CRM- generated heating profile look-up tables for the three rain types; convective, shallow stratiform, and anvil rain (deep stratiform with a melting level). For convective and shallow stratiform regions, the look-up table refers to the precipitation top height (PTH). For anvil region, on the other hand, the look- up table refers to the precipitation rate at the melting level instead of PTH. For global applications, it is necessary to examine the universality of the look-up table. In this paper, we compare the look-up tables produced from the numerical simulations of cloud ensembles forced with the Tropical Ocean Global Atmosphere (TOGA) Coupled Atmosphere-Ocean Response Experiment (COARE) data and the GARP Atlantic Tropical Experiment (GATE) data. There are some notable differences between the TOGA-COARE table and the GATE table, especially for the convective heating. First, there is larger number of deepest convective profiles in the TOGA-COARE table than in the GATE table, mainly due to the differences in SST. Second, shallow convective heating is stronger in the TOGA COARE table than in the GATE table. This might be attributable to the difference in the strength of the low-level inversions. Third, altitudes of convective heating maxima are larger in the TOGA COARE table than in the GATE table. Levels of convective heating maxima are located just below the melting level, because warm-rain processes are prevalent in tropical oceanic convective systems. Differences in levels of convective heating maxima probably reflect differences in melting layer heights. We are now extending our study to simulations of other field experiments (e.g. SCSMEX and ARM) in order to examine the universality of the look-up table. The impact of look-up tables on the retrieved latent heating profiles will also be assessed.

An Exploratory Analysis of Personality, Attitudes, and Study Skills on the Learning Curve within a Team-based Learning Environment

PubMed Central

Henry, Teague; Campbell, Ashley

2015-01-01

Objective. To examine factors that determine the interindividual variability of learning within a team-based learning environment. Methods. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students’ Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. Results. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. Conclusion. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course. PMID:25861101

An exploratory analysis of personality, attitudes, and study skills on the learning curve within a team-based learning environment.

PubMed

Persky, Adam M; Henry, Teague; Campbell, Ashley

2015-03-25

To examine factors that determine the interindividual variability of learning within a team-based learning environment. Students in a pharmacokinetics course were given 4 interim, low-stakes cumulative assessments throughout the semester and a cumulative final examination. Students' Myers-Briggs personality type was assessed, as well as their study skills, motivations, and attitudes towards team-learning. A latent curve model (LCM) was applied and various covariates were assessed to improve the regression model. A quadratic LCM was applied for the first 4 assessments to predict final examination performance. None of the covariates examined significantly impacted the regression model fit except metacognitive self-regulation, which explained some of the variability in the rate of learning. There were some correlations between personality type and attitudes towards team learning, with introverts having a lower opinion of team-learning than extroverts. The LCM could readily describe the learning curve. Extroverted and introverted personality types had the same learning performance even though preference for team-learning was lower in introverts. Other personality traits, study skills, or practice did not significantly contribute to the learning variability in this course.

An information processing/associative learning account of behavioral disinhibition in externalizing psychopathology.

PubMed

Endres, Michael J; Donkin, Chris; Finn, Peter R

2014-04-01

Externalizing psychopathology (EXT) is associated with low executive working memory (EWM) capacity and problems with inhibitory control and decision-making; however, the specific cognitive processes underlying these problems are not well known. This study used a linear ballistic accumulator computational model of go/no-go associative-incentive learning conducted with and without a working memory (WM) load to investigate these cognitive processes in 510 young adults varying in EXT (lifetime problems with substance use, conduct disorder, ADHD, adult antisocial behavior). High scores on an EXT factor were associated with low EWM capacity and higher scores on a latent variable reflecting the cognitive processes underlying disinhibited decision-making (more false alarms, faster evidence accumulation rates for false alarms [vFA], and lower scores on a Response Precision Index [RPI] measure of information processing efficiency). The WM load increased disinhibited decision-making, decisional uncertainty, and response caution for all subjects. Higher EWM capacity was associated with lower scores on the latent disinhibited decision-making variable (lower false alarms, lower vFAs and RPI scores) in both WM load conditions. EWM capacity partially mediated the association between EXT and disinhibited decision-making under no-WM load, and completely mediated this association under WM load. The results underline the role that EWM has in associative-incentive go/no-go learning and indicate that common to numerous types of EXT are impairments in the cognitive processes associated with the evidence accumulation-evaluation-decision process. PsycINFO Database Record (c) 2014 APA, all rights reserved.

An information processing/associative learning account of behavioral disinhibition in externalizing psychopathology

PubMed Central

Endres, Michael J.; Donkin, Chris; Finn, Peter R.

2014-01-01

Externalizing psychopathology (EXT) is associated with low executive working memory (EWM) capacity and problems with inhibitory control and decision-making; however, the specific cognitive processes underlying these problems are not well known. This study used a linear ballistic accumulator computational model of go/no-go associative-incentive learning conducted with and without a working memory (WM) load to investigate these cognitive processes in 510 young adults varying in EXT (lifetime problems with substance use, conduct disorder, ADHD, adult antisocial behavior). High scores on an EXT factor were associated with low EWM capacity and higher scores on a latent variable reflecting the cognitive processes underlying disinhibited decision making (more false alarms, faster evidence accumulation rates for false alarms (vFA), and lower scores on a Response Precision Index (RPI) measure of information processing efficiency). The WM load increased disinhibited decision making, decisional uncertainty, and response caution for all subjects. Higher EWM capacity was associated with lower scores on the latent disinhibited decision making variable (lower false alarms, lower vFAs and RPI scores) in both WM load conditions. EWM capacity partially mediated the association between EXT and disinhibited decision making under no-WM load, and completely mediated this association under WM load. The results underline the role that EWM has in associative – incentive go/no-go learning and indicate that common to numerous types of EXT are impairments in the cognitive processes associated with the evidence accumulation – evaluation – decision process. PMID:24611834

Potentiation of latent inhibition by haloperidol and clozapine is attenuated in Dopamine D2 receptor (Drd-2)-deficient mice: Do antipsychotics influence learning to ignore irrelevant stimuli via both Drd-2 and non-Drd-2 mechanisms?

PubMed Central

O’Callaghan, Matthew J; Bay-Richter, Cecilie; O’Tuathaigh, Colm MP; Heery, David M; Waddington, John L; Moran, Paula M

2014-01-01

Whether the dopamine Drd-2 receptor is necessary for the behavioural action of antipsychotic drugs is an important question, as Drd-2 antagonism is responsible for their debilitating motor side effects. Using Drd-2 null mice (Drd2 -/-) it has previously been shown that Drd-2 is not necessary for antipsychotic drugs to reverse D-amphetamine disruption of latent inhibition (LI), a behavioural measure of learning to ignore irrelevant stimuli. Weiner’s ‘two-headed’ model indicates that antipsychotics not only reverse LI disruption, ‘disrupted LI’, but also potentiate LI when low/absent in controls, ‘persistent’ LI. We investigated whether antipsychotic drugs haloperidol or clozapine potentiated LI in wild-type controls or Drd2 -/-. Both drugs potentiated LI in wild-type but not in Drd2-/- mice, suggesting moderation of this effect of antipsychotics in the absence of Drd-2. Haloperidol potentiated LI similarly in both Drd1-/- and wild-type mice, indicating no such moderation in Drd1-/-. These data suggest that antipsychotic drugs can have either Drd-2 or non-Drd-2 effects on learning to ignore irrelevant stimuli, depending on how the abnormality is produced. Identification of the non-Drd-2 mechanism may help to identify novel non-Drd2 based therapeutic strategies for psychosis. PMID:25122042

Latent semantic analysis cosines as a cognitive similarity measure: Evidence from priming studies.

PubMed

Günther, Fritz; Dudschig, Carolin; Kaup, Barbara

2016-01-01

In distributional semantics models (DSMs) such as latent semantic analysis (LSA), words are represented as vectors in a high-dimensional vector space. This allows for computing word similarities as the cosine of the angle between two such vectors. In two experiments, we investigated whether LSA cosine similarities predict priming effects, in that higher cosine similarities are associated with shorter reaction times (RTs). Critically, we applied a pseudo-random procedure in generating the item material to ensure that we directly manipulated LSA cosines as an independent variable. We employed two lexical priming experiments with lexical decision tasks (LDTs). In Experiment 1 we presented participants with 200 different prime words, each paired with one unique target. We found a significant effect of cosine similarities on RTs. The same was true for Experiment 2, where we reversed the prime-target order (primes of Experiment 1 were targets in Experiment 2, and vice versa). The results of these experiments confirm that LSA cosine similarities can predict priming effects, supporting the view that they are psychologically relevant. The present study thereby provides evidence for qualifying LSA cosine similarities not only as a linguistic measure, but also as a cognitive similarity measure. However, it is also shown that other DSMs can outperform LSA as a predictor of priming effects.

Using medical knowledge sources on handheld computers--a qualitative study among junior doctors.

PubMed

Axelson, Christian; Wårdh, Inger; Strender, Lars-Erik; Nilsson, Gunnar

2007-09-01

The emergence of mobile computing could have an impact on how junior doctors learn. To exploit this opportunity it is essential to understand their information seeking process. To explore junior doctors' experiences of using medical knowledge sources on handheld computers. Interviews with five Swedish junior doctors. A qualitative manifest content analysis of a focus group interview followed by a qualitative latent content analysis of two individual interviews. A focus group interview showed that users were satisfied with access to handheld medical knowledge sources, but there was concern about contents, reliability and device dependency. Four categories emerged from individual interviews: (1) A feeling of uncertainty about using handheld technology in medical care; (2) A sense of security that handhelds can provide; (3) A need for contents to be personalized; (4) A degree of adaptability to make the handheld a versatile information tool. A theme was established to link the four categories together, as expressed in the Conclusion section. Junior doctors' experiences of using medical knowledge sources on handheld computers shed light on the need to decrease uncertainty about clinical decisions during medical internship, and to find ways to influence the level of self-confidence in the junior doctor's process of decision-making.

Health Educators' Perceptions of a Sexual Health Intervention for Homeless Adolescents

PubMed Central

Rew, Lynn; Rochlen, Aaron B.; Murphey, Christina

2008-01-01

Objective The purpose of this qualitative descriptive study was to explore the perceptions and experiences of health educators in providing a brief, street-based intervention to homeless adolescents. Method Qualitative data were collected via e-mail from a purposive sample of 13 male and female health educators who provided the intervention and analyzed using manifest and latent content analysis techniques. Results Five categories with two or more subcategories were identified in the data and included how the educators' views changed, how they felt homeless youth were similar to and different from other adolescents, positive aspects and challenges of providing the intervention, and suggestions for future interventionists working with this population. Conclusions The health educators' practice was strengthened over the course of providing the intervention through their positive experiences, changes in their perceptions, some of which were biased, and ability to confront the challenges that accompany working with this vulnerable population. Practice Implications Health educators who work with this population should learn about the culture of homeless youth and characteristics of homeless youth that may influence their participation in a sexual health intervention. Moreover, they need to be non-judgmental, practice the intervention, be aware of their biases, and remain flexible. PMID:18343623

Improving Learning for All Students through Equity-Based Inclusive Reform Practices: Effectiveness of a Fully Integrated Schoolwide Model on Student Reading and Math Achievement

ERIC Educational Resources Information Center

Choi, Jeong Hoon; Meisenheimer, Jessica M.; McCart, Amy B.; Sailor, Wayne

2017-01-01

The present investigation examines the schoolwide applications model (SAM) as a potentially effective school reform model for increasing equity-based inclusive education practices while enhancing student reading and math achievement for all students. A 3-year quasi-experimental comparison group analysis using latent growth modeling (LGM) was used…

Mediators, Moderators, and Predictors of 1-Year Outcomes Among Children Treated for Early-Onset Conduct Problems: A Latent Growth Curve Analysis

ERIC Educational Resources Information Center

Beauchaine, Theodore P.; Webster-Stratton, Carolyn; Reid, M. Jamila

2006-01-01

Several child conduct problem interventions have been classified as either efficacious or well established. Nevertheless, much remains to be learned about predictors of treatment response and mechanisms of behavioral change. In this study, the authors combine data from 6 randomized clinical trials and 514 children, ages 3.0-8.5 years, to evaluate…

Is There a Typology of Teacher and Leader Responders to Call and Do They Cluster in Different Types of Schools? A Two-Level Latent Class Analysis of Call Survey Data

ERIC Educational Resources Information Center

Bowers, Alex J.; Blitz, Mark; Modeste, Marsha; Salisbury, Jason; Halverson, Richard R.

2017-01-01

Background: Across the recent research on school leadership, leadership for learning has emerged as a strong framework for integrating current theories, such as instructional, transformational, and distributed leadership as well as effective human resource practices, instructional evaluation, and resource allocation. Yet, questions remain as to…

Patterns of Cognitive Impairments among Heroin and Cocaine Users: The Association with Self-Reported Learning Disabilities and Infectious Disease

ERIC Educational Resources Information Center

Severtson, Stevan G.; Hedden, Sarra L.; Martins, Silvia S.; Latimer, William W.

2012-01-01

This study used data from six neuropsychological measures of executive function (EF) and general intellectual functioning (GIF) administered to 303 regular users of heroin and/or cocaine as indicators in a latent profile analysis (LPA). Results indicated the presence of three profiles: impaired GIF and EF profile (30.8%), intact GIF and EF profile…

Phonological Awareness and Rapid Automatized Naming Predicting Early Development in Reading and Spelling: Results from a Cross-Linguistic Longitudinal Study

ERIC Educational Resources Information Center

Furnes, Bjarte; Samuelsson, Stefan

2011-01-01

In this study, the relationship between latent constructs of phonological awareness (PA) and rapid automatized naming (RAN) was investigated and related to later measures of reading and spelling in children learning to read in different alphabetic writing systems (i.e., Norwegian/Swedish vs. English). 750 U.S./Australian children and 230…

Integrated Low-Rank-Based Discriminative Feature Learning for Recognition.

PubMed

Zhou, Pan; Lin, Zhouchen; Zhang, Chao

2016-05-01

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

Semi-supervised tracking of extreme weather events in global spatio-temporal climate datasets

NASA Astrophysics Data System (ADS)

Kim, S. K.; Prabhat, M.; Williams, D. N.

2017-12-01

Deep neural networks have been successfully applied to solve problem to detect extreme weather events in large scale climate datasets and attend superior performance that overshadows all previous hand-crafted methods. Recent work has shown that multichannel spatiotemporal encoder-decoder CNN architecture is able to localize events in semi-supervised bounding box. Motivated by this work, we propose new learning metric based on Variational Auto-Encoders (VAE) and Long-Short-Term-Memory (LSTM) to track extreme weather events in spatio-temporal dataset. We consider spatio-temporal object tracking problems as learning probabilistic distribution of continuous latent features of auto-encoder using stochastic variational inference. For this, we assume that our datasets are i.i.d and latent features is able to be modeled by Gaussian distribution. In proposed metric, we first train VAE to generate approximate posterior given multichannel climate input with an extreme climate event at fixed time. Then, we predict bounding box, location and class of extreme climate events using convolutional layers given input concatenating three features including embedding, sampled mean and standard deviation. Lastly, we train LSTM with concatenated input to learn timely information of dataset by recurrently feeding output back to next time-step's input of VAE. Our contribution is two-fold. First, we show the first semi-supervised end-to-end architecture based on VAE to track extreme weather events which can apply to massive scaled unlabeled climate datasets. Second, the information of timely movement of events is considered for bounding box prediction using LSTM which can improve accuracy of localization. To our knowledge, this technique has not been explored neither in climate community or in Machine Learning community.

Exposure to Different Types of Violence and Subsequent Sexual Risk Behavior among Female STD Clinic Patients: A Latent Class Analysis

PubMed Central

Walsh, Jennifer L.; Senn, Theresa E.; Carey, Michael P.

2013-01-01

Objective Diverse forms of violence, including childhood maltreatment (CM), intimate partner violence (IPV), and exposure to community violence (ECV), have been linked separately with sexual risk behaviors. However, few studies have explored multiple experiences of violence simultaneously in relation to sexual risk-taking, especially in women who are most vulnerable to violent experiences. Methods Participants were 481 women (66% African American, Mage = 27 years) attending a publicly-funded STD clinic who reported on their past and current experiences with violence and their current sexual risk behavior. We identified patterns of experience with violence using latent class analysis (LCA) and investigated which combinations of experiences were associated with the riskiest sexual outcomes. Results Four classes of women with different experiences of violence were identified: Low Violence (39%), Predominantly ECV (20%), Predominantly CM (23%), and Multiply Victimized (18%). Women in the Multiply Victimized and Predominantly ECV classes reported the highest levels of sexual risk behavior, including more lifetime sexual partners and a greater likelihood of receiving STD treatment and using substances before sex. Conclusions Women with different patterns of violent experiences differed in their sexual risk behavior. Interventions to reduce sexual risk should address violence against women, focusing on experiences with multiple types of violence and experiences specifically with ECV. Additional research is needed to determine the best ways to address violence in sexual risk reduction interventions. PMID:23626921

Content-Related Knowledge of Biology Teachers from Secondary Schools: Structure and learning opportunities

NASA Astrophysics Data System (ADS)

Großschedl, Jörg; Mahler, Daniela; Kleickmann, Thilo; Harms, Ute

2014-09-01

Teachers' content-related knowledge is a key factor influencing the learning progress of students. Different models of content-related knowledge have been proposed by educational researchers; most of them take into account three categories: content knowledge, pedagogical content knowledge, and curricular knowledge. As there is no consensus about the empirical separability (i.e. empirical structure) of content-related knowledge yet, a total of 134 biology teachers from secondary schools completed three tests which were to capture each of the three categories of content-related knowledge. The empirical structure of content-related knowledge was analyzed by Rasch analysis, which suggests content-related knowledge to be composed of (1) content knowledge, (2) pedagogical content knowledge, and (3) curricular knowledge. Pedagogical content knowledge and curricular knowledge are highly related (rlatent = .70). The latent correlations between content knowledge and pedagogical content knowledge (rlatent = .48)-and curricular knowledge, respectively (rlatent = .35)-are moderate to low (all ps < .001). Beyond the empirical structure of content-related knowledge, different learning opportunities for teachers were investigated with regard to their relationship to content knowledge, pedagogical content knowledge, and curricular knowledge acquisition. Our results show that an in-depth training in teacher education, professional development, and teacher self-study are positively related to particular categories of content-related knowledge. Furthermore, our results indicate that teaching experience is negatively related to curricular knowledge, compared to no significant relationship with content knowledge and pedagogical content knowledge.

Using Instrumental Variable (IV) Tests to Evaluate Model Specification in Latent Variable Structural Equation Models*

PubMed Central

Kirby, James B.; Bollen, Kenneth A.

2009-01-01

Structural Equation Modeling with latent variables (SEM) is a powerful tool for social and behavioral scientists, combining many of the strengths of psychometrics and econometrics into a single framework. The most common estimator for SEM is the full-information maximum likelihood estimator (ML), but there is continuing interest in limited information estimators because of their distributional robustness and their greater resistance to structural specification errors. However, the literature discussing model fit for limited information estimators for latent variable models is sparse compared to that for full information estimators. We address this shortcoming by providing several specification tests based on the 2SLS estimator for latent variable structural equation models developed by Bollen (1996). We explain how these tests can be used to not only identify a misspecified model, but to help diagnose the source of misspecification within a model. We present and discuss results from a Monte Carlo experiment designed to evaluate the finite sample properties of these tests. Our findings suggest that the 2SLS tests successfully identify most misspecified models, even those with modest misspecification, and that they provide researchers with information that can help diagnose the source of misspecification. PMID:20419054

[Latent Class Analysis of Gambling Activities among Korean Adolescents].

PubMed

Kang, Kyonghwa; Kim, Hyeongsu; Park, Ae Ran; Kim, Hee Young; Lee, Kunsei

2018-04-01

The aim of this study is to identify the types of gambling among adolescents and provide basic prevention information regarding adolescents' gambling problems. Secondary data from representative national survey on 2015 Youth Gambling Problems of Korea Center on Gambling Problems were used. Using latent class analysis (LCA), 13 gambling types such as offline and online games of 14,011 adolescents were classified, and gambling experiences and characteristics were analyzed. The subgroups of adolescent gambling were identified as four latent classes: a rare group (84.5% of the sample), a risk group (1.0%), an offline group (11.9%), and an expanded group (2.6%). The types and characteristics of gambling among the latent classes differed. In the risk group, adolescents participated in online illegal sports betting and internet casino, and gambling time, gambling expenses, and the number of gambling types were higher than other groups. Gambling frequently occur among adolescent, and the subtypes of gambling did not reveal homogeneous characteristics. In order to prevent adolescent gambling problems, it is a necessary to develop tailored prevention intervention in the nursing field, which is appropriate to the characteristics of adolescent gambling group and can help with early identification. © 2018 Korean Society of Nursing Science.

Activation of Latent HIV Using Drug-loaded Nanoparticles

NASA Astrophysics Data System (ADS)

Kovochich, Michael

Antiretroviral therapy is currently only capable of controlling human immunodeficiency virus (HIV) replication, rather than completely eradicating virus from patients. This is due in part to the establishment of a latent virus reservoir in resting CD4+ T-cells, which persists even in the presence of highly active antiretroviral therapy (HAART). It is thought that forced activation of latently infected cells could induce virus production, allowing targeting of the cell by the immune response. A variety of molecules are able to stimulate HIV from latency. However, no tested purging strategy has proven capable of eliminating the infection completely or preventing viral rebound if therapy is stopped. Hence, novel latency activation approaches are required. Nanoparticles can offer several advantages over more traditional drug delivery methods, including improved drug solubility, stability, and the ability to simultaneously target multiple different molecules to particular cell or tissue types. Here we describe the development of a novel lipid nanoparticle with the protein kinase C activator bryostatin-2 incorporated (LNP-Bry). These particles can target, activate primary human CD4+ T-cells, and stimulate latent virus production from human T-cell lines in vitro and from latently infected cells in a humanized mouse model ex vivo. This activation was synergistically enhanced by the histone deacetylase inhibitor (HDACi) sodium butyrate. Furthermore, LNP-Bry can also be loaded with the protease inhibitor nelfinavir (LNP-Bry-Nel), producing a particle capable of both activating latent virus and inhibiting viral spread. LNP-Bry was further tested for its in vivo biodistribution in both wild type mice (C57 black 6), as well as humanized mice (SCID-hu Thy/Liv, and bone marrow-liver-thymus [BLT]). LNP-Bry accumulated in the spleen and induced the early activation marker CD69 in wild type mice. Taken together, these data demonstrate the ability of nanotechnological approaches to provide improved methods for activating latent HIV and provide key proof-of-principle experiments showing how novel delivery systems may enhance future HIV therapy.

Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis.

PubMed

Yeung, Wing-Fai; Chung, Ka-Fai; Zhang, Nevin Lian-Wen; Zhang, Shi Ping; Yung, Kam-Ping; Chen, Pei-Xian; Ho, Yan-Yee

2016-01-01

Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes. One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes. The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into fire, Liver fire flaming upward, Stomach disharmony, Hyperactivity of fire due to yin deficiency, Heart-kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified. Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical feature co-occurrence patterns, four mutual-exclusion patterns, and one pattern with a single clinical feature variable.

Exploring chiropractic students' experiences of the educational environment in healthcare professional training: a qualitative study.

PubMed

Palmgren, Per J; Laksov, Klara Bolander

2015-08-05

The educational environment has a significant impact on students' behavior, sense of well-being, and academic advancement. While various research methodologies have been used to explore the educational environment, there is a paucity of studies employing qualitative research methods. This study engages in an in-depth exploration of chiropractic students' experiences of the meaning of the educational environment. A qualitative approach was employed by interviewing 26 students in four focus group interviews at two different points in time. A conventional manifest and latent content analysis was chosen to investigate and interpret the experiences of the educational environment in an undergraduate chiropractic training institution in Sweden. The analysis resulted in five overarching themes: Personal growth; Being part of a community; A place of meaningfulness; Trust in a regulated system; and Scaffolding relationships. Early in the training, the meaning of the educational environment was experienced as part of a vocational community and the scaffolding of intra-institutional relationships. In later stages, the environment was experienced in terms of personal growth - balancing academic pressures and progress within the professional community - thus laying the foundations for autonomy and motivation. During the clinical training, the environment was experienced as where learning happens, thus creating a place of meaningfulness. Throughout the training, the formal and clinical environments were experienced as isolating, with little bridging between the two. A regulated system - conveying an operative organization with clear communication regarding what to expect - was experienced as important for an apt educational environment. We found that experiences of an educational environment are dynamic and change over time. When restructuring or evaluating curriculums, educational managers can consider the emerged themes as constituting facets relating to the educational environment, and thus possible learning conditions. Likewise, researchers can consider these aspects of the educational environment when: interpreting results from quantitative and qualitative inquiries, constructing and refining instruments, or conceptualizing and framing the educational environment phenomenon.

Analyzing large-scale proteomics projects with latent semantic indexing.

PubMed

Klie, Sebastian; Martens, Lennart; Vizcaíno, Juan Antonio; Côté, Richard; Jones, Phil; Apweiler, Rolf; Hinneburg, Alexander; Hermjakob, Henning

2008-01-01

Since the advent of public data repositories for proteomics data, readily accessible results from high-throughput experiments have been accumulating steadily. Several large-scale projects in particular have contributed substantially to the amount of identifications available to the community. Despite the considerable body of information amassed, very few successful analyses have been performed and published on this data, leveling off the ultimate value of these projects far below their potential. A prominent reason published proteomics data is seldom reanalyzed lies in the heterogeneous nature of the original sample collection and the subsequent data recording and processing. To illustrate that at least part of this heterogeneity can be compensated for, we here apply a latent semantic analysis to the data contributed by the Human Proteome Organization's Plasma Proteome Project (HUPO PPP). Interestingly, despite the broad spectrum of instruments and methodologies applied in the HUPO PPP, our analysis reveals several obvious patterns that can be used to formulate concrete recommendations for optimizing proteomics project planning as well as the choice of technologies used in future experiments. It is clear from these results that the analysis of large bodies of publicly available proteomics data by noise-tolerant algorithms such as the latent semantic analysis holds great promise and is currently underexploited.

Exploiting Language Models to Classify Events from Twitter

PubMed Central

Vo, Duc-Thuan; Hai, Vo Thuan; Ock, Cheol-Young

2015-01-01

Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguishing terms between tweets in events and measure their similarities with learning language models such as ConceptNet and a latent Dirichlet allocation method for selectional preferences (LDA-SP), which have been widely studied based on large text corpora within computational linguistic relations. The relationship of term words in tweets will be discovered by checking them under each model. We then proposed a method to compute the similarity between tweets based on tweets' features including common term words and relationships among their distinguishing term words. It will be explicit and convenient for applying to k-nearest neighbor techniques for classification. We carefully applied experiments on the Edinburgh Twitter Corpus to show that our method achieves competitive results for classifying events. PMID:26451139

Multilinear Graph Embedding: Representation and Regularization for Images.

PubMed

Chen, Yi-Lei; Hsu, Chiou-Ting

2014-02-01

Given a set of images, finding a compact and discriminative representation is still a big challenge especially when multiple latent factors are hidden in the way of data generation. To represent multifactor images, although multilinear models are widely used to parameterize the data, most methods are based on high-order singular value decomposition (HOSVD), which preserves global statistics but interprets local variations inadequately. To this end, we propose a novel method, called multilinear graph embedding (MGE), as well as its kernelization MKGE to leverage the manifold learning techniques into multilinear models. Our method theoretically links the linear, nonlinear, and multilinear dimensionality reduction. We also show that the supervised MGE encodes informative image priors for image regularization, provided that an image is represented as a high-order tensor. From our experiments on face and gait recognition, the superior performance demonstrates that MGE better represents multifactor images than classic methods, including HOSVD and its variants. In addition, the significant improvement in image (or tensor) completion validates the potential of MGE for image regularization.

Methods for Assessment of Memory Reactivation.

PubMed

Liu, Shizhao; Grosmark, Andres D; Chen, Zhe

2018-04-13

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

A Discrete Choice Conjoint Experiment to Evaluate Parent Preferences for Treatment of Young, Medication Naive Children with ADHD

ERIC Educational Resources Information Center

Waschbusch, Daniel A.; Cunningham, Charles E.; Pelham, William E., Jr.; Rimas, Heather L.; Greiner, Andrew R.; Gnagy, Elizabeth M.; Waxmonsky, James; Fabiano, Gregory A.; Robb, Jessica A.; Burrows-MacLean, Lisa; Scime, Mindy; Hoffman, Martin T.

2011-01-01

The current study examined treatment preferences of 183 parents of young (average age = 5.8 years, SD = 0.6), medication naive children with ADHD. Preferences were evaluated using a discrete choice experiment in which parents made choices between different combinations of treatment characteristics, outcomes, and costs. Latent class analysis…

Latent Profiles among Sexual Assault Survivors: Implications for Defensive Coping and Resistance

ERIC Educational Resources Information Center

Macy, Rebecca J.; Nurius, Paula S.; Norris, Jeanette

2007-01-01

Rape resistance trainings need to prepare women to recognize and resist sexual assault across a range of experiences and contexts. To help address this need, this research used an investigation of 415 college women who completed a survey about their situational responding to an experience of acquaintance sexual assault. A previously established…

Racial Socialization and Racial Identity: African American Parents' Messages about Race as Precursors to Identity

ERIC Educational Resources Information Center

Neblett, Enrique W., Jr.; Smalls, Ciara P.; Ford, Kahlil R.; Nguyen, Hoa X.; Sellers, Robert M.

2009-01-01

This study uses two waves of data to examine the relationships among patterns of racial socialization experiences and racial identity in a sample of 358 African American adolescents (60% female and 40% male). Using latent class analyses, we identified three patterns of adolescent-reported racial socialization experiences: High Positive, Moderate…

Patterns of Racial Socialization and Psychological Adjustment: Can Parental Communications about Race Reduce the Impact of Racial Discrimination?

ERIC Educational Resources Information Center

Neblett, Enrique W., Jr.; White, Rhonda L.; Ford, Kahlil R.; Philip, Cheri L.; Nguye-N, Hoa X.; Sellers, Robert M.

2008-01-01

This study uses two waves of data to examine the relations among racial discrimination experiences, patterns of racial socialization practices, and psychological adjustment in a sample of 361 African American adolescents. Using latent class analyses, we identified four patterns of child-reported racial socialization experiences: Moderate Positive,…

Performance of the cometary experiment MUPUS on the body Earth

NASA Astrophysics Data System (ADS)

Marczewski, W.; Usowicz, B.; Schröer, K.; Seiferlin, K.; Spohn, T.

2003-04-01

Thermal experiment MUPUS for the Rosetta mission was extensively experience in field and laboratory conditions to predict its performance under physical processes available on the Earth. The goal was not guessing a cometary material in the ground but available behavior of thermal sensor responses monitoring mass and energy transfer. The processes expected on a comet are different in composition and environmental from those met on the Earth but basically similar in physics. Nature of energy powering the processes is also essentially the same - solar radiation. Several simple laboratory experiments with freezing and thawing with water ice, with mixture of water and oil and water layers strongly diverged by salinity revealed capability of recognition layered structure of the medium under test. More over effects of slow convection and latent heat related to the layers are also observed well. Cometary environment without atmosphere makes process of sublimation dominant. Open air conditions on the Earth may also offer a change of state in matter but between different phases. Learning temperature gradient in snow layers under thawing show that effects stimulated by a cause of daily cycling may be detected thermally. Results from investigations in snow made on Spitzbergen are good proofs on capability of the method. Relevance of thermal effects to heat powered processes of mass transport in the matter of ground is meaningful for the cometary experiment of MUPUS and for Earth sciences much concerned on water, gas and solid matter transport in the terrestrial ground. Results leading to energy balance studied on the Earth surface may be interesting also for the experiment on the comet and are to be discussed.

Can overeating induce conditioned taste avoidance in previously food restricted rats?

PubMed

Hertel, Amanda; Eikelboom, Roelof

2010-03-30

While feeding is rewarding, the feeling of satiation has been theorized to have a mixed affect. Using a food restriction model of overeating we examined whether bingeing was capable of supporting conditioned taste avoidance (CTA). Adult male Sprague-Dawley rats were maintained on either an ad lib (n=8) or restricted (50% of regular consumption; n=24) food access for 20 days. On Days 9, 14, and 19 all rats were given access to a novel saccharin solution in place of water, and two groups of food restricted rats were given access to either 100% of regular food consumption or ad lib food. Ad lib access in the restricted rats induced significant overeating on all three exposures. After all rats were returned to ad lib feeding, a 24h two-bottle saccharin/water choice test displayed significantly reduced saccharin consumption in the overeating rats, compared to those in the other 3 groups. To determine whether this avoidance was due to a learned association, a second experiment used a latent inhibition paradigm, familiarizing half the rats with the saccharin for 8 days prior to pairing it with overeating. Using the design of Experiment 1, with only the continuously ad lib and the restricted to ad lib feeding groups, it was found that the overeating-induced saccharin avoidance was attenuated by the pre-exposure. These results suggest that self-induced overeating is capable of supporting a learned avoidance of a novel solution suggestive of a conditioned satiety or taste avoidance. (c) 2009 Elsevier Inc. All rights reserved.

Elemental representation and configural mappings: combining elemental and configural theories of associative learning.

PubMed

McLaren, I P L; Forrest, C L; McLaren, R P

2012-09-01

In this article, we present our first attempt at combining an elemental theory designed to model representation development in an associative system (based on McLaren, Kaye, & Mackintosh, 1989) with a configural theory that models associative learning and memory (McLaren, 1993). After considering the possible advantages of such a combination (and some possible pitfalls), we offer a hybrid model that allows both components to produce the phenomena that they are capable of without introducing unwanted interactions. We then successfully apply the model to a range of phenomena, including latent inhibition, perceptual learning, the Espinet effect, and first- and second-order retrospective revaluation. In some cases, we present new data for comparison with our model's predictions. In all cases, the model replicates the pattern observed in our experimental results. We conclude that this line of development is a promising one for arriving at general theories of associative learning and memory.

Factors influencing readiness to deploy in disaster response: findings from a cross-sectional survey of the Department of Veterans Affairs Disaster Emergency Medical Personnel System

PubMed Central

2014-01-01

Background The Disaster Emergency Medical Personnel System (DEMPS) program provides a system of volunteers whereby active or retired Department of Veterans Affairs (VA) personnel can register to be deployed to support other VA facilities or the nation during national emergencies or disasters. Both early and ongoing volunteer training is required to participate. Methods This study aims to identify factors that impact willingness to deploy in the event of an emergency. This analysis was based on responses from 2,385 survey respondents (response rate, 29%). Latent variable path models were developed and tested using the EQS structural equations modeling program. Background demographic variables of education, age, minority ethnicity, and female gender were used as predictors of intervening latent variables of DEMPS Volunteer Experience, Positive Attitude about Training, and Stress. The model had acceptable fit statistics, and all three intermediate latent variables significantly predicted the outcome latent variable Readiness to Deploy. Results DEMPS Volunteer Experience and a Positive Attitude about Training were associated with Readiness to Deploy. Stress was associated with decreased Readiness to Deploy. Female gender was negatively correlated with Readiness to Deploy; however, there was an indirect relationship between female gender and Readiness to Deploy through Positive Attitude about Training. Conclusions These findings suggest that volunteer emergency management response programs such as DEMPS should consider how best to address the factors that may make women less ready to deploy than men in order to ensure adequate gender representation among emergency responders. The findings underscore the importance of training opportunities to ensure that gender-sensitive support is a strong component of emergency response, and may apply to other emergency response programs such as the Medical Reserve Corps and the American Red Cross. PMID:25038628

Factors influencing readiness to deploy in disaster response: findings from a cross-sectional survey of the Department of Veterans Affairs Disaster Emergency Medical Personnel System.

PubMed

Zagelbaum, Nicole K; Heslin, Kevin C; Stein, Judith A; Ruzek, Josef; Smith, Robert E; Nyugen, Tam; Dobalian, Aram

2014-07-19

The Disaster Emergency Medical Personnel System (DEMPS) program provides a system of volunteers whereby active or retired Department of Veterans Affairs (VA) personnel can register to be deployed to support other VA facilities or the nation during national emergencies or disasters. Both early and ongoing volunteer training is required to participate. This study aims to identify factors that impact willingness to deploy in the event of an emergency. This analysis was based on responses from 2,385 survey respondents (response rate, 29%). Latent variable path models were developed and tested using the EQS structural equations modeling program. Background demographic variables of education, age, minority ethnicity, and female gender were used as predictors of intervening latent variables of DEMPS Volunteer Experience, Positive Attitude about Training, and Stress. The model had acceptable fit statistics, and all three intermediate latent variables significantly predicted the outcome latent variable Readiness to Deploy. DEMPS Volunteer Experience and a Positive Attitude about Training were associated with Readiness to Deploy. Stress was associated with decreased Readiness to Deploy. Female gender was negatively correlated with Readiness to Deploy; however, there was an indirect relationship between female gender and Readiness to Deploy through Positive Attitude about Training. These findings suggest that volunteer emergency management response programs such as DEMPS should consider how best to address the factors that may make women less ready to deploy than men in order to ensure adequate gender representation among emergency responders. The findings underscore the importance of training opportunities to ensure that gender-sensitive support is a strong component of emergency response, and may apply to other emergency response programs such as the Medical Reserve Corps and the American Red Cross.

Deep and Structured Robust Information Theoretic Learning for Image Analysis.

PubMed

Deng, Yue; Bao, Feng; Deng, Xuesong; Wang, Ruiping; Kong, Youyong; Dai, Qionghai

2016-07-07

This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e. missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we respectively discuss three types of the RIT implementations with linear subspace embedding, deep transformation and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark datasets. The structured sparse RIT is further applied to a medical image analysis task for brain MRI segmentation that allows group-level feature selections on the brain tissues.

A proto-architecture for innate directionally selective visual maps.

PubMed

Adams, Samantha V; Harris, Chris M

2014-01-01

Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example robotics, where the ability to extract and learn features from the environment in an unsupervised way is important. In this computational study we explore a DS map that is already latent in a simple artificial network. This latent selectivity arises purely from the cortical architecture without any explicit coding for DS and prior to any self-organising process facilitated by spontaneous activity or training. We find DS maps with local patchy regions that exhibit features similar to maps derived experimentally and from previous modeling studies. We explore the consequences of changes to the afferent and lateral connectivity to establish the key features of this proto-architecture that support DS.

Restoration of fMRI Decodability Does Not Imply Latent Working Memory States

PubMed Central

Schneegans, Sebastian; Bays, Paul M.

2018-01-01

Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retrocueing. The simulation reproduces both behavioral and fMRI results previously taken as evidence for latent states, in particular the restoration of spatial reconstruction quality following an informative cue. Our results demonstrate that recovery of the decodability of an imaging signal does not provide compelling evidence for an activity-silent working memory state. PMID:28820674

Detecting trends in academic research from a citation network using network representation learning

PubMed Central

Mori, Junichiro; Ochi, Masanao; Sakata, Ichiro

2018-01-01

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. PMID:29782521

GTM-Based QSAR Models and Their Applicability Domains.

PubMed

Gaspar, H A; Baskin, I I; Marcou, G; Horvath, D; Varnek, A

2015-06-01

In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Health promotion in Swedish schools: school managers' views.

PubMed

Persson, Louise; Haraldsson, Katarina

2017-04-01

Schools are recognized worldwide as settings for health promotion, and leadership has a bearing on schools' ability to be health promoting. School managers have a great influence on what is prioritized in school, which in turn affects students' school performance and health. There is lack of research into school managers' views on health promotion, and what they consider to be central to health promotion. The aim was therefore to examine school managers' views about what health promotion in schools include. An explorative design, qualitative content analysis, was performed. In-depth interviews were conducted with all 13 school managers of a middle-sized municipality in central Sweden. The analysis had both manifest and latent content and three categories: 'Organization and Collaboration', 'Optimize the arena' and 'Strengthen the individual', and 10 subcategories emerged. The theme, 'Opportunities for learning and a good life', describes the latent content of these categories. Taking into account the views of school managers are important because these views help form a more complete picture of how school managers work with health promotion and what is needed to enhance health promotion to improve students' opportunities for learning and a good life. The Ottawa Charter for Health promotion is thereby transformed into practice. © The Author (2013). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A Gap-Filling Procedure for Hydrologic Data Based on Kalman Filtering and Expectation Maximization: Application to Data from the Wireless Sensor Networks of the Sierra Nevada

NASA Astrophysics Data System (ADS)

Coogan, A.; Avanzi, F.; Akella, R.; Conklin, M. H.; Bales, R. C.; Glaser, S. D.

2017-12-01

Automatic meteorological and snow stations provide large amounts of information at dense temporal resolution, but data quality is often compromised by noise and missing values. We present a new gap-filling and cleaning procedure for networks of these stations based on Kalman filtering and expectation maximization. Our method utilizes a multi-sensor, regime-switching Kalman filter to learn a latent process that captures dependencies between nearby stations and handles sharp changes in snowfall rate. Since the latent process is inferred using observations across working stations in the network, it can be used to fill in large data gaps for a malfunctioning station. The procedure was tested on meteorological and snow data from Wireless Sensor Networks (WSN) in the American River basin of the Sierra Nevada. Data include air temperature, relative humidity, and snow depth from dense networks of 10 to 12 stations within 1 km2 swaths. Both wet and dry water years have similar data issues. Data with artificially created gaps was used to quantify the method's performance. Our multi-sensor approach performs better than a single-sensor one, especially with large data gaps, as it learns and exploits the dominant underlying processes in snowpack at each site.

[Mammary gland tumor induction in rats by N-nitroso-N-methylurea and N-methyl-N1-nitro-N-nitrosoguanidine].

PubMed

Eliseev, V V; Vlasov, N N

1980-01-01

Cancer of the mammary gland was induced in female non-inbred rats under the local effect of N-nitroso-N-methylurea (NMU) and N-methyl-N-nitro-N-nitrosoguanidine (MNNG). During 10 weeks 2.5 mg of the substance in 0.2 ml of saline was injected in the region of the third mammary gland once a week. Under NMU exposure a primary tumor arose 3 months following the initiation of the experiment, the average latent period being 5.8 months, the incidence rate--76.7%. All tumors of this series were adenocarcinomas, in 5 cases there were noted sites of fibroadenomatosis with malignification along the tumor node margins. MNNG produced a primary tumor at the 7th month of the experiment, an average latent period--8.3 months, the incidence rate--56.7%. Tumors were mostly adenocarcinomas.

Modeling of Convective-Stratiform Precipitation Processes: Sensitivity to Partitioning Methods

NASA Technical Reports Server (NTRS)

Lang, S. E.; Tao, W.-K.; Simpson, J.; Ferrier, B.; Starr, David OC. (Technical Monitor)

2001-01-01

Six different convective-stratiform separation techniques, including a new technique that utilizes the ratio of vertical and terminal velocities, are compared and evaluated using two-dimensional numerical simulations of a tropical [Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA COARE)] and midlatitude continental [Preliminary Regional Experiment for STORM-Central (PRESTORM)] squall line. Comparisons are made in terms of rainfall, cloud coverage, mass fluxes, apparent heating and moistening, mean hydrometeor profiles, CFADs (Contoured Frequency with Altitude Diagrams), microphysics, and latent heating retrieval. Overall, it was found that the different separation techniques produced results that qualitatively agreed. However, the quantitative differences were significant. Observational comparisons were unable to conclusively evaluate the performance of the techniques. Latent heating retrieval was shown to be sensitive to the use of separation technique mainly due to the stratiform region for methods that found very little stratiform rain.

The Road to Creative Achievement: A Latent Variable Model of Ability and Personality Predictors

PubMed Central

Jauk, Emanuel; Benedek, Mathias; Neubauer, Aljoscha C

2014-01-01

This study investigated the significance of different well-established psychometric indicators of creativity for real-life creative outcomes. Specifically, we tested the effects of creative potential, intelligence, and openness to experiences on everyday creative activities and actual creative achievement. Using a heterogeneous sample of 297 adults, we performed latent multiple regression analyses by means of structural equation modelling. We found openness to experiences and two independent indicators of creative potential, ideational originality and ideational fluency, to predict everyday creative activities. Creative activities, in turn, predicted actual creative achievement. Intelligence was found to predict creative achievement, but not creative activities. Moreover, intelligence moderated the effect of creative activities on creative achievement, suggesting that intelligence may play an important role in transforming creative activities into publically acknowledged creative achievements. This study supports the view of creativity as a multifaceted construct and provides an integrative model illustrating the potential interplay between its different facets. PMID:24532953

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

PubMed

Pajak, Bozena; Fine, Alex B; Kleinschmidt, Dave F; Jaeger, T Florian

2016-12-01

We present a framework of second and additional language (L2/L n ) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/L n learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/L n acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/L n learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing

PubMed Central

Pajak, Bozena; Fine, Alex B.; Kleinschmidt, Dave F.; Jaeger, T. Florian

2015-01-01

We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa. PMID:28348442

Latent profiles of problem behavior within learning, peer, and teacher contexts: identifying subgroups of children at academic risk across the preschool year.

PubMed

Bulotsky-Shearer, Rebecca J; Bell, Elizabeth R; Domínguez, Ximena

2012-12-01

Employing a developmental and ecological model, the study identified initial levels and rates of change in academic skills for subgroups of preschool children exhibiting problem behavior within routine classroom situations. Six distinct latent profile types of emotional and behavioral adjustment were identified for a cohort of low-income children early in the preschool year (N=4417). Profile types provided a descriptive picture of patterns of classroom externalizing, internalizing, and situational adjustment problems common to subgroups of children early in the preschool year. The largest profile type included children who exhibited low problem behavior and were characterized as well-adjusted to the preschool classroom early in the year. The other profile types were characterized by distinct combinations of elevated internalizing, externalizing, and situational problem behavior. Multinomial logistic regression identified younger children and boys at increased risk for classification in problem types, relative to the well-adjusted type. Latent growth models indicated that children classified within the extremely socially and academically disengaged profile type, started and ended the year with the lowest academic skills, relative to all other types. Implications for future research, policy, and practice are discussed. Copyright © 2012 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

New Insights from Elucidating the Role of LMP1 in Nasopharyngeal Carcinoma

PubMed Central

Shair, Kathy H. Y.; Reddy, Akhil

2018-01-01

Latent membrane protein 1 (LMP1) is an Epstein-Barr virus (EBV) oncogenic protein that has no intrinsic enzymatic activity or sequence homology to cellular or viral proteins. The oncogenic potential of LMP1 has been ascribed to pleiotropic signaling properties initiated through protein-protein interactions in cytosolic membrane compartments, but the effects of LMP1 extend to nuclear and extracellular processes. Although LMP1 is one of the latent genes required for EBV-immortalization of B cells, the biology of LMP1 in the pathogenesis of the epithelial cancer nasopharyngeal carcinoma (NPC) is more complex. NPC is prevalent in specific regions of the world with high incidence in southeast China. The epidemiology and time interval from seroconversion to NPC onset in adults would suggest the involvement of multiple risk factors that complement the establishment of a latent and persistent EBV infection. The contribution of LMP1 to EBV pathogenesis in polarized epithelia has only recently begun to be elucidated. Furthermore, the LMP1 gene has emerged as one of the most divergent sequences in the EBV genome. This review will discuss the significance of recent advances in NPC research from elucidating LMP1 function in epithelial cells and lessons that could be learned from mining LMP1 sequence diversity. PMID:29561768

Do International Health Partnerships contribute to reverse innovation? a mixed methods study of THET-supported partnerships in the UK.

PubMed

Kulasabanathan, Kavian; Issa, Hamdi; Bhatti, Yasser; Prime, Matthew; Del Castillo, Jacqueline; Darzi, Ara; Harris, Matthew

2017-04-18

International health partnerships (IHPs) are changing, with an increased emphasis on mutual accountability and joint agenda setting for both the high- and the low- or middle-income country (LMIC) partners. There is now an important focus on the bi-directionality of learning however for the UK partners, this typically focuses on learning at the individual level, through personal and professional development. We sought to evaluate whether this learning also takes the shape of 'Reverse Innovation' -when an idea conceived in a low-income country is subsequently adopted in a higher-income country. This mixed methods study used an initial scoping survey of all the UK-leads of the Tropical Health Education Trust (THET)-supported International Health Partnerships (n = 114) to ascertain the extent to which the IHPs are or have been vehicles for Reverse Innovation. The survey formed the sampling frame for further deep-dive interviews to focus on volunteers' experiences and attitudes to learning from LMICs. Interviews of IHP leads (n = 12) were audio-recorded and transcribed verbatim. Survey data was analysed descriptively. Interview transcripts were coded thematically, using an inductive approach. Survey response rate was 27% (n = 34). The majority (70%) strongly agreed that supporting LMIC partners best described the mission of the partnership but only 13% of respondents strongly agreed that learning about new innovations and models was a primary mission of their partnership. Although more than half of respondents reported having observed innovative practice in the LMIC, only one IHP respondent indicated that this has led to Reverse Innovation. Interviews with a sample of survey respondents revealed themes primarily around how learning is conceptualised, but also a central power imbalance between the UK and LMIC partners. Paternalistic notions of knowledge could be traced to partnership power dynamics and latent attitudes to LMICs. Given the global flow of innovation, if High-income countries (HICs) are to benefit from LMIC practices, it is paramount to keep an open mind about where such learning can come from. Making the potential for learning more explicit and facilitating innovation dissemination upon return will ultimately underpin the success of adoption.

Polar low formation: ambient environments and the role of moisture

NASA Astrophysics Data System (ADS)

Terpstra, Annick; Spengler, Thomas; Michel, Clio; Moore, Richard

2016-04-01

Polar lows are maritime cyclones occurring during cold air outbreaks in high latitudes. Previous studies have shown that wind shear, baroclinicity, latent heat release, and surface fluxes are important factors during formation and intensification, yet their relative contributions and importance are still not fully understood. We use the ambient atmospheric conditions during polar low genesis to provide dynamical insights to the intensification and formation mechanisms for polar lows. We identify the characteristics of the ambient pre-polar low environment utilising an existing polar low database and ERA-Interim reanalysis data. Classification of these environments is based on the the direction between the thermal wind and the mean flow in the lower troposphere, where environments are classified as 'reverse shear' if the thermal wind and mean flow are in opposing directions and 'forward shear' if they are in the same direction. The two types of pre-polar low environments exhibit distinctly different features in terms of synoptic scale patterns, baroclinicity, configuration of the sea-surface temperature, as well as depth and stratification of the troposphere. These clear-cut differences hint at different dynamical pathways for the formation and intensification of polar lows for different shear environments. We also explore the role of latent heating during polar low formation utilising an idealised baroclinic channel model. The experimental design resembles a typical forward-shear moist-baroclinic environment at high-latitudes. Cyclogenesis is triggered by a weak, low-level thermal perturbation in hydrostatic and geostrophic balance. Our experiments show that significant disturbance growth is possible in absence of upper level forcing, surface fluxes, and radiation. The relative importance of diabatic versus baroclinic processes for the generation of eddy available potential energy is used to differentiate between the dynamical processes contributing to disturbance growth. The experiments indicate that sufficient latent heat release in the north-eastern quadrant of the cyclone is crucial for rapid disturbance intensification, where environmental relative humidity, baroclinicity, and static stability modulate the relative importance of latent heat release. Furthermore, the relative shallowness of the perturbation at high-latitudes increases the effectiveness of latent heat release on cyclone amplification.

Instructor and peer bullying in college students: Distinct typologies based on Latent Class Analysis.

PubMed

Marraccini, Marisa E; Brick, Leslie Ann D; Weyandt, Lisa L

2018-03-22

Although bullying is traditionally considered within the context of primary and secondary school, recent evidence suggests that bullying continues into college and workplace settings. Participants/Method: Latent class analysis (LCA) was employed to classify college bullying involvement typologies among 325 college students attending a northeastern university. Four classes concerning bullying involvement were revealed: Non-involved (36%); Instructor victim (30%); Peer bully-victim (22%); and Peer bully-victim/ Instructor victim (12%). Findings from this study, which classified college bullying experiences by incorporating both peer and instructor (teacher and professor) bullying, add substantially to the literature by providing insight into patterns of relatively unexplored bullying behaviors.

Amatchmethod Based on Latent Semantic Analysis for Earthquakehazard Emergency Plan

NASA Astrophysics Data System (ADS)

Sun, D.; Zhao, S.; Zhang, Z.; Shi, X.

2017-09-01

The structure of the emergency plan on earthquake is complex, and it's difficult for decision maker to make a decision in a short time. To solve the problem, this paper presents a match method based on Latent Semantic Analysis (LSA). After the word segmentation preprocessing of emergency plan, we carry out keywords extraction according to the part-of-speech and the frequency of words. Then through LSA, we map the documents and query information to the semantic space, and calculate the correlation of documents and queries by the relation between vectors. The experiments results indicate that the LSA can improve the accuracy of emergency plan retrieval efficiently.

Multiplex mass spectrometry imaging for latent fingerprints.

PubMed

Yagnik, Gargey B; Korte, Andrew R; Lee, Young Jin

2013-01-01

We have previously developed in-parallel data acquisition of orbitrap mass spectrometry (MS) and ion trap MS and/or MS/MS scans for matrix-assisted laser desorption/ionization MS imaging (MSI) to obtain rich chemical information in less data acquisition time. In the present study, we demonstrate a novel application of this multiplex MSI methodology for latent fingerprints. In a single imaging experiment, we could obtain chemical images of various endogenous and exogenous compounds, along with simultaneous MS/MS images of a few selected compounds. This work confirms the usefulness of multiplex MSI to explore chemical markers when the sample specimen is very limited. Copyright © 2013 John Wiley & Sons, Ltd.

Perfectionism and longitudinal patterns of stress for STEM majors: Implications for academic performance.

PubMed

Rice, Kenneth G; Ray, Merideth E; Davis, Don E; DeBlaere, Cirleen; Ashby, Jeffrey S

2015-10-01

Complementary hypotheses suggest that perfectionism may (a) cause later stress (stress generation) and (b) moderate the effects of stress on subsequent outcomes (stress enhancement). The present study tested these hypotheses with a sample of 432 first-time college freshmen pursuing science, technology, engineering, and math (STEM) majors. Students completed baseline perfectionism scales and repeated measures for perceived academic stress at monthly intervals 3 times in the fall semester and 3 times in the spring semester. Course grade data from institutional records were used to calculate first-year STEM grade point average (GPA) as the distal outcome in analyses. Gender, high school GPA, SAT math scores, and university were covariates. Latent profile analyses supported adaptive, maladaptive, and nonperfectionist classes and latent class growth mixture models identified distinctly low, moderate, and high patterns of academic stress over the year. Latent transition analyses indicated that maladaptive perfectionists were likely to experience moderate or high stress (none transitioned to low stress), and adaptive perfectionists were likely to have low or moderate stress (only 4% transitioned to high stress). Women were substantially more likely than male maladaptive perfectionists to experience high stress. Low-stressed adaptive perfectionists followed by moderately stressed maladaptive perfectionists had relatively higher GPAs than other groups. Subgroups of perfectionists who transitioned to the next higher stress level had substantially lower GPAs than other groups. Overall, results were consistent with stress-generation and stress-enhancement hypotheses regarding perfectionists. Findings suggested implications for prevention and intervention with perfectionistic STEM students that should be implemented early in their college experience. (c) 2015 APA, all rights reserved).

Paternal Mental Health Trajectory Classes and Early Fathering Experiences: Prospective Study on a Normative and Formerly Infertile Sample

ERIC Educational Resources Information Center

Vänskä, Mervi; Punamäki, Raija-Leena; Tolvanen, Asko; Lindblom, Jallu; Flykt, Marjo; Unkila-Kallio, Leila; Tulppala, Maija; Tiitinen, Aila

2017-01-01

A father's mental health is important for family well-being, but research is scarce on paternal symptoms during the transition to fatherhood. This study identified fathers' latent mental health trajectory classes from the pre- to postnatal period and examined their associations with early fathering experiences. It further analysed, whether a…

Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization

PubMed Central

Masino, Aaron J.

2016-01-01

Commonly referred to as predictive modeling, the use of machine learning and statistical methods to improve healthcare outcomes has recently gained traction in biomedical informatics research. Given the vast opportunities enabled by large Electronic Health Records (EHR) data and powerful resources for conducting predictive modeling, we argue that it is yet crucial to first carefully examine the prediction task and then choose predictive methods accordingly. Specifically, we argue that there are at least three distinct prediction tasks that are often conflated in biomedical research: 1) data imputation, where a model fills in the missing values in a dataset, 2) future forecasting, where a model projects the development of a medical condition for a known patient based on existing observations, and 3) new-patient generalization, where a model transfers the knowledge learned from previously observed patients to newly encountered ones. Importantly, the latter two tasks—future forecasting and new-patient generalizations—tend to be more difficult than data imputation as they require predictions to be made on potentially out-of-sample data (i.e., data following a different predictable pattern from what has been learned by the model). Using hearing loss progression as an example, we investigate three regression models and show that the modeling of latent clusters is a robust method for addressing the more challenging prediction scenarios. Overall, our findings suggest that there exist significant differences between various kinds of prediction tasks and that it is important to evaluate the merits of a predictive model relative to the specific purpose of a prediction task. PMID:27636203

Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization.

PubMed

Qian, Ting; Masino, Aaron J

2016-01-01

Commonly referred to as predictive modeling, the use of machine learning and statistical methods to improve healthcare outcomes has recently gained traction in biomedical informatics research. Given the vast opportunities enabled by large Electronic Health Records (EHR) data and powerful resources for conducting predictive modeling, we argue that it is yet crucial to first carefully examine the prediction task and then choose predictive methods accordingly. Specifically, we argue that there are at least three distinct prediction tasks that are often conflated in biomedical research: 1) data imputation, where a model fills in the missing values in a dataset, 2) future forecasting, where a model projects the development of a medical condition for a known patient based on existing observations, and 3) new-patient generalization, where a model transfers the knowledge learned from previously observed patients to newly encountered ones. Importantly, the latter two tasks-future forecasting and new-patient generalizations-tend to be more difficult than data imputation as they require predictions to be made on potentially out-of-sample data (i.e., data following a different predictable pattern from what has been learned by the model). Using hearing loss progression as an example, we investigate three regression models and show that the modeling of latent clusters is a robust method for addressing the more challenging prediction scenarios. Overall, our findings suggest that there exist significant differences between various kinds of prediction tasks and that it is important to evaluate the merits of a predictive model relative to the specific purpose of a prediction task.

School-readiness profiles of children with language impairment: linkages to home and classroom experiences.

PubMed

Pentimonti, Jill M; Justice, Laura M; Kaderavek, Joan N

2014-01-01

This study represents an effort to advance our understanding of the nature of school readiness among children with language impairment (LI), a population of children acknowledged to be at risk of poor academic achievement. The academic, social-emotional, and behavioural competencies with which children arrive at kindergarten affect the nature of their future educational experiences, and their overall academic achievement. To examine whether there are reliable profiles that characterize children with LI just prior to kindergarten entrance, and the extent to which profile membership is associated with characteristics of children's homes and preschool experiences. Questions addressed were twofold: (1) To what extent are there reliable profiles of children with LI with respect to their school readiness? (2) To what extent is children's profile membership associated with characteristics of their homes and preschool classrooms? Participants were 136 children with LI from early childhood special education classrooms. We utilized latent class analysis (LCA) to classify individuals into profiles based on individual responses on school readiness measures. We then used multilevel hierarchical generalized linear models to examine the relations between profile membership and children's home/classroom experiences. LCA analyses revealed that a four-profile solution was the most appropriate fit for the data and that classroom experiences were predictive of these profiles, such that children in classrooms with more instructional/emotional support were more likely to be placed in profiles characterized by higher school readiness skills. These results suggest that the school readiness profiles of young children with LI are associated with the quality of children's classroom experiences, and that high-quality classroom experiences can be influential for ensuring that young children with LI arrive in kindergarten ready to learn. © 2014 Royal College of Speech and Language Therapists.

Individual differences in early adolescents' latent trait cortisol (LTC): Relation to early adversity.

PubMed

Stroud, Catherine B; Chen, Frances R; Doane, Leah D; Granger, Douglas A

2016-09-01

Substantial evidence suggests that youth who experience early adversity exhibit alterations in hypothalamic pituitary adrenal (HPA) axis functioning, thereby increasing risk for negative health outcomes. However, few studies have explored whether early adversity alters enduring trait indicators of HPA axis activity. Using objective contextual stress interviews with adolescents and their mothers to assess early adversity, we examined the cumulative impact of nine types of early adversity on early adolescents girls' latent trait cortisol (LTC). Adolescents (n = 122; M age = 12.39 years) provided salivary cortisol samples three times a day (waking, 30 min post-waking, and bedtime) over 3 days. Latent state-trait modeling indicated that the waking and 30 min post-waking samples contributed to a LTC factor. Moreover, greater early adversity was associated with a lower LTC level. Implications of LTC for future research examining the impact of early adversity on HPA axis functioning are discussed. © 2016 Wiley Periodicals, Inc. Dev Psychobiol 58:700-713, 2016. © 2016 Wiley Periodicals, Inc.

Segmentation of Image Ensembles via Latent Atlases

PubMed Central

Van Leemput, Koen; Menze, Bjoern H.; Wells, William M.; Golland, Polina

2010-01-01

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a method for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, a latent atlas, initialized by at most a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The algorithm is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method on two datasets, segmenting subcortical and cortical structures in a multi-subject study and extracting brain tumors in a single-subject multi-modal longitudinal experiment. We compare the segmentation results to manual segmentations, when those exist, and to the results of a state-of-the-art atlas-based segmentation method. The quality of the results supports the latent atlas as a promising alternative when existing atlases are not compatible with the images to be segmented. PMID:20580305

The Influence of Static and Dynamic Intrapersonal Factors on Longitudinal Patterns of Peer Victimization through Mid-adolescence: a Latent Transition Analysis.

PubMed

Haltigan, John D; Vaillancourt, Tracy

2018-01-01

Using 6 cycles (grade 5 through grade 10) of data obtained from a large prospective sample of Canadian school children (N = 700; 52.6% girls), we replicated previous findings concerning the empirical definition of peer victimization (i.e., being bullied) and examined static and dynamic intrapersonal factors associated with its emergence and experiential continuity through mid-adolescence. Latent class analyses consistently revealed a low victimization and an elevated victimization class across time, supporting previous work suggesting peer victimization was defined by degree rather than by type (e.g., physical). Using latent transition analyses (LTA), we found that child sex, parent-perceived pubertal development, and internalizing symptoms influenced the probability of transitioning from the low to the elevated victimization class across time. Higher-order extensions within the LTA modeling framework revealed a lasting effect of grade 5 victimization status on grade 10 victimization status and a large effect of chronic victimization on later parent-reported youth internalizing symptoms (net of prior parent-reported internalizing symptoms) in later adolescence (grade 11). Implications of the current findings for the experience of peer victimization, as well as the application of latent transition analysis as a useful approach for peer victimization research, are discussed.

The Estimation of Surface Latent Heat Flux over the Ocean and its Relationship to Marine Atmospheric Boundary Layer (MABL) Structure

NASA Technical Reports Server (NTRS)

Palm, Stephen P.; Schwemmer, Geary K.; Vandemark, Doug; Evans, Keith; Miller, David O.; Demoz, Belay B.; Starr, David OC. (Technical Monitor)

2001-01-01

A new technique combining active and passive remote sensing instruments for the estimation of surface latent heat flux over the ocean is presented. This synergistic method utilizes aerosol lidar backscatter data, multi-channel infrared radiometer data, and microwave scatterometer data acquired onboard the NASA P-313 research aircraft during an extended field campaign over the Atlantic ocean in support of the Lidar In-space Technology Experiment (LITE) in September of 1994. The 10 meter wind speed derived from scatterometers and lidar-radiometer inferred near-surface moisture are used to obtain an estimate of the surface flux of moisture via a bulk aerodynamic formula. The results are compared with the Special Sensor Microwave Imager (SSM/I) daily average latent heat flux and show reasonable agreement. However, the SSM/I values are biased low by about 15 W/sq m. In addition, the Marine Atmospheric Boundary Layer (MABL) height, entrainment zone thickness and integrated lidar backscatter intensity are computed from the lidar data and compared with the magnitude of the surface fluxes. The results show that the surface latent heat flux is most strongly correlated with entrainment zone depth, MABL height and the integrated MABL lidar backscatter, with corresponding correlation coefficients of 0.39, 0.43 and 0.71, respectively.

Latency and Persistence of 'Candidatus Liberibacter asiaticus' in Its Psyllid Vector, Diaphorina citri (Hemiptera: Liviidae).

PubMed

Canale, Maria Cristina; Tomaseto, Arthur Fernando; Haddad, Marineia de Lara; Della Coletta-Filho, Helvécio; Lopes, João Roberto Spotti

2017-03-01

Although 'Candidatus Liberibacter asiaticus' (Las) is a major pathogen associated with citrus huanglongbing (HLB), some characteristics of transmission by the psyllid vector Diaphorina citri are not fully understood. We examined the latent period and persistence of transmission of Las by D. citri in a series of experiments at 25°C, in which third-instar psyllid nymphs and 1-week-old adults were confined on infected citrus for an acquisition access period (AAP), and submitted to sequential inoculation access periods (IAPs) on healthy citrus seedlings. The median latent period (LP 50 , i.e., acquisition time after which 50% of the individuals can inoculate) of 16.8 and 17.8 days for psyllids that acquired Las as nymphs and adults, respectively, was determined by transferring single individuals in 48-h IAPs. Inoculation events were intermittent and randomly distributed over the IAPs, but were more frequent after acquisition by nymphs. A minimum latent period of 7 to 10 days was observed by transferring groups of 10 psyllids in 48-h IAPs, after a 96-h AAP by nymphs. Psyllids transmitted for up to 5 weeks, when submitted to sequential 1-week IAPs after a 14-day AAP as nymphs. The long latent period and persistence of transmission are indirect evidences of circulative propagation of Las in D. citri.

The Estimation of Surface Latent Heat Flux Over the Ocean and its Relationship to Marine Atmospheric Boundary Layer (MABL) Structure

NASA Technical Reports Server (NTRS)

Palm, Stephen P.; Miller, David O.; Schwemmer, Geary

2000-01-01

A new technique combining active and passive remote sensing instruments for the estimation of surface latent heat flux over the ocean is presented. This synergistic method uses aerosol lidar backscatter data, multi-channel infrared radiometer data and microwave scatterometer data acquired onboard the NASA P-3B research aircraft during an extended field campaign over the Atlantic ocean in support of the Lidar In-space Technology Experiment (LITE) in September of 1994. The 10 meter wind speed derived from the scatterometers and the lidar-radiometer inferred near-surface moisture are used to obtain an estimate of the surface flux of moisture via bulk aerodynamic formulae. The results are compared with the Special Sensor Microwave Imager (SSM/I) daily average latent heat flux and show reasonable agreement with an rms error and bias of about 50 and 25 W per square meters, respectively. In addition, the MABL height, entrainment zone thickness and integrated lidar backscatter intensity are computed from the lidar data and compared with the magnitude of the surface fluxes. The results show that the surface latent heat flux is most strongly correlated with entrainment zone top, bottom and the integrated MABL lidar backscatter, with corresponding correlation coefficients of 0.62, 0.67 and 0.61, respectively.

Quality of life and patient preferences: identification of subgroups of multiple sclerosis patients.

PubMed

Rosato, Rosalba; Testa, Silvia; Oggero, Alessandra; Molinengo, Giorgia; Bertolotto, Antonio

2015-09-01

The aim of this study was to estimate preferences related to quality of life attributes in people with multiple sclerosis, by keeping heterogeneity of patient preference in mind, using the latent class approach. A discrete choice experiment survey was developed using the following attributes: activities of daily living, instrumental activities of daily living, pain/fatigue, anxiety/depression and attention/concentration. Choice sets were presented as pairs of hypothetical health status, based upon a fractional factorial design. The latent class logit model estimated on 152 patients identified three subpopulations, which, respectively, attached more importance to: (1) the physical dimension; (2) pain/fatigue and anxiety/depression; and (3) instrumental activities of daily living impairments, anxiety/depression and attention/concentration. A posterior analysis suggests that the latent class membership may be related to an individual's age to some extent, or to diagnosis and treatment, while apart from energy dimension, no significant difference exists between latent groups, with regard to Multiple Sclerosis Quality of Life-54 scales. A quality of life preference-based utility measure for people with multiple sclerosis was developed. These utility values allow identification of a hierarchic priority among different aspects of quality of life and may allow physicians to develop a care programme tailored to patient needs.

When are Overcomplete Representations Identifiable? Uniqueness of Tensor Decompositions Under Expansion Constraints

DTIC Science & Technology

2013-06-16

Science Dept., University of California, Irvine, USA 92697. Email : a.anandkumar@uci.edu,mjanzami@uci.edu. Daniel Hsu and Sham Kakade are with...Microsoft Research New England, 1 Memorial Drive, Cambridge, MA 02142. Email : dahsu@microsoft.com, skakade@microsoft.com 1 a latent space dimensionality...Sparse coding for multitask and transfer learning. ArxXiv preprint, abs/1209.0738, 2012. [34] G.H. Golub and C.F. Van Loan. Matrix Computations. The

The Relationships between Perceived Parenting Style, Learning Motivation, Friendship Satisfaction, and the Addictive Use of Smartphones with Elementary School Students of South Korea: Using Multivariate Latent Growth Modeling

ERIC Educational Resources Information Center

Bae, Sung Man

2015-01-01

We examined how perceived parenting style, friendship satisfaction, and academic motivation influence the addictive use of smartphones longitudinally. We utilized the panel data (from 2010-2012) of Korean children and youth panel survey of the National Youth Policy Institute. Data were collected from 2,376 individuals in the first year (boys:…

Grounding cognitive control in associative learning.

PubMed

Abrahamse, Elger; Braem, Senne; Notebaert, Wim; Verguts, Tom

2016-07-01

Cognitive control covers a broad range of cognitive functions, but its research and theories typically remain tied to a single domain. Here we outline and review an associative learning perspective on cognitive control in which control emerges from associative networks containing perceptual, motor, and goal representations. Our review identifies 3 trending research themes that are shared between the domains of conflict adaptation, task switching, response inhibition, and attentional control: Cognitive control is context-specific, can operate in the absence of awareness, and is modulated by reward. As these research themes can be envisaged as key characteristics of learning, we propose that their joint emergence across domains is not coincidental but rather reflects a (latent) growth of interest in learning-based control. Associative learning has the potential for providing broad-scaled integration to cognitive control theory, and offers a promising avenue for understanding cognitive control as a self-regulating system without postulating an ill-defined set of homunculi. We discuss novel predictions, theoretical implications, and immediate challenges that accompany an associative learning perspective on cognitive control. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Prediction of hemoglobin in blood donors using a latent class mixed-effects transition model.

PubMed

Nasserinejad, Kazem; van Rosmalen, Joost; de Kort, Wim; Rizopoulos, Dimitris; Lesaffre, Emmanuel

2016-02-20

Blood donors experience a temporary reduction in their hemoglobin (Hb) value after donation. At each visit, the Hb value is measured, and a too low Hb value leads to a deferral for donation. Because of the recovery process after each donation as well as state dependence and unobserved heterogeneity, longitudinal data of Hb values of blood donors provide unique statistical challenges. To estimate the shape and duration of the recovery process and to predict future Hb values, we employed three models for the Hb value: (i) a mixed-effects models; (ii) a latent-class mixed-effects model; and (iii) a latent-class mixed-effects transition model. In each model, a flexible function was used to model the recovery process after donation. The latent classes identify groups of donors with fast or slow recovery times and donors whose recovery time increases with the number of donations. The transition effect accounts for possible state dependence in the observed data. All models were estimated in a Bayesian way, using data of new entrant donors from the Donor InSight study. Informative priors were used for parameters of the recovery process that were not identified using the observed data, based on results from the clinical literature. The results show that the latent-class mixed-effects transition model fits the data best, which illustrates the importance of modeling state dependence, unobserved heterogeneity, and the recovery process after donation. The estimated recovery time is much longer than the current minimum interval between donations, suggesting that an increase of this interval may be warranted. Copyright © 2015 John Wiley & Sons, Ltd.

Symptom Trajectories in Children Receiving Treatment for Leukemia: A Latent Class Growth Analysis With Multitrajectory Modeling.

PubMed

Hockenberry, Marilyn J; Hooke, Mary C; Rodgers, Cheryl; Taylor, Olga; Koerner, Kari M; Mitby, Pauline; Moore, Ida; Scheurer, Michael E; Pan, Wei

2017-07-01

Cancer treatment symptoms play a major role in determining the health of children with cancer. Symptom toxicity often results in complications, treatment delays, and therapy dose reductions that can compromise leukemia therapy and jeopardize chances for long-term survival. Critical to understanding symptom experiences during treatment is the need for exploration of "why" inter-individual symptom differences occur; this will determine who may be most susceptible to treatment toxicities. This study examined specific symptom trajectories during the first 18 months of childhood leukemia treatment. Symptom measures included fatigue, sleep disturbances, pain, nausea, and depression. Symptom trajectories of 236 children with leukemia three to 18 years old were explored prospectively over four periods: initiation of post-induction therapy, four and eight post-induction therapy, and the last time point was at the beginning of maintenance/continuation therapy. Latent class growth analysis was used to classify patients into distinctive groups with similar symptom trajectories based on patients' response patterns on the symptom measures over time. Three latent classes of symptom trajectories were identified and classified into mild, moderate, and severe symptom trajectories. The only demographic characteristic with a significant relationship to membership in the latent class symptom trajectories was race/ethnicity. All other demographic characteristics including leukemia risk levels showed no significant relationships. This study is unique in that groups of patients with similar symptoms were identified rather than groups of symptoms. Further research using latent class growth analysis is needed. Copyright © 2017 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Re-examining the roles of surface heat flux and latent heat release in a "hurricane-like" polar low over the Barents Sea

NASA Astrophysics Data System (ADS)

Kolstad, Erik W.; Bracegirdle, Thomas J.; Zahn, Matthias

2016-07-01

Polar lows are intense mesoscale cyclones that occur at high latitudes in both hemispheres during winter. Their sometimes evidently convective nature, fueled by strong surface fluxes and with cloud-free centers, have led to some polar lows being referred to as "arctic hurricanes." Idealized studies have shown that intensification by hurricane development mechanisms is theoretically possible in polar winter atmospheres, but the lack of observations and realistic simulations of actual polar lows have made it difficult to ascertain if this occurs in reality. Here the roles of surface heat fluxes and latent heat release in the development of a Barents Sea polar low, which in its cloud structures showed some similarities to hurricanes, are studied with an ensemble of sensitivity experiments, where latent heating and/or surface fluxes of sensible and latent heat were switched off before the polar low peaked in intensity. To ensure that the polar lows in the sensitivity runs did not track too far away from the actual environmental conditions, a technique known as spectral nudging was applied. This was shown to be crucial for enabling comparisons between the different model runs. The results presented here show that (1) no intensification occurred during the mature, postbaroclinic stage of the simulated polar low; (2) surface heat fluxes, i.e., air-sea interaction, were crucial processes both in order to attain the polar low's peak intensity during the baroclinic stage and to maintain its strength in the mature stage; and (3) latent heat release played a less important role than surface fluxes in both stages.

Exploring MEDLINE Space with Random Indexing and Pathfinder Networks

PubMed Central

Cohen, Trevor

2008-01-01

The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search. PMID:18999236

Exploring MEDLINE space with random indexing and pathfinder networks.

PubMed

Cohen, Trevor

2008-11-06

The integration of disparate research domains is a prerequisite for the success of the translational science initiative. MEDLINE abstracts contain content from a broad range of disciplines, presenting an opportunity for the development of methods able to integrate the knowledge they contain. Latent Semantic Analysis (LSA) and related methods learn human-like associations between terms from unannotated text. However, their computational and memory demands limits their ability to address a corpus of this size. Furthermore, visualization methods previously used in conjunction with LSA have limited ability to define the local structure of the associative networks LSA learns. This paper explores these issues by (1) processing the entire MEDLINE corpus using Random Indexing, a variant of LSA, and (2) exploring learned associations using Pathfinder Networks. Meaningful associations are inferred from MEDLINE, including a drug-disease association undetected by PUBMED search.

Rotation invariant deep binary hashing for fast image retrieval

NASA Astrophysics Data System (ADS)

Dai, Lai; Liu, Jianming; Jiang, Aiwen

2017-07-01

In this paper, we study how to compactly represent image's characteristics for fast image retrieval. We propose supervised rotation invariant compact discriminative binary descriptors through combining convolutional neural network with hashing. In the proposed network, binary codes are learned by employing a hidden layer for representing latent concepts that dominate on class labels. A loss function is proposed to minimize the difference between binary descriptors that describe reference image and the rotated one. Compared with some other supervised methods, the proposed network doesn't have to require pair-wised inputs for binary code learning. Experimental results show that our method is effective and achieves state-of-the-art results on the CIFAR-10 and MNIST datasets.

Latent toxoplasmosis is associated with neurocognitive impairment in young adults with and without chronic HIV infection.

PubMed

Ene, L; Marcotte, T D; Umlauf, A; Grancea, C; Temereanca, A; Bharti, A; Achim, C L; Letendre, S; Ruta, S M

2016-10-15

We evaluated the impact of latent toxoplasmosis (LT) on neurocognitive (NC) and neurobehavioural functioning in young adults with and without chronic HIV infection, using a standardised NC test battery, self-reported Beck Depression Inventory, Frontal System Behavior Scale, MINI-International Neuropsychiatric Interview and risk-assessment battery. 194 young adults (median age 24years, 48.2% males) with chronic HIV infection (HIV+) since childhood and 51 HIV seronegative (HIV-) participants were included. HIV+ individuals had good current immunological status (median CD4: 479 cells/μl) despite a low CD4 nadir (median: 93 cells/μl). LT (positive anti-Toxoplasma IgG antibodies) was present in one third of participants. The impairment rates in the HIV- with and without Toxo were not significantly different (p=0.17). However, we observed an increasing trend (p<0.001) in impairment rates with HIV and LT status: HIV-/LT- (6.1%); HIV-/LT+ (22%), HIV+/LT- (31%), HIV+/LT+ (49%). In a multivariable analysis using the entire study group there were main effects on cognition for HIV and also for LT. Within the HIV+ group LT was associated with worse performance globally (p=0.006), in memory (p=0.009), speed of information processing (p=0.01), verbal (p=0.02) and learning (p=0.02) domains. LT was not associated with depressive symptoms, frontal systems dysfunction or risk behaviors in any of the groups. HIV participants with lower Toxoplasma antibody concentration had worse NC performance, with higher GDS values (p=0.03) and worse learning (p=0.002), memory (p=0.006), speed of information processing (p=0.01) T scores. Latent Toxoplasmosis may contribute to NC impairment in young adults, including those with and without chronic HIV infection. Copyright © 2016 Elsevier B.V. All rights reserved.

Latent Class Analysis of Early Developmental Trajectory in Baby Siblings of Children with Autism

PubMed Central

Landa, Rebecca J.; Gross, Alden L.; Stuart, Elizabeth A.; Bauman, Margaret

2012-01-01

Background Siblings of children with autism (sibs-A) are at increased genetic risk for autism spectrum disorders (ASD) and milder impairments. To elucidate diversity and contour of early developmental trajectories exhibited by sibs-A, regardless of diagnostic classification, latent class modeling was used. Methods Sibs-A (n=204) were assessed with the Mullen Scales of Early Learning from age 6–36 months. Mullen T scores served as dependent variables. Outcome classifications at age 36 months included: ASD (n=52); non-ASD social/communication delay (broader autism phenotype; BAP) (n=31); and unaffected (n=121). Child-specific patterns of performance were studied using latent class growth analysis. Latent class membership was then related to diagnostic outcome through estimation of within-class proportions of children assigned to each diagnostic classification. Results A 4-class model was favored. Class 1 represented accelerated development and consisted of 25.7% of the sample, primarily unaffected children. Class 2 (40.0% of the sample), was characterized by normative development with above-average nonverbal cognitive outcome. Class 3 (22.3% of the sample) was characterized by receptive language, and gross and fine motor delay. Class 4 (12.0% of the sample), was characterized by widespread delayed skill acquisition, reflected by declining trajectories. Children with an outcome diagnosis of ASD were spread across Classes 2, 3, and 4. Conclusions Results support a category of ASD that involves slowing in early non-social development. Receptive language and motor development is vulnerable to early delay in sibs-A with and without ASD outcomes. Non-ASD sibs-A are largely distributed across classes depicting average or accelerated development. Developmental trajectories of motor, language, and cognition appear independent of communication and social delays in non-ASD sibs-A. PMID:22574686

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

DOE PAGES

Tramontana, Gianluca; Jung, Martin; Schwalm, Christopher R.; ...

2016-07-29

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data andmore » (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange ( R 2 < 0.5), ecosystem respiration ( R 2 > 0.6), gross primary production ( R 2> 0.7), latent heat ( R 2 > 0.7), sensible heat ( R 2 > 0.7), and net radiation ( R 2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well ( R 2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted ( R 2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). Finally, the evaluated large ensemble of ML-based models will be the basis of new global flux products.« less

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

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

Tramontana, Gianluca; Jung, Martin; Schwalm, Christopher R.

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data andmore » (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange ( R 2 < 0.5), ecosystem respiration ( R 2 > 0.6), gross primary production ( R 2> 0.7), latent heat ( R 2 > 0.7), sensible heat ( R 2 > 0.7), and net radiation ( R 2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well ( R 2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted ( R 2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). Finally, the evaluated large ensemble of ML-based models will be the basis of new global flux products.« less

The Curiosity and Exploration Inventory-II: Development, Factor Structure, and Psychometrics

PubMed Central

Kashdan, Todd B.; Gallagher, Matthew W.; Silvia, Paul J.; Winterstein, Beate P.; Breen, William E.; Terhar, Daniel; Steger, Michael F.

2009-01-01

Given curiosity’s fundamental role in motivation, learning, and well-being, we sought to refine the measurement of trait curiosity with an improved version of the Curiosity and Exploration Inventory (CEI; Kashdan, Rose, & Fincham, 2004). A preliminary pool of 36 items was administered to 311 undergraduate students, who also completed measures of emotion, emotion regulation, personality, and well-being. Factor analyses indicated a two factor model—motivation to seek out knowledge and new experiences (Stretching; 5 items) and a willingness to embrace the novel, uncertain, and unpredictable nature of everyday life (Embracing; 5 items). In two additional samples (ns = 150 and 119), we cross-validated this factor structure and provided initial evidence for construct validity. This includes positive correlations with personal growth, openness to experience, autonomy, purpose in life, self-acceptance, psychological flexibility, positive affect, and positive social relations, among others. Applying item response theory (IRT) to these samples (n = 578), we showed that the items have good discrimination and a desirable breadth of difficulty. The item information functions and test information function were centered near zero, indicating that the scale assesses the mid-range of the latent curiosity trait most reliably. The findings thus far provide good evidence for the psychometric properties of the 10-item CEI-II. PMID:20160913

Joint Attributes and Event Analysis for Multimedia Event Detection.

PubMed

Ma, Zhigang; Chang, Xiaojun; Xu, Zhongwen; Sebe, Nicu; Hauptmann, Alexander G

2017-06-15

Semantic attributes have been increasingly used the past few years for multimedia event detection (MED) with promising results. The motivation is that multimedia events generally consist of lower level components such as objects, scenes, and actions. By characterizing multimedia event videos with semantic attributes, one could exploit more informative cues for improved detection results. Much existing work obtains semantic attributes from images, which may be suboptimal for video analysis since these image-inferred attributes do not carry dynamic information that is essential for videos. To address this issue, we propose to learn semantic attributes from external videos using their semantic labels. We name them video attributes in this paper. In contrast with multimedia event videos, these external videos depict lower level contents such as objects, scenes, and actions. To harness video attributes, we propose an algorithm established on a correlation vector that correlates them to a target event. Consequently, we could incorporate video attributes latently as extra information into the event detector learnt from multimedia event videos in a joint framework. To validate our method, we perform experiments on the real-world large-scale TRECVID MED 2013 and 2014 data sets and compare our method with several state-of-the-art algorithms. The experiments show that our method is advantageous for MED.

Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.

PubMed

Whiteway, Matthew R; Butts, Daniel A

2017-03-01

The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not "noise" and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end. NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are related to behavioral contexts outside of direct experimental control. Copyright © 2017 the American Physiological Society.

Expectation-maximization of the potential of mean force and diffusion coefficient in Langevin dynamics from single molecule FRET data photon by photon.

PubMed

Haas, Kevin R; Yang, Haw; Chu, Jhih-Wei

2013-12-12

The dynamics of a protein along a well-defined coordinate can be formally projected onto the form of an overdamped Lagevin equation. Here, we present a comprehensive statistical-learning framework for simultaneously quantifying the deterministic force (the potential of mean force, PMF) and the stochastic force (characterized by the diffusion coefficient, D) from single-molecule Förster-type resonance energy transfer (smFRET) experiments. The likelihood functional of the Langevin parameters, PMF and D, is expressed by a path integral of the latent smFRET distance that follows Langevin dynamics and realized by the donor and the acceptor photon emissions. The solution is made possible by an eigen decomposition of the time-symmetrized form of the corresponding Fokker-Planck equation coupled with photon statistics. To extract the Langevin parameters from photon arrival time data, we advance the expectation-maximization algorithm in statistical learning, originally developed for and mostly used in discrete-state systems, to a general form in the continuous space that allows for a variational calculus on the continuous PMF function. We also introduce the regularization of the solution space in this Bayesian inference based on a maximum trajectory-entropy principle. We use a highly nontrivial example with realistically simulated smFRET data to illustrate the application of this new method.

Evolution of an Intense Warm Frontal Precipitation Band During the GPM Cold-Season Precipitation Experiment (GCPEx)

NASA Technical Reports Server (NTRS)

Colle, Brian A.; Naeger, Aaron R.; Molthan, Andrew

2017-01-01

The band developed with low-level deformation and frontogenesis along the sloping warm frontal zone, and the vertical motions became large enough to produce graupel on the south side of the band. Embedded convective cells developed earlier in our GCPEx event, but the frontogenesis was weak then and banding was limited. As the deformation increased the stability also increased near the banding location (MPV* 0), which favored the development of single band. Through sensitivity studies (not shown) we found that latent heating helps increase the frontal circulations and resulting band development. Latent cooling also helps increase the frontogenesis given the evaporative and sublimation cooling within the frontal precipitation.

Data on the interexaminer variation of minutia markup on latent fingerprints.

PubMed

Ulery, Bradford T; Hicklin, R Austin; Roberts, Maria Antonia; Buscaglia, JoAnn

2016-09-01

The data in this article supports the research paper entitled "Interexaminer variation of minutia markup on latent fingerprints" [1]. The data in this article describes the variability in minutia markup during both analysis of the latents and comparison between latents and exemplars. The data was collected in the "White Box Latent Print Examiner Study," in which each of 170 volunteer latent print examiners provided detailed markup documenting their examinations of latent-exemplar pairs of prints randomly assigned from a pool of 320 pairs. Each examiner examined 22 latent-exemplar pairs; an average of 12 examiners marked each latent.

Chronic Generalized Harassment during College: Influences on Alcohol and Drug Use

PubMed Central

McGinley, Meredith; Rospenda, Kathleen M.; Liu, Li; Richman, Judith A.

2015-01-01

The experience of chronic generalized harassment from others can have a deleterious impact on individuals over time. Specifically, coping resources may be taxed, resulting in the use of avoidant coping strategies such substance use. However, little is known about the experience of chronic generalized harassment (e.g., verbal hostility, manipulation by others, exclusion from important events) and its impact on substance use in collegiate populations. In the current study, we examined the latent growth of generalized harassment across the transition from high school to college, whether this growth was heterogeneous, and the relationships between latent generalized harassment classifications and substance use. Incoming freshmen students (N = 2890; 58% female; 53% White) at eight colleges in Illinois completed a web survey at four points: fall 2011 (baseline), spring 2012 (T1), fall 2012 (T2), and fall 2013 (T3). Students were required to be at least 18 years old at baseline, and were compensated with online gift certificates. Two-part Latent Class Growth Analysis (LCGA) was implemented in order to examine heterogeneous growth over time. The results supported a two-class solution (infrequent and chronic classes) for generalized harassment. Growth in harassment was characterized by a decrease from baseline through college entry, with a recovery in rates by T3. Members of the chronically harassed class had greater mean generalized harassment over time, and were less likely to report zero instances of harassment experiences. As hypothesized, membership in the chronic class predicted future binge drinking, drinking to intoxication, problems due to alcohol use, and cigarette use, but not marijuana use. Future interventions should focus on providing college students with resources to help cope with distress stemming from persistent generalized harassment from peers, faculty, and other individuals in higher-education settings. PMID:26081935

Chronic Generalized Harassment During College: Influences on Alcohol and Drug Use.

PubMed

McGinley, Meredith; Rospenda, Kathleen M; Liu, Li; Richman, Judith A

2015-10-01

The experience of chronic generalized harassment from others can have a deleterious impact on individuals over time. Specifically, coping resources may be taxed, resulting in the use of avoidant coping strategies such as substance use. However, little is known about the experience of chronic generalized harassment (e.g., verbal hostility, manipulation by others, exclusion from important events) and its impact on substance use in collegiate populations. In the current study, we examined the latent growth of generalized harassment across the transition from high school to college, whether this growth was heterogeneous, and the relationships between latent generalized harassment classifications and substance use. Incoming freshmen students (N = 2890; 58% female; 53% white) at eight colleges in Illinois completed a web survey at five points: fall 2011 (baseline), spring 2012 (T1), fall 2012 (T2), fall 2013 (T3) and fall 2014 (T4). Students were required to be at least 18 years old at baseline, and were compensated with online gift certificates. Two-part latent class growth analysis was implemented in order to examine heterogeneous growth over time. The results supported a two-class solution (infrequent and chronic classes) for generalized harassment. Growth in harassment was characterized by a decrease from baseline through college entry, with a recovery in rates by T3. Members of the chronically harassed class had greater mean generalized harassment over time, and were less likely to report zero instances of harassment experiences. As hypothesized, membership in the chronic class predicted future binge drinking, drinking to intoxication, problems due to alcohol use, and cigarette use, but not marijuana use. Future interventions should focus on providing college students with resources to help cope with distress stemming from persistent generalized harassment from peers, faculty, and other individuals in higher-education settings.

Multi-level multi-task learning for modeling cross-scale interactions in nested geospatial data

USGS Publications Warehouse

Yuan, Shuai; Zhou, Jiayu; Tan, Pang-Ning; Fergus, Emi; Wagner, Tyler; Sorrano, Patricia

2017-01-01

Predictive modeling of nested geospatial data is a challenging problem as the models must take into account potential interactions among variables defined at different spatial scales. These cross-scale interactions, as they are commonly known, are particularly important to understand relationships among ecological properties at macroscales. In this paper, we present a novel, multi-level multi-task learning framework for modeling nested geospatial data in the lake ecology domain. Specifically, we consider region-specific models to predict lake water quality from multi-scaled factors. Our framework enables distinct models to be developed for each region using both its local and regional information. The framework also allows information to be shared among the region-specific models through their common set of latent factors. Such information sharing helps to create more robust models especially for regions with limited or no training data. In addition, the framework can automatically determine cross-scale interactions between the regional variables and the local variables that are nested within them. Our experimental results show that the proposed framework outperforms all the baseline methods in at least 64% of the regions for 3 out of 4 lake water quality datasets evaluated in this study. Furthermore, the latent factors can be clustered to obtain a new set of regions that is more aligned with the response variables than the original regions that were defined a priori from the ecology domain.

The Rubber Hand Illusion paradigm as a sensory learning process in patients with schizophrenia.

PubMed

Lev-Ari, L; Hirschmann, S; Dyskin, O; Goldman, O; Hirschmann, I

2015-10-01

The Rubber Hand Illusion (RHI) has previously been used to depict the hierarchy between visual, tactile and perceptual stimuli. Studies on schizophrenia inpatients (SZs) have found mixed results in the ability to first learn the illusion, and have yet to explain the learning process involved. This study's aim was two-fold: to examine the learning process of the RHI in SZs and healthy controls over time, and to better understand the relationship between psychotic symptoms and the RHI. Thirty schizophrenia inpatients and 30 healthy controls underwent five different trials of the RHI over a two-week period. As has been found in previous studies, SZs felt the initial illusion faster than healthy controls did, but their learning process throughout the trials was inconsistent. Furthermore, for SZs, no correlations between psychotic symptoms and the learning of the illusion emerged. Healthy individuals show a delayed reaction to first feeling the illusion (due to latent inhibition), but easily learn the illusion over time. For SZs, both strength of the illusion and the ability to learn the illusion over time are inconsistent. The cognitive impairment in SZ impedes the learning process of the RHI, and SZs are unable to utilize the repetition of the process as healthy individuals can. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Relating Schizotypy and Personality to the Phenomenology of Creativity

PubMed Central

Nelson, B.; Rawlings, D.

2010-01-01

Introduction: Although a considerable amount of research has addressed psychopathological and personality correlates of creativity, the relationship between these characteristics and the phenomenology of creativity has been neglected. Relating these characteristics to the phenomenology of creativity may assist in clarifying the precise nature of the relationship between psychopathology and creativity. The current article reports on an empirical study of the relationship between the phenomenology of the creative process and psychopathological and personality characteristics in a sample of artists. Method: A total of 100 artists (43 males, 57 females, mean age = 34.69 years) from a range of disciplines completed the Experience of Creativity Questionnaire and measures of “positive” schizotypy, affective disturbance, mental boundaries, and normal personality. Results: The sample of artists was found to be elevated on “positive” schizotypy, unipolar affective disturbance, thin boundaries, and the personality dimensions of Openness to Experience and Neuroticism, compared with norm data. Schizotypy was found to be the strongest predictor of a range of creative experience scales (Distinct Experience, Anxiety, Absorption, Power/Pleasure), suggesting a strong overlap of schizotypal and creative experience. Discussion: These findings indicate that “positive” schizotypy is associated with central features of “flow”-type experience, including distinct shift in phenomenological experience, deep absorption, focus on present experience, and sense of pleasure. The neurologically based construct of latent inhibition may be a mechanism that facilitates entry into flow-type states for schizotypal individuals. This may occur by reduced latent inhibition providing a “fresh” awareness and therefore a greater absorption in present experience, thus leading to flow-type states. PMID:18682376

Learning topic models by belief propagation.

PubMed

Zeng, Jia; Cheung, William K; Liu, Jiming

2013-05-01

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

Ratcheting up the ratchet: on the evolution of cumulative culture

PubMed Central

Tennie, Claudio; Call, Josep; Tomasello, Michael

2009-01-01

Some researchers have claimed that chimpanzee and human culture rest on homologous cognitive and learning mechanisms. While clearly there are some homologous mechanisms, we argue here that there are some different mechanisms at work as well. Chimpanzee cultural traditions represent behavioural biases of different populations, all within the species’ existing cognitive repertoire (what we call the ‘zone of latent solutions’) that are generated by founder effects, individual learning and mostly product-oriented (rather than process-oriented) copying. Human culture, in contrast, has the distinctive characteristic that it accumulates modifications over time (what we call the ‘ratchet effect’). This difference results from the facts that (i) human social learning is more oriented towards process than product and (ii) unique forms of human cooperation lead to active teaching, social motivations for conformity and normative sanctions against non-conformity. Together, these unique processes of social learning and cooperation lead to humans’ unique form of cumulative cultural evolution. PMID:19620111

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination.

PubMed

Zhao, Qibin; Zhang, Liqing; Cichocki, Andrzej

2015-09-01

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank . In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.

A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing

PubMed Central

Wang, Guoli; Ebrahimi, Nader

2014-01-01

Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H, such that V ∼ W H. It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H. In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data. PMID:25821345

A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing.

PubMed

Devarajan, Karthik; Wang, Guoli; Ebrahimi, Nader

2015-04-01

Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H , such that V ∼ W H . It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H . In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data.

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

PubMed

Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

2015-01-01

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

Deletion of striatal adenosine A2A receptor spares latent inhibition and prepulse inhibition but impairs active avoidance learning

PubMed Central

Singer, Philipp; Wei, Catherine J.; Chen, Jiang-Fan; Boison, Detlev; Yee, Benjamin K.

2013-01-01

Following early clinical leads, the adenosine A2AR receptor (A2AR) has continued to attract attention as a potential novel target for treating schizophrenia; especially against the negative and cognitive symptoms of the disease because of A2AR’s unique modulatory action over glutamatergic in addition to dopaminergic signaling. Through the antagonistic interaction with the dopamine D2 receptor, and by regulating glutamate release and N-methyl-d-aspartate receptor function, striatal A2AR is ideally positioned to fine-tune the dopamine-glutamate balance whose disturbance is implicated in the pathophysiology of schizophrenia. However, the precise function of striatal A2ARsin the regulation of schizophrenia-relevant behavior is poorly understood. Here, we tested the impact of conditional striatum-specific A2AR knockout (st-A2AR-KO) on latent inhibition (LI) and prepulse inhibition (PPI) – behavior that is tightly regulated by striatal dopamine and glutamate. These are two common cross-species translational tests for the assessment of selective attention and sensorimotor gating deficits reported in schizophrenia patients; and enhanced performance in these tests is associated with antipsychotic drug action. We found that neither LI nor PPI was significantly affected in st-A2AR-KO mice; although a deficit in active avoidance learning was identified in these animals. The latter phenotype, however, was not replicated in another form of aversive conditioning – conditioned taste aversion. Hence, the present study shows that neither learned inattention (as measured by LI) nor sensory gating (as indexed by PPI) requires the integrity of striatal A2ARs– a finding that may undermine the hypothesized importance of A2AR in the genesis and/or treatment of schizophrenia. PMID:23276608

Machine Learning-Based Classification of 38 Years of Spine-Related Literature Into 100 Research Topics.

PubMed

Sing, David C; Metz, Lionel N; Dudli, Stefan

2017-06-01

Retrospective review. To identify the top 100 spine research topics. Recent advances in "machine learning," or computers learning without explicit instructions, have yielded broad technological advances. Topic modeling algorithms can be applied to large volumes of text to discover quantifiable themes and trends. Abstracts were extracted from the National Library of Medicine PubMed database from five prominent peer-reviewed spine journals (European Spine Journal [ESJ], The Spine Journal [SpineJ], Spine, Journal of Spinal Disorders and Techniques [JSDT], Journal of Neurosurgery: Spine [JNS]). Each abstract was entered into a latent Dirichlet allocation model specified to discover 100 topics, resulting in each abstract being assigned a probability of belonging in a topic. Topics were named using the five most frequently appearing terms within that topic. Significance of increasing ("hot") or decreasing ("cold") topic popularity over time was evaluated with simple linear regression. From 1978 to 2015, 25,805 spine-related research articles were extracted and classified into 100 topics. Top two most published topics included "clinical, surgeons, guidelines, information, care" (n = 496 articles) and "pain, back, low, treatment, chronic" (424). Top two hot trends included "disc, cervical, replacement, level, arthroplasty" (+0.05%/yr, P < 0.001), and "minimally, invasive, approach, technique" (+0.05%/yr, P < 0.001). By journal, the most published topics were ESJ-"operative, surgery, postoperative, underwent, preoperative"; SpineJ-"clinical, surgeons, guidelines, information, care"; Spine-"pain, back, low, treatment, chronic"; JNS- "tumor, lesions, rare, present, diagnosis"; JSDT-"cervical, anterior, plate, fusion, ACDF." Topics discovered through latent Dirichlet allocation modeling represent unbiased meaningful themes relevant to spine care. Topic dynamics can provide historical context and direction for future research for aspiring investigators and trainees interested in spine careers. Please explore https://singdc.shinyapps.io/spinetopics. N A.

Cognition Predicts Quality of Life Among Patients With End-Stage Liver Disease.

PubMed

Paulson, Daniel; Shah, Mona; Miller-Matero, Lisa Renee; Eshelman, Anne; Abouljoud, Marwan

2016-01-01

Impaired cognitive functioning and poor quality of life (QoL) are both common among patients with end-stage liver disease; however, it is unclear how these are related. This study examines how specific cognitive domains predict QoL among liver transplant candidates by replicating Stewart and colleagues' (2010) 3-factor model of cognitive functioning, and determining how variability in these cognitive domains predicts mental health and physical QoL. The sample included 246 patients with end-stage liver disease who were candidates for liver transplant at a large, Midwestern health care center. Measures, including the Repeatable Battery for the Assessment of Neuropsychological Status, Trail Making Test, Shipley Institute of Living Scale, Short-Form Health Survey-36 Version 2, and Hospital Anxiety and Depression Scale, comprised latent variables representing global intellectual functioning, psychomotor speed, and learning and memory functioning. Confirmatory factor analysis results indicate that the 3-factor solution model comprised of global intellectual functioning, psychomotor speed, and learning and memory functioning fit the data well. Addition of physical and mental health QoL latent factors resulted in a structural model also with good fit. Results related physical QoL to global intellectual functioning, and mental health QoL to global intellectual functioning and psychomotor functioning. Findings elucidate a relationship between cognition and QoL and support the use of routine neuropsychological screening with end-stage liver disease patients, specifically examining the cognitive domains of global intellectual, psychomotor, and learning and memory functioning. Subsequently, screening results may inform implementation of targeted interventions to improve QoL. Copyright © 2016 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.

A person-centered analysis of posttraumatic stress disorder symptoms following a natural disaster: predictors of latent class membership.

PubMed

Rosellini, Anthony J; Coffey, Scott F; Tracy, Melissa; Galea, Sandro

2014-01-01

The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Person-Centered Analysis of Posttraumatic Stress Disorder Symptoms Following a Natural Disaster: Predictors of Latent Class Membership

PubMed Central

Rosellini, Anthony J.; Coffey, Scott F.; Tracy, Melissa; Galea, Sandro

2014-01-01

The present study applied latent class analysis to a sample of 810 participants residing in southern Mississippi at the time of Hurricane Katrina to determine if people would report distinct, meaningful PTSD symptom classes following a natural disaster. We found a four-class solution that distinguished persons on the basis of PTSD symptom severity/pervasiveness (Severe, Moderate, Mild, and Negligible Classes). Multinomial logistic regression models demonstrated that membership in the Severe and Moderate Classes was associated with potentially traumatic hurricane-specific experiences (e.g., being physically injured, seeing dead bodies), pre-hurricane traumatic events, co-occurring depression symptom severity and suicidal ideation, certain religious beliefs, and post-hurricane stressors (e.g., social support). Collectively, the findings suggest that more severe/pervasive typologies of natural disaster PTSD may be predicted by the frequency and severity of exposure to stressful/traumatic experiences (before, during, and after the disaster), co-occurring psychopathology, and specific internal beliefs. PMID:24334161

Profiles of Adolescent Religiousness using Latent Profile Analysis: Implications for Psychopathology

PubMed Central

Longo, Gregory S.; Bray, Bethany; Kim-Spoon, Jungmeen

2017-01-01

Prior research has documented robust associations between adolescent religiousness/spirituality (R/S) and psychopathology outcomes including externalizing and internalizing symptomatology, yet no previous studies have examined these associations with adolescent R/S profiles using a person-centered approach. We examined if there are identifiable subgroups characterized by unique multidimensional patterns of R/S experiences and how these experiences may be related to externalizing and internalizing symptomatology. The sample consisted of 220 Appalachian adolescents between 12 and 18 years old who were primarily White and primarily Christian. Latent profile analysis revealed three profiles of adolescent R/S; high religiousness (28.4%), introjectors (47.6%), and low religiousness (24.0%). These profiles were differentially related to internalizing and externalizing symptomatology such that the high religiousness group was significantly lower than the introjectors with respect to internalizing and externalizing symptomatology and lower than the low religiousness group in externalizing symptomatology. Implications and suggestions for future research using person-centered approaches to better understand differential developmental trajectories of religious development are provided. PMID:28220955

The study of latent heat transport characteristics by solid particles and saccharide solution mixtures

NASA Astrophysics Data System (ADS)

Morita, Shin-ichi; Hayamizu, Yasutaka; Inaba, Hideo

2011-06-01

The purpose of this study is the development of latent heat transport system by using the mixture of the minute latent heat storage materials and the saccharine solution as medium. The experimental studies are carried out by the evaluation of viscosity and pressure loss in a pipe. Polyethylene (P.E.) is selected as the dispersed minute material that has closeness density (920kg/m3) of ice (917kg/m3). D-sorbitol and D-xylose solutions are picked as continuum phase of the test mixture. The concentration of D-sorbitol solution is set 48mass% from measured results of saturation solubility and the melting point. 40mass% solution of D-xylose is selected as the other test continuum phase. The non-ion surfactant, EA157 Dai-ichiseiyaku CO. Ltd, is used in order to prevent of dispersed P.E. powder cohere. The pressure loss of test mixture is measured by the straight circular pipe that has smooth inner surface. The measuring length for pressure loss is 1000 mm, and the inner diameter of pipe is 15mm. The accuracy of experiment apparatus for measuring pressure loss is within ±5%. The pressure loss data is estimated by the relationship between the heat transport ratio and the required pump power. It is clarified that the optimum range of mixing ratio exists over 10mass% of latent heat storage material.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives.

PubMed

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context.

Toward a self-organizing pre-symbolic neural model representing sensorimotor primitives

PubMed Central

Zhong, Junpei; Cangelosi, Angelo; Wermter, Stefan

2014-01-01

The acquisition of symbolic and linguistic representations of sensorimotor behavior is a cognitive process performed by an agent when it is executing and/or observing own and others' actions. According to Piaget's theory of cognitive development, these representations develop during the sensorimotor stage and the pre-operational stage. We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams. This model employs neural network architecture incorporating a predictive sensory module based on an RNNPB (Recurrent Neural Network with Parametric Biases) and a horizontal product model. We exemplify this model through a robot passively observing an object to learn its features and movements. During the learning process of observing sensorimotor primitives, i.e., observing a set of trajectories of arm movements and its oriented object features, the pre-symbolic representation is self-organized in the parametric units. These representational units act as bifurcation parameters, guiding the robot to recognize and predict various learned sensorimotor primitives. The pre-symbolic representation also accounts for the learning of sensorimotor primitives in a latent learning context. PMID:24550798

Machine Learning Approaches for Detecting Diabetic Retinopathy from Clinical and Public Health Records.

PubMed

Ogunyemi, Omolola; Kermah, Dulcie

2015-01-01

Annual eye examinations are recommended for diabetic patients in order to detect diabetic retinopathy and other eye conditions that arise from diabetes. Medically underserved urban communities in the US have annual screening rates that are much lower than the national average and could benefit from informatics approaches to identify unscreened patients most at risk of developing retinopathy. Using clinical data from urban safety net clinics as well as public health data from the CDC's National Health and Nutrition Examination Survey, we examined different machine learning approaches for predicting retinopathy from clinical or public health data. All datasets utilized exhibited a class imbalance. Classifiers learned on the clinical data were modestly predictive of retinopathy with the best model having an AUC of 0.72, sensitivity of 69.2% and specificity of 55.9%. Classifiers learned on public health data were not predictive of retinopathy. Successful approaches to detecting latent retinopathy using machine learning could help safety net and other clinics identify unscreened patients who are most at risk of developing retinopathy and the use of ensemble classifiers on clinical data shows promise for this purpose.

Predicting Nurses' Turnover: The Aversive Effects of Decreased Identity, Poor Interpersonal Communication, and Learned Helplessness.

PubMed

Moreland, Jennifer J; Ewoldsen, David R; Albert, Nancy M; Kosicki, Gerald M; Clayton, Margaret F

2015-01-01

Through a social identity theoretical lens, this study examines how nurses' identification with their working small group, unit, or floor, nursing role (e.g., staff ER nurse, nurse practitioner), and nursing profession relate to nurses' interaction involvement, willingness to confront conflict, feelings of learned helplessness, and tenure (employment turnover) intentions. A cross-sectional survey (N = 466) was conducted at a large, quaternary care hospital system. Structural equation modeling uncovered direct and indirect effects between the five primary variables. Findings demonstrate direct relationships between nurse identity (as a latent variable) and interaction involvement, willingness to confront conflict, and tenure intentions. Feelings of learned helplessness are attenuated by increased nurse identity through interaction involvement and willingness to confront conflict. In addition, willingness to confront conflict and learned helplessness mediate the relationship between interaction involvement and nurses' tenure intentions. Theoretical extensions include indirect links between nurse identity and learned helplessness via interaction involvement and willingness to confront conflict. Implications for interpersonal communication theory development, health communication, and the nursing profession are discussed.

Microcomputer-based classification of environmental data in municipal areas

NASA Astrophysics Data System (ADS)

Thiergärtner, H.

1995-10-01

Multivariate data-processing methods used in mineral resource identification can be used to classify urban regions. Using elements of expert systems, geographical information systems, as well as known classification and prognosis systems, it is possible to outline a single model that consists of resistant and of temporary parts of a knowledge base including graphical input and output treatment and of resistant and temporary elements of a bank of methods and algorithms. Whereas decision rules created by experts will be stored in expert systems directly, powerful classification rules in form of resistant but latent (implicit) decision algorithms may be implemented in the suggested model. The latent functions will be transformed into temporary explicit decision rules by learning processes depending on the actual task(s), parameter set(s), pixels selection(s), and expert control(s). This takes place both at supervised and nonsupervised classification of multivariately described pixel sets representing municipal subareas. The model is outlined briefly and illustrated by results obtained in a target area covering a part of the city of Berlin (Germany).

Maximum likelihood estimation of finite mixture model for economic data

NASA Astrophysics Data System (ADS)

Phoong, Seuk-Yen; Ismail, Mohd Tahir

2014-06-01

Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.

Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

PubMed Central

Jones, Michael N.

2017-01-01

A central goal of cognitive neuroscience is to decode human brain activity—that is, to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive—that is, capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a probabilistic decoding framework based on a novel topic model—Generalized Correspondence Latent Dirichlet Allocation—that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text—enabling researchers, for the first time, to generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity. PMID:29059185

The potential of latent semantic analysis for machine grading of clinical case summaries.

PubMed

Kintsch, Walter

2002-02-01

This paper introduces latent semantic analysis (LSA), a machine learning method for representing the meaning of words, sentences, and texts. LSA induces a high-dimensional semantic space from reading a very large amount of texts. The meaning of words and texts can be represented as vectors in this space and hence can be compared automatically and objectively. A generative theory of the mental lexicon based on LSA is described. The word vectors LSA constructs are context free, and each word, irrespective of how many meanings or senses it has, is represented by a single vector. However, when a word is used in different contexts, context appropriate word senses emerge. Several applications of LSA to educational software are described, involving the ability of LSA to quickly compare the content of texts, such as an essay written by a student and a target essay. An LSA-based software tool is sketched for machine grading of clinical case summaries written by medical students.

Discriminative parameter estimation for random walks segmentation.

PubMed

Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

2013-01-01

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

Simulating Expert Clinical Comprehension: Adapting Latent Semantic Analysis to Accurately Extract Clinical Concepts from Psychiatric Narrative

PubMed Central

Cohen, Trevor; Blatter, Brett; Patel, Vimla

2008-01-01

Cognitive studies reveal that less-than-expert clinicians are less able to recognize meaningful patterns of data in clinical narratives. Accordingly, psychiatric residents early in training fail to attend to information that is relevant to diagnosis and the assessment of dangerousness. This manuscript presents cognitively motivated methodology for the simulation of expert ability to organize relevant findings supporting intermediate diagnostic hypotheses. Latent Semantic Analysis is used to generate a semantic space from which meaningful associations between psychiatric terms are derived. Diagnostically meaningful clusters are modeled as geometric structures within this space and compared to elements of psychiatric narrative text using semantic distance measures. A learning algorithm is defined that alters components of these geometric structures in response to labeled training data. Extraction and classification of relevant text segments is evaluated against expert annotation, with system-rater agreement approximating rater-rater agreement. A range of biomedical informatics applications for these methods are suggested. PMID:18455483

Epstein-Barr Virus Latent Membrane Protein 1 Regulates the Function of Interferon Regulatory Factor 7 by Inducing Its Sumoylation

PubMed Central

Bentz, Gretchen L.; Shackelford, Julia

2012-01-01

Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1) induces multiple signal transduction pathways during latent EBV infection via its C-terminal activating region 1 (CTAR1), CTAR2, and the less-studied CTAR3. One mechanism by which LMP1 regulates cellular activation is through the induction of protein posttranslational modifications, including phosphorylation and ubiquitination. We recently documented that LMP1 induces a third major protein modification by physically interacting with the SUMO-conjugating enzyme Ubc9 through CTAR3 and inducing the sumoylation of cellular proteins in latently infected cells. We have now identified a specific target of LMP1-induced sumoylation, interferon regulatory factor 7 (IRF7). We hypothesize that during EBV latency, LMP1 induces the sumoylation of IRF7, limiting its transcriptional activity and modulating the activation of innate immune responses. Our data show that endogenously sumoylated IRF7 is detected in latently infected EBV lymphoblastoid cell lines. LMP1 expression coincided with increased sumoylation of IRF7 in a CTAR3-dependent manner. Additional experiments show that LMP1 CTAR3-induced sumoylation regulates the expression and function of IRF7 by decreasing its turnover, increasing its nuclear retention, decreasing its DNA binding, and limiting its transcriptional activation. Finally, we identified that IRF7 is sumoylated at lysine 452. These data demonstrate that LMP1 CTAR3 does in fact function in intracellular signaling, leading to biologic effects. We propose that CTAR3 is an important signaling region of LMP1 that regulates protein function by sumoylation. We have shown specifically that LMP1 CTAR3, in cooperation with CTAR2, can limit the ability of IRF7 to induce innate immune responses by inducing the sumoylation of IRF7. PMID:22951831

Effects of latent heat in various cloud microphysics processes on autumn rainstorms with different intensities on Hainan Island, China

NASA Astrophysics Data System (ADS)

Li, Jiangnan; Wu, Kailu; Li, Fangzhou; Chen, Youlong; Huang, Yanbin; Feng, YeRong

2017-06-01

In this study, we used the Weather Research and Forecasting (WRF) and WRF-3DVAR models to perform a series of simulations of two autumn rainstorms on Hainan Island. The results of neighborhood fractions and Hanssen skill scoring (FSS, HSS) methods show that the control experiments reproduced well two heavy rainfall episodes. Effects of latent heat in various cloud microphysical processes are different at distinct intensities or stages of precipitation. In the absence of any heating effect of deposition, precipitation weakened. The greater was the precipitation, the more significant was the weakening effect. Ascending movement at upper troposphere could be weakened or descending movement at lower troposphere enhanced. With decreases in the strength of precipitation, cloud ice, snow, graupel, and rainwater, increases in latent heat lessened. With weak precipitation, at upper troposphere the rainwater content increased and snow and ice content decreased, whereas at middle troposphere, the ice, snow, and graupel contents increased. Latent heating increased at middle and lower troposphere and decreased at upper troposphere. The absence of any heating effect of freezing had little effect on precipitation. By removing the evaporative cooling of cloud water, the interactions between vertical movement and cloud microphysical processes resulted in a weakening of strong precipitation and an intensification of weak precipitation. However, in the preliminary stages of these two precipitation events, snow, graupel, cloud ice, and rainwater all increased, and precipitation was enhanced in both. In the later stages, strong precipitation systems weakened and weak precipitation systems strengthened. Latent heating first increased and then dropped in strong precipitation systems, whereas they continuously increased in weak precipitation systems.

A Latent Transition Model with Logistic Regression

ERIC Educational Resources Information Center

Chung, Hwan; Walls, Theodore A.; Park, Yousung

2007-01-01

Latent transition models increasingly include covariates that predict prevalence of latent classes at a given time or transition rates among classes over time. In many situations, the covariate of interest may be latent. This paper describes an approach for handling both manifest and latent covariates in a latent transition model. A Bayesian…

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

PubMed

Howard, Rebecca; Rattray, Magnus; Prosperi, Mattia; Custovic, Adnan

2015-07-01

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies.

Heritability, family, school and academic achievement in adolescence.

PubMed

Pokropek, Artur; Sikora, Joanna

2015-09-01

We demonstrate how genetically informed designs can be applied to administrative exam data to study academic achievement. ACE mixture latent class models have been used with Year 6 and 9 exam data for seven cohorts of Polish students which include 24,285 pairs of twins. Depending on a learning domain and classroom environment history, from 58% to 88% of variance in exam results is attributable to heritability, up to 34% to shared environment and from 8% to 15% depends on unique events in students' lives. Moreover, between 54% and 66% of variance in students' learning gains made between Years 6 and 9 is explained by heritability. The unique environment accounts for between 34% and 46% of that variance. However, we find no classroom effects on student progress made between Years 6 and 9. We situate this finding against the view that classroom peer groups and teachers matter for adolescent learning. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting long-term growth in students' mathematics achievement: the unique contributions of motivation and cognitive strategies.

PubMed

Murayama, Kou; Pekrun, Reinhard; Lichtenfeld, Stephanie; Vom Hofe, Rudolf

2013-01-01

This research examined how motivation (perceived control, intrinsic motivation, and extrinsic motivation), cognitive learning strategies (deep and surface strategies), and intelligence jointly predict long-term growth in students' mathematics achievement over 5 years. Using longitudinal data from six annual waves (Grades 5 through 10; Mage  = 11.7 years at baseline; N = 3,530), latent growth curve modeling was employed to analyze growth in achievement. Results showed that the initial level of achievement was strongly related to intelligence, with motivation and cognitive strategies explaining additional variance. In contrast, intelligence had no relation with the growth of achievement over years, whereas motivation and learning strategies were predictors of growth. These findings highlight the importance of motivation and learning strategies in facilitating adolescents' development of mathematical competencies. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Effect of detergents, trypsin and unsaturated fatty acids on latent loquat fruit polyphenol oxidase: basis for the enzyme's activity regulation.

PubMed

Sellés-Marchart, Susana; Casado-Vela, Juan; Bru-Martínez, Roque

2007-08-15

The effects of detergents, trypsin and fatty acids on structural and functional properties of a pure loquat fruit latent polyphenol oxidase have been studied in relation to its regulation. Anionic detergents activated PPO at pH 6.0 below critical micelle concentration (cmc), but inhibited at pH 4.5 well above cmc. This behavior is due to a detergent-induced pH profile alkaline shift, accompanied by changes of intrinsic fluorescence of the protein. Gel filtration experiments demonstrate the formation of PPO-SDS mixed micelles. Partial PPO proteolysis suggest that latent PPO losses an SDS micelle-interacting region but conserves an SDS monomer-interacting site. Unsaturated fatty acids inhibit PPO at pH 4.5, the strongest being linolenic acid while the weakest was gamma-linolenic acid for both, the native and the trypsin-treated PPO. Down-regulation of PPO activity by anionic amphiphiles is discussed based on both, the pH profile shift induced upon anionic amphiphile binding and the PPO interaction with negatively charged membranes.

Latent classes of sexual behaviors: Prevalence, predictors, and consequences

PubMed Central

Wesche, Rose; Lefkowitz, Eva S.; Vasilenko, Sara A.

2016-01-01

Scholars of adolescent and emerging adult sexuality have recently begun to study how diverse patterns of sexual behaviors contribute to development and well-being. A person-oriented approach to studying sexual behaviors provides a nuanced understanding of sexual repertoires. The goals of this paper were to document patterns of sexual behaviors ranging from kissing to penetrative sex, and to examine how latent classes of behaviors, gender, and partner type (romantic vs. nonromantic) predict intra- and interpersonal consequences of sexual behaviors. Latent class analysis of a stratified random sample of U.S. college students revealed four classes of sexual behaviors: Kissing Only, Kissing and Touching, All Behaviors, and Oral and Penetrative Only. Compared to individuals in the All Behaviors class, individuals in the Kissing Only class were less likely to experience a positive or a negative intrapersonal consequence of sexual behaviors. Men were less likely to report a negative intrapersonal consequence than women were. Partner type predicted negative interpersonal consequences for the All Behaviors class. Implications are discussed in terms of normative sexual development, prevention, and sexual and relationship education. PMID:28163800

Old ideas to innovate tuberculosis control: preventive treatment to achieve elimination.

PubMed

Diel, Roland; Loddenkemper, Robert; Zellweger, Jean-Pierre; Sotgiu, Giovanni; D'Ambrosio, Lia; Centis, Rosella; van der Werf, Marieke J; Dara, Masoud; Detjen, Anne; Gondrie, Peter; Reichman, Lee; Blasi, Francesco; Migliori, Giovanni Battista

2013-09-01

The introduction of new rapid diagnostic tools for tuberculosis (TB) and the promising TB drugs pipeline together with the development of a new World Health Organization Strategy post 2015 allows new discussions on how to direct TB control. The European Respiratory Society's European Forum for TB Innovation was created to stimulate discussion on how to best take advantage of old and new opportunities, and advances, to improve TB control and eventually progress towards the elimination of TB. While TB control is aimed at reducing the incidence of TB by early diagnosis and treatment of infectious cases of TB, TB elimination requires focus on sterilising the pool of latently infected individuals, from which future TB cases would be generated. This manuscript describes the three core components that are necessary to implement the elimination strategy fully. 1) Improve diagnosis of latent TB infected individuals. 2) Improve regimens to treat latent TB infection. 3) ensure public health commitment to make both 1) and 2) possible. Old and new evidence is critically described, focusing on the European commitment to reach elimination and on the innovative experiences and best practices available.

Recent mouse models and vaccine candidates for preventing chronic/latent tuberculosis infection and its reactivation.

PubMed

Pedroza-Roldán, César; Flores-Valdez, Mario Alberto

2017-08-31

Tuberculosis (TB) remains a major challenge in public health worldwide. Until today, the only widely used and approved vaccine is the Mycobacterium bovis bacille Calmette-Guerin (BCG). This vaccine provides a highly variable level of protection against the active, pulmonary form of tuberculosis, and practically none against the latent form of TB infection. This disparity in protection has been extensively studied, and for this reason, several groups have focused their research on the quest for attenuated vaccines based on M. tuberculosis or on the identification of latency-associated antigens that can be incorporated into modified BCG, or that can be used as adjuvanted subunit vaccines. In order to seek new potential antigens relevant for infection, some researchers have performed experiments with highly sensitive techniques such as transcriptomic and proteomic analyses using sputum samples from humans or by using mouse models resembling several aspects of TB. In this review, we focus on reports of new mouse models or mycobacterial antigens recently tested for developing vaccine candidates against chronic/latent tuberculosis and its reactivation.

Latent heat of traffic moving from rest

NASA Astrophysics Data System (ADS)

Farzad Ahmadi, S.; Berrier, Austin S.; Doty, William M.; Greer, Pat G.; Habibi, Mohammad; Morgan, Hunter A.; Waterman, Josam H. C.; Abaid, Nicole; Boreyko, Jonathan B.

2017-11-01

Contrary to traditional thinking and driver intuition, here we show that there is no benefit to ground vehicles increasing their packing density at stoppages. By systematically controlling the packing density of vehicles queued at a traffic light on a Smart Road, drone footage revealed that the benefit of an initial increase in displacement for close-packed vehicles is completely offset by the lag time inherent to changing back into a ‘liquid phase’ when flow resumes. This lag is analogous to the thermodynamic concept of the latent heat of fusion, as the ‘temperature’ (kinetic energy) of the vehicles cannot increase until the traffic ‘melts’ into the liquid phase. These findings suggest that in situations where gridlock is not an issue, drivers should not decrease their spacing during stoppages in order to lessen the likelihood of collisions with no loss in flow efficiency. In contrast, motion capture experiments of a line of people walking from rest showed higher flow efficiency with increased packing densities, indicating that the importance of latent heat becomes trivial for slower moving systems.

Microaggressions, Discrimination, and Phenotype among African Americans: A Latent Class Analysis of the Impact of Skin Tone and BMI.

PubMed

Keith, Verna M; Nguyen, Ann W; Taylor, Robert Joseph; Mouzon, Dawne M; Chatters, Linda M

2017-05-01

Data from the 2001-2003National Survey of American Life are used to investigate the effects of phenotype on everyday experiences with discrimination among African Americans (N=3343). Latent class analysis is used to identify four classes of discriminatory treatment: 1) low levels of discrimination, 2) disrespect and condescension, 3) character-based discrimination, and 4) high levels of discrimination. We then employ latent class multinomial logistic regression to evaluate the association between skin tone and body weight and these four classes of discrimination. Designating the low level discrimination class as the reference group, findings revealed that respondents with darker skin were more likely to be classified into the disrespect/condescension and the high level microaggression types. BMI was unrelated to the discrimination type, although there was a significant interaction effect between gender and BMI. BMI was strongly and positively associated with membership in the disrespect and condescension type among men but not among women. These findings indicate that skin tone and body weight are two phenotypic characteristics that influence the type and frequency of discrimination experienced by African Americans.

Anesthesiology mentoring.

PubMed

Wenzel, Volker; Gravenstein, Nikolaus

2016-12-01

Mentoring is fundamentally valuable and important to students considering a path into our specialty, as well as to colleagues already in it and with ambition to advance. General principles and personal experiences are collected and described to help inform future mentors and to reinforce the value of having a mentor and the satisfaction (and work) that is associated with such a role. Detecting a latent talent among medical students or residents may be challenging but is worth the effort to develop personal careers and the specialty itself. Upon agreeing to jointly move a certain project, a professional plan is needed to improve chances of success and decrease the likelihood of frustration. Various challenges always have to be detected and solved, with the ultimate goal to guide a medical student to residency, subsequently into faculty status and preferably to lifelong collaboration. Access to a mentor is an often-cited key to choosing a specialty and the success of junior colleagues and thus the entire department. Mentoring is fundamentally valuable in providing role modeling and also in protecting the mentee from the inefficiency of learning lessons the hard way.

Everyday Discrimination and Mood and Substance Use Disorders: A Latent Profile Analysis with African Americans and Caribbean Blacks

PubMed Central

Clark, Trenette T.; Salas-Wright, Christopher P.; Vaughn, Michael G.; Whitfield, Keith E.

2016-01-01

Perceived discrimination is a major source of health-related stress. The purpose of this study was to model the heterogeneity of everyday-discrimination experiences among African American and Caribbean Blacks and to identify differences in the prevalence of mood and substance use outcomes, including generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder among the identified subgroups. The study uses data from the National Survey of American Life obtained from a sample of African American and Caribbean Black respondents (N = 4,462) between 18 and 65 years. We used latent profile analysis and multinomial regression analyses to identify and validate latent subgroups and test hypotheses, yielding 4 classes of perceived everyday discrimination: Low Discrimination, Disrespect and Condescension, General Discrimination, and Chronic Discrimination. Findings show significant differences exist between the Low Discrimination and General Discrimination classes for major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Moreover, we find significant differences exist between the Low Discrimination and Chronic Discrimination classes for the four disorders examined. Compared with the Chronic Discrimination class, members of the other classes were significantly less likely to meet criteria for generalized anxiety disorder, major depressive disorder, alcohol-use disorder, and illicit drug-use disorder. Findings suggest elevated levels of discrimination increase risk for mood and substance-use disorders. Importantly, results suggest the prevalence of mood and substance-use disorders is a function of the type and frequency of discrimination that individuals experience. PMID:25254321

Spam comments prediction using stacking with ensemble learning

NASA Astrophysics Data System (ADS)

Mehmood, Arif; On, Byung-Won; Lee, Ingyu; Ashraf, Imran; Choi, Gyu Sang

2018-01-01

Illusive comments of product or services are misleading for people in decision making. The current methodologies to predict deceptive comments are concerned for feature designing with single training model. Indigenous features have ability to show some linguistic phenomena but are hard to reveal the latent semantic meaning of the comments. We propose a prediction model on general features of documents using stacking with ensemble learning. Term Frequency/Inverse Document Frequency (TF/IDF) features are inputs to stacking of Random Forest and Gradient Boosted Trees and the outputs of the base learners are encapsulated with decision tree to make final training of the model. The results exhibits that our approach gives the accuracy of 92.19% which outperform the state-of-the-art method.

Situational relevance: Context as a factor in serial overshadowing of taste aversion learning.

PubMed

Kwok, Dorothy W S; Boakes, Robert A

2017-08-31

In a serial overshadowing procedure a target stimulus, A, is followed after an interval by a potentially interfering stimulus, B, and this is then followed by an unconditioned stimulus, US. Revusky (1977) proposed that the degree to which B overshadows conditioning of A depends on whether or not the two events take place in the same context. To test this proposal two experiments used a 1-trial long-delay conditioned taste aversion (CTA) procedure; sucrose served as the target taste (A) and dilute hydrochloric acid (HCl) as the overshadowing taste (B), with lithium chloride injection providing the US. In Experiment 1 these tastes were novel; weaker overshadowing by HCl of an aversion to sucrose was found when the two tastes were presented in different contexts. Experiment 2 tested whether the effect of pre-exposure to HCl, thereby rendering it less effective in overshadowing a sucrose aversion, was also context-dependent. In the conditioning session rats again received either context-same or context-different presentations of sucrose and HCl. However, for some rats HCl was pre-exposed in the same context to which it was later presented during conditioning (Consistent), while others were pre-exposed to HCl in a different context to the one in which it was presented during conditioning (Inconsistent). The Inconsistent group produced greater overshadowing than the Consistent group and thus confirmed that the latent inhibition effect was also context dependent. This study supports Revusky's (1977) idea of situational relevance.

M3D (Media 3D): a new programming language for web-based virtual reality in E-Learning and Edutainment

NASA Astrophysics Data System (ADS)

Chakaveh, Sepideh; Skaley, Detlef; Laine, Patricia; Haeger, Ralf; Maad, Soha

2003-01-01

Today, interactive multimedia educational systems are well established, as they prove useful instruments to enhance one's learning capabilities. Hitherto, the main difficulty with almost all E-Learning systems was latent in the rich media implementation techniques. This meant that each and every system should be created individually as reapplying the media, be it only a part, or the whole content was not directly possible, as everything must be applied mechanically i.e. by hand. Consequently making E-learning systems exceedingly expensive to generate, both in time and money terms. Media-3D or M3D is a new platform independent programming language, developed at the Fraunhofer Institute Media Communication to enable visualisation and simulation of E-Learning multimedia content. M3D is an XML-based language, which is capable of distinguishing between the3D models from that of the 3D scenes, as well as handling provisions for animations, within the programme. Here we give a technical account of M3D programming language and briefly describe two specific application scenarios where M3D is applied to create virtual reality E-Learning content for training of technical personnel.

Latent fingermark visualisation using reduced-pressure sublimation of copper phthalocyanine.

PubMed

Williams, Geraint; ap Llwyd Dafydd, Hefin; Watts, Alun; McMurray, Neil

2011-01-30

The sublimation of copper phthalocyanine (CuPc) at a temperature of 400°C under conditions of reduced pressure is shown to be an effective method of developing latent fingermarks on certain types of surface. Preliminary experiments on a limited selection of surfaces including paper, plastic and ceramic tiles were carried out using a simple apparatus consisting of a vacuum desiccator and a resistive heater. CuPc from the gas phase condenses preferentially on fingermark deposits, revealing deep blue patterns with excellent ridge detail clarity on light coloured surfaces. The technique is shown to be most effective on porous surfaces such as paper, but relatively ineffective on non-porous ceramic and plastic surfaces. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Heat transfer and evaporative cooling in the function of pot-in-pot coolers

NASA Astrophysics Data System (ADS)

Chemin, Arsène; Levy Dit Vehel, Victor; Caussarieu, Aude; Plihon, Nicolas; Taberlet, Nicolas

2018-03-01

A pot-in-pot cooler is an affordable electricity-free refrigerator which uses the latent heat of vaporization of water to maintain a low temperature inside an inner compartment. In this article, we experimentally investigate the influence of the main physical parameters in model pot-in-pot coolers. The effect of the wind on the evaporation rate of the cooling fluid is studied in model experiments while the influence of the fluid properties (thermal conductivity, specific heat, and latent heat) is elucidated using a variety of cooling fluids (water, ethanol, and ether). A model based on a simplified heat conduction equation is proposed and is shown to be in good quantitative agreement with the experimental measurements.

Test program for transmitter experiment package and heat pipe system for the communications technology satellite

NASA Technical Reports Server (NTRS)

Depauw, J. F.; Reader, K. E.; Staskus, J. V.

1976-01-01

The test program is described for the 200 watt transmitter experiment package and the variable conductance heat pipe system which are components of the high-power transponder aboard the Communications Technology Satellite. The program includes qualification tests to demonstrate design adequacy, acceptance tests to expose latent defects in flight hardware, and development tests to integrate the components into the transponder system and to demonstrate compatibility.

Men's and Women's Pathways to Adulthood and Their Adolescent Precursors

ERIC Educational Resources Information Center

Oesterle, Sabrina; Hawkins, J. David; Hill, Karl G.; Bailey, Jennifer A.

2010-01-01

This study compared men's and women's pathways to adulthood by examining how role transitions in education, work, marriage, and parenthood intersect and form developmental pathways from ages 18-30. The study investigated how sociodemographic factors and adolescent experiences were associated with these pathways. We used latent class analysis to…

77 FR 16232 - Agency Information Collection Activities: Proposed Collection; Comment Request; Postmarketing...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-20

... health from adverse drug experiences. All applicants who have received marketing approval of drug... common adverse effects, the larger and more diverse patient populations exposed to the marketed drug provide the opportunity to collect information on rare, latent, and long-term effects. Signals are...

Transcript profiling differentiates healthy control, sub-clinical and clinical Johne's disease in dairy cattle

USDA-ARS?s Scientific Manuscript database

Johne’s Disease (JD), a ruminant infectious disease caused by Mycobacterium avium subspecies paratuberculosis (MAP), is characterized by a long latent period followed by an aggressive acute phase in which the animal experiences diarrhea and extreme wasting. The absence of symptoms and low levels of ...

Usage-Based Language: Investigating the Latent Structures That Underpin Acquisition

ERIC Educational Resources Information Center

Ellis, Nick C.; O'Donnell, Matthew Brook; Romer, Ute

2013-01-01

Each of us as language learners had different language experiences, yet somehow we have converged upon broadly the same language system. From diverse, often noisy samples, we have attained similar linguistic competence. How so? What mechanisms channel language acquisition? Could our linguistic commonalities possibly have converged from our shared…

Exploration of Action Figure Appeals Using Evaluation Grid Method and Quantification Theory Type I

ERIC Educational Resources Information Center

Chang, Hua-Cheng; Chen, Hung-Yuan

2017-01-01

Contemporary toy is characterized by accelerating social, cultural and technological change. An attractive action figure can grab consumers' attention, influence the latent consuming preference and evoke their pleasure. However, traditional design of action figure is always dependent on designer's opinion, subjective experience and preference. It…

Learning trajectories of children with special health care needs across the severity spectrum.

PubMed

Goldfeld, Sharon; O'Connor, Meredith; Quach, Jon; Tarasuik, Joanne; Kvalsvig, Amanda

2015-01-01

A significant proportion of school-aged children experience special health care needs (SCHN) and seek care from pediatricians with a wide range of condition types and severity levels. This study examines the learning pathways of children with established (already diagnosed at school entry) and emerging (teacher identified) SHCN from school entry through the elementary school years. The Longitudinal Study of Australian Children (LSAC) is a nationally representative clustered cross-sequential sample of 2 cohorts of Australian children which commenced in May 2004. Data were analyzed from the LSAC kindergarten cohort (n = 4,983), as well as a subsample of 720 children for whom teachers also completed the Australian Early Development Index checklist, a measure of early childhood development at school entry that includes SHCN. Latent class analysis was utilized to establish 3 academic trajectories from 4-5 to 10-11 years: high (24.3%), average (49.8%), and low (23.6%). Descriptive statistics revealed a trend for both children with established and emerging SHCN to fall into weaker performing learning pathways. Multinomial logistic regression focusing on those children with emerging SHCN confirmed this pattern of results, even after adjustment for covariates (relative risk 3.06, 95% confidence interval 1.03-9.10). Children who additionally had low socioeconomic standing were particularly at risk. Even children with less complex SCHN are at risk for academic failure. Early identification, together with integrated health and educational support, may promote stronger pathways of educational attainment for these children. Achieving these better outcomes will require the involvement of both educational and health practitioners. Copyright © 2015 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.

Weakly Supervised Dictionary Learning

NASA Astrophysics Data System (ADS)

You, Zeyu; Raich, Raviv; Fern, Xiaoli Z.; Kim, Jinsub

2018-05-01

We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly learning a dictionary and corresponding sparse coefficients to provide accurate data representation. This approach is useful for denoising and signal restoration, but may lead to sub-optimal classification performance. By contrast, analysis dictionary learning provides a transform that maps data to a sparse discriminative representation suitable for classification. We consider the problem of analysis dictionary learning for time-series data under a weak supervision setting in which signals are assigned with a global label instead of an instantaneous label signal. We propose a discriminative probabilistic model that incorporates both label information and sparsity constraints on the underlying latent instantaneous label signal using cardinality control. We present the expectation maximization (EM) procedure for maximum likelihood estimation (MLE) of the proposed model. To facilitate a computationally efficient E-step, we propose both a chain and a novel tree graph reformulation of the graphical model. The performance of the proposed model is demonstrated on both synthetic and real-world data.

Partitioning of sublimation and evaporation from Lake Bonney using water vapor isotope and latent heat fluxes

NASA Astrophysics Data System (ADS)

Bellagamba, A. W.; Berkelhammer, M. B.; Winslow, L.; Peter, D.; Myers, K. F.

2017-12-01

The landscapes of the McMurdo Dry Valleys in Antarctica are characterized by a series of frozen lakes. Although the conditions in this region are severe, the lakes share common characteristics with lakes at glacial termini elsewhere. Geochemical and geomorphological evidence suggest these lakes have experienced large historical changes indicative of changes water balances. While part of these shifts in lake volume arise from changes in glacial inflow, they likely also reflect changes in the latent heat flux from the lake surfaces. Here we present a joint analysis of the stable isotopic ratio of surface ice/water and the water vapor flux over Dry Valley frozen lakes to ascertain the processes controlling water losses from the lake surfaces. We compare the isotopic ratio of the latent heat flux with the surface water isotopes to derive a fractionation factor associated with latent flux. This data is then used to provide insight into how much of the water vapor flux is sublimated versus evaporated, as well as how the sublimation and evaporative components of the flux change with synoptic weather. We used a Picarro L2130-I isotopic water analyzer to measure humidity and the isotopic ratio of water vapor at three heights over Lake Bonney in Taylor Valley, Antarctica and used the flux-gradient approach to convert the isotopic ratio of the vapor to an "isoflux". An on-site meteorological station recorded temperature, relative humidity and wind direction/intensity at two different heights above the lake and an infrared radiometer recorded lake skin temperature. These data were used to calculate the sensible and latent heat fluxes. The fractionation factor was close to 0, which indicates that sublimation was the primary component of the flux although evaporation became increasingly prominent following a katabatic wind event. The results suggest this technique could be an effective tool to study the sensitivity of latent heat fluxes to weather here and in other similar environments. The trial run performed at Lake Bonney in November-December 2016 was performed as part of the ongoing LTER (Long Term Ecological Research) project at the McMurdo Dry Valleys and a second experiment will be performed in January 2018.

Exploring evidence of a dissociative subtype in PTSD: Baseline symptom structure, etiology, and treatment efficacy for those who dissociate.

PubMed

Burton, Mark S; Feeny, Norah C; Connell, Arin M; Zoellner, Lori A

2018-05-01

With the inclusion of a dissociative subtype, recent changes to the DSM-5 diagnosis of posttraumatic stress disorder (PTSD) have emphasized the role of dissociation in the experience and treatment of the disorder. However, there is a lack of research exploring the clinical impact for highly dissociative groups receiving treatment for PTSD. The current study examined the presence and clinical impact of a dissociative subtype in a sample of individuals receiving treatment for chronic PTSD. This study used latent transition analyses (LTA), an expanded form of latent profile analyses (LPA), to examine latent profiles of PTSD and dissociation symptoms before and after treatment for individuals (N = 200) receiving prolonged exposure (PE) or sertraline treatment for chronic PTSD. The best fitting LTA model was one with a 4-class solution at both pretreatment and posttreatment. There was a latent class at pretreatment with higher levels of dissociative symptoms. However, this class was also marked by higher reexperiencing symptoms, and membership was not predicted by chronic child abuse. Further, although those in the class were less likely to transition to the responder class overall, this was not the case for exposure-based treatment specifically. These findings are not in line with the dissociative-subtype theoretical literature that proposes those who dissociate represent a clinically distinct group that may respond worse to exposure-based treatments for PTSD. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Convex composite wavelet frame and total variation-based image deblurring using nonconvex penalty functions

NASA Astrophysics Data System (ADS)

Shen, Zhengwei; Cheng, Lishuang

2017-09-01

Total variation (TV)-based image deblurring method can bring on staircase artifacts in the homogenous region of the latent images recovered from the degraded images while a wavelet/frame-based image deblurring method will lead to spurious noise spikes and pseudo-Gibbs artifacts in the vicinity of discontinuities of the latent images. To suppress these artifacts efficiently, we propose a nonconvex composite wavelet/frame and TV-based image deblurring model. In this model, the wavelet/frame and the TV-based methods may complement each other, which are verified by theoretical analysis and experimental results. To further improve the quality of the latent images, nonconvex penalty function is used to be the regularization terms of the model, which may induce a stronger sparse solution and will more accurately estimate the relative large gradient or wavelet/frame coefficients of the latent images. In addition, by choosing a suitable parameter to the nonconvex penalty function, the subproblem that splits by the alternative direction method of multipliers algorithm from the proposed model can be guaranteed to be a convex optimization problem; hence, each subproblem can converge to a global optimum. The mean doubly augmented Lagrangian and the isotropic split Bregman algorithms are used to solve these convex subproblems where the designed proximal operator is used to reduce the computational complexity of the algorithms. Extensive numerical experiments indicate that the proposed model and algorithms are comparable to other state-of-the-art model and methods.

Vertical Profiles of Latent Heat Release and Their Retrieval for TOGA COARE Convective Systems Using a Cloud Resolving Model, SSM/I, and Ship-borne Radar Data

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Lang, S.; Simpson, J.; Olson, W. S.; Johnson, D.; Ferrier, B.; Kummerow, C.; Adler, R.

1999-01-01

Latent heating profiles associated with three (TOGA COARE) Tropical Ocean and Global Atmosphere Coupled Ocean Atmosphere Response Experiment active convective episodes (December 10-17 1992; December 19-27 1992; and February 9-13 1993) are examined using the Goddard Cumulus Ensemble (GCE) Model and retrieved by using the Goddard Convective and Stratiform Heating (CSH) algorithm . The following sources of rainfall information are input into the CSH algorithm: Special Sensor Microwave Imager (SSM/1), Radar and the GCE model. Diagnostically determined latent heating profiles calculated using 6 hourly soundings are used for validation. The GCE model simulated rainfall and latent heating profiles are in excellent agreement with those estimated by soundings. In addition, the typical convective and stratiform heating structures (or shapes) are well captured by the GCE model. Radar measured rainfall is smaller than that both estimated by the GCE model and SSM/I in all three different COARE IFA periods. SSM/I derived rainfall is more than the GCE model simulated for the December 19-27 and February 9-13 periods, but is in excellent agreement with the GCE model for the December 10-17 period. The GCE model estimated stratiform amount is about 50% for December 19-27, 42% for December 11-17 and 56% for the February 9-13 case. These results are consistent with large-scale analyses. The accurate estimates of stratiform amount is needed for good latent heating retrieval. A higher (lower) percentage of stratiform rain can imply a maximum heating rate at a higher (lower) altitude. The GCE model always simulates more stratiform rain (10 to 20%) than the radar for all three convective episodes. SSM/I derived stratiform amount is about 37% for December 19-27, 48% for December 11-17 and 41% for the February 9-13 case. Temporal variability of CSH algorithm retrieved latent heating profiles using either GCE model simulated or radar estimated rainfall and stratiform amount is in good agreement with that diagnostically determined for all three periods. However, less rainfall and a smaller stratiform percentage estimated by radar resulted in a weaker (underestimated) latent heating profile and a lower maximum latent heating level compared to those determined diagnostically. Rainfall information from SSM/I can not retrieve individual convective events due to poor temporal sampling. Nevertheless, this study suggests that a good 4r, rainfall retrieval from SSM/I for a convective event always leads to a good latent heating retrieval. Sensitivity testing has been performed and the results indicate that the SSM/I derived time averaged stratiform amount may be underestimated for December 19-27. Time averaged heating profiles derived from SSM/I, however, are not in bad agreement with those derived by soundings for the December 10-17 convective period. The heating retrievals may be more accurate for longer time scales provided there is no bias in the sampling.

Macrophages Are the Major Reservoir of Latent Murine Gammaherpesvirus 68 in Peritoneal Cells

PubMed Central

Weck, Karen E.; Kim, Susanne S.; Virgin, Herbert W.; Speck, Samuel H.

1999-01-01

B cells have previously been identified as the major hematopoietic cell type harboring latent gammaherpesvirus 68 (γHV68) (N. P. Sunil-Chandra, S. Efstathiou, and A. A. Nash, J. Gen. Virol. 73:3275–3279, 1992). However, we have shown that γHV68 efficiently establishes latency in B-cell-deficient mice (K. E. Weck, M. L. Barkon, L. I. Yoo, S. H. Speck, and H. W. Virgin, J. Virol. 70:6775–6780, 1996), demonstrating that B cells are not required for γHV68 latency. To understand this dichotomy, we determined whether hematopoietic cell types, in addition to B cells, carry latent γHV68. We observed a high frequency of cells that reactivate latent γHV68 in peritoneal exudate cells (PECs) derived from both B-cell-deficient and normal C57BL/6 mice. PECs were composed primarily of macrophages in B-cell-deficient mice and of macrophages plus B cells in normal C57BL/6 mice. To determine which cells in PECs from C57BL/6 mice carry latent γHV68, we developed a limiting-dilution PCR assay to quantitate the frequency of cells carrying the γHV68 genome in fluorescence-activated cell sorter-purified cell populations. We also quantitated the contribution of individual cell populations to the total frequency of cells carrying latent γHV68. At early times after infection, the frequency of PECs that reactivated γHV68 correlated very closely with the frequency of PECs carrying the γHV68 genome, validating measurement of the frequency of viral-genome-positive cells as a measure of latency in this cell population. F4/80-positive macrophage-enriched, lymphocyte-depleted PECs harbored most of the γHV68 genome and efficiently reactivated γHV68, while CD19-positive, B-cell-enriched PECs harbored about a 10-fold lower frequency of γHV68 genome-positive cells. CD4-positive, T-cell-enriched PECs contained only a very low frequency of γHV68 genome-positive cells, consistent with previous analyses indicating that T cells are not a reservoir for γHV68 latency (N. P. Sunil-Chandra, S. Efstathiou, and A. A. Nash, J. Gen. Virol. 73:3275–3279, 1992). Since macrophages are bone marrow derived, we determined whether elicitation of a large inflammatory response in the peritoneum would recruit additional latent cells into the peritoneum. Thioglycolate inoculation increased the total number of PECs by about 20-fold but did not affect the frequency of cells that reactivate γHV68, consistent with a bone marrow reservoir for latent γHV68. These experiments demonstrate γHV68 latency in two different hematopoietic cell types, F4/80-positive macrophages and CD19-positive B cells, and argue for a bone marrow reservoir for latent γHV68. PMID:10074181

Development of fraction comparison strategies: A latent transition analysis.

PubMed

Rinne, Luke F; Ye, Ai; Jordan, Nancy C

2017-04-01

The present study investigated the development of fraction comparison strategies through a longitudinal analysis of children's responses to a fraction comparison task in 4th through 6th grades (N = 394). Participants were asked to choose the larger value for 24 fraction pairs blocked by fraction type. Latent class analysis of performance over item blocks showed that most children initially exhibited a "whole number bias," indicating that larger numbers in numerators and denominators produce larger fraction values. However, some children instead chose fractions with smaller numerators and denominators, demonstrating a partial understanding that smaller numbers can yield larger fractions. Latent transition analysis showed that most children eventually adopted normative comparison strategies. Children who exhibited a partial understanding by choosing fractions with smaller numbers were more likely to adopt normative comparison strategies earlier than those with larger number biases. Controlling for general math achievement and other cognitive abilities, whole number line estimation accuracy predicted the probability of transitioning to normative comparison strategies. Exploratory factor analyses showed that over time, children appeared to increasingly represent fractions as discrete magnitudes when simpler strategies were unavailable. These results support the integrated theory of numerical development, which posits that an understanding of numbers as magnitudes unifies the process of learning whole numbers and fractions. The findings contrast with conceptual change theories, which propose that children must move from a view of numbers as counting units to a new view that accommodates fractions to overcome whole number bias. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Exploring New Pathways in Precipitation Assimilation

NASA Technical Reports Server (NTRS)

Hou, Arthur; Zhang, Sara Q.

2004-01-01

Precipitation assimilation poses a special challenge in that the forward model for rain in a global forecast system is based on parameterized physics, which can have large systematic errors that must be rectified to use precipitation data effectively within a standard statistical analysis framework. We examine some key issues in precipitation assimilation and describe several exploratory studies in assimilating rainfall and latent heating information in NASA's global data assimilation systems using the forecast model as a weak constraint. We present results from two research activities. The first is the assimilation of surface rainfall data using a time-continuous variational assimilation based on a column model of the full moist physics. The second is the assimilation of convective and stratiform latent heating retrievals from microwave sensors using a variational technique with physical parameters in the moist physics schemes as a control variable. We will show the impact of assimilating these data on analyses and forecasts. Among the lessons learned are (1) that the time-continuous application of moisture/temperature tendency corrections to mitigate model deficiencies offers an effective strategy for assimilating precipitation information, and (2) that the model prognostic variables must be allowed to directly respond to an improved rain and latent heating field within an analysis cycle to reap the full benefit of assimilating precipitation information. of microwave radiances versus retrieval information in raining areas, and initial efforts in developing ensemble techniques such as Kalman filter/smoother for precipitation assimilation. Looking to the future, we discuss new research directions including the assimilation

Modeling Learning and Memory Using Verbal Learning Tests: Results From ACTIVE

PubMed Central

Gross, Alden L.

2013-01-01

Objective. To investigate the influence of memory training on initial recall and learning. Method. The Advanced Cognitive Training for Independent and Vital Elderly study of community-dwelling adults older than age 65 (n = 1,401). We decomposed trial-level recall in the Auditory Verbal Learning Test (AVLT) and Hopkins Verbal Learning Test (HVLT) into initial recall and learning across trials using latent growth models. Results. Trial-level increases in words recalled in the AVLT and HVLT at each follow-up visit followed an approximately logarithmic shape. Over the 5-year study period, memory training was associated with slower decline in Trial 1 AVLT recall (Cohen’s d = 0.35, p = .03) and steep pre- and posttraining acceleration in learning (d = 1.56, p < .001). Findings were replicated using the HVLT (decline in initial recall, d = 0.60, p = .01; pre- and posttraining acceleration in learning, d = 3.10, p < .001). Because of the immediate training boost, the memory-trained group had a higher level of recall than the control group through the end of the 5-year study period despite faster decline in learning. Discussion. This study contributes to the understanding of the mechanisms by which training benefits memory and expands current knowledge by reporting long-term changes in initial recall and learning, as measured from growth models and by characterization of the impact of memory training on these components. Results reveal that memory training delays the worsening of memory span and boosts learning. PMID:22929389

Modeling learning and memory using verbal learning tests: results from ACTIVE.

PubMed

Gross, Alden L; Rebok, George W; Brandt, Jason; Tommet, Doug; Marsiske, Michael; Jones, Richard N

2013-03-01

To investigate the influence of memory training on initial recall and learning. The Advanced Cognitive Training for Independent and Vital Elderly study of community-dwelling adults older than age 65 (n = 1,401). We decomposed trial-level recall in the Auditory Verbal Learning Test (AVLT) and Hopkins Verbal Learning Test (HVLT) into initial recall and learning across trials using latent growth models. Trial-level increases in words recalled in the AVLT and HVLT at each follow-up visit followed an approximately logarithmic shape. Over the 5-year study period, memory training was associated with slower decline in Trial 1 AVLT recall (Cohen's d = 0.35, p = .03) and steep pre- and posttraining acceleration in learning (d = 1.56, p < .001). Findings were replicated using the HVLT (decline in initial recall, d = 0.60, p = .01; pre- and posttraining acceleration in learning, d = 3.10, p < .001). Because of the immediate training boost, the memory-trained group had a higher level of recall than the control group through the end of the 5-year study period despite faster decline in learning. This study contributes to the understanding of the mechanisms by which training benefits memory and expands current knowledge by reporting long-term changes in initial recall and learning, as measured from growth models and by characterization of the impact of memory training on these components. Results reveal that memory training delays the worsening of memory span and boosts learning.

Navigating complex decision spaces: Problems and paradigms in sequential choice

PubMed Central

Walsh, Matthew M.; Anderson, John R.

2015-01-01

To behave adaptively, we must learn from the consequences of our actions. Doing so is difficult when the consequences of an action follow a delay. This introduces the problem of temporal credit assignment. When feedback follows a sequence of decisions, how should the individual assign credit to the intermediate actions that comprise the sequence? Research in reinforcement learning provides two general solutions to this problem: model-free reinforcement learning and model-based reinforcement learning. In this review, we examine connections between stimulus-response and cognitive learning theories, habitual and goal-directed control, and model-free and model-based reinforcement learning. We then consider a range of problems related to temporal credit assignment. These include second-order conditioning and secondary reinforcers, latent learning and detour behavior, partially observable Markov decision processes, actions with distributed outcomes, and hierarchical learning. We ask whether humans and animals, when faced with these problems, behave in a manner consistent with reinforcement learning techniques. Throughout, we seek to identify neural substrates of model-free and model-based reinforcement learning. The former class of techniques is understood in terms of the neurotransmitter dopamine and its effects in the basal ganglia. The latter is understood in terms of a distributed network of regions including the prefrontal cortex, medial temporal lobes cerebellum, and basal ganglia. Not only do reinforcement learning techniques have a natural interpretation in terms of human and animal behavior, but they also provide a useful framework for understanding neural reward valuation and action selection. PMID:23834192

Neural Responses to Injury: Prevention, Protection and Repair; Volume 3: The Neuro-Immunology of Stress, Injury and Infection

DTIC Science & Technology

1996-10-01

thermal stress (10 minutes at 43 °C) and restraint stress (60 minutes) as indirect mediators of HSV-1 reactivation from neural tissues . These experiments...between the reactivation of infectious virus in the tears, ocular tissue , and trigeminal ganglia of infected, stressed animals was significantly...and nervous tissues of latently infected, stressed animals. The overall goal of the experiments conducted as part of this specific aim are to

Why Do I Feel More Confident? Bandura's Sources Predict Preservice Teachers' Latent Changes in Teacher Self-Efficacy

PubMed Central

Pfitzner-Eden, Franziska

2016-01-01

Teacher self-efficacy (TSE) is associated with a multitude of positive outcomes for teachers and students. However, the development of TSE is an under-researched area. Bandura (1997) proposed four sources of self-efficacy: mastery experiences, vicarious experiences, verbal persuasion, and physiological and affective states. This study introduces a first instrument to assess the four sources for TSE in line with Bandura's conception. Gathering evidence of convergent validity, the contribution that each source made to the development of TSE during a practicum at a school was explored for two samples of German preservice teachers. The first sample (N = 359) were beginning preservice teachers who completed an observation practicum. The second sample (N = 395) were advanced preservice teachers who completed a teaching practicum. The source measure showed good reliability, construct validity, and convergent validity. Latent true change modeling was applied to explore how the sources predicted changes in TSE. Three different models were compared. As expected, results showed that TSE changes in both groups were significantly predicted by mastery experiences, with a stronger relationship in the advanced group. Further, the results indicated that mastery experiences were largely informed by the other three sources to varying degrees depending on the type of practicum. Implications for the practice of teacher education are discussed in light of the results. PMID:27807422

Why Do I Feel More Confident? Bandura's Sources Predict Preservice Teachers' Latent Changes in Teacher Self-Efficacy.

PubMed

Pfitzner-Eden, Franziska

2016-01-01

Teacher self-efficacy (TSE) is associated with a multitude of positive outcomes for teachers and students. However, the development of TSE is an under-researched area. Bandura (1997) proposed four sources of self-efficacy: mastery experiences, vicarious experiences, verbal persuasion, and physiological and affective states. This study introduces a first instrument to assess the four sources for TSE in line with Bandura's conception. Gathering evidence of convergent validity, the contribution that each source made to the development of TSE during a practicum at a school was explored for two samples of German preservice teachers. The first sample ( N = 359) were beginning preservice teachers who completed an observation practicum. The second sample ( N = 395) were advanced preservice teachers who completed a teaching practicum. The source measure showed good reliability, construct validity, and convergent validity. Latent true change modeling was applied to explore how the sources predicted changes in TSE. Three different models were compared. As expected, results showed that TSE changes in both groups were significantly predicted by mastery experiences, with a stronger relationship in the advanced group. Further, the results indicated that mastery experiences were largely informed by the other three sources to varying degrees depending on the type of practicum. Implications for the practice of teacher education are discussed in light of the results.