Set Of Exemplars Research Articles

Quantitative Literacy and Affective Reactivity in Processing Statistical Information and Case Histories in the News

After exposure to statistical information and/or samplings of exemplifying cases in a news report on health risks, quantitative impressions and associated affective dispositions were examined in persons differing in arithmetic competence. Whereas the variation of such competence was without appreciable effect on incidence estimates, it markedly influenced affect-mediated assessments of empathy with victims, safety risks, and protective concerns. Specifically, exposure to sets of pertinent exemplars fostered higher assessments by persons of lower numeric ability than by persons of higher numeric ability. In addition, all presentations involving exemplars fostered stronger affective assessments than did the presentation of statistical data alone. The findings suggest that variation in numeric competence is associated with distinct differences in information processing that serves the formation of affect-mediated risk-related assessments. Implications for cognitive processing styles are considered and applied to the design of informative and persuasive media campaigns.

Analysis of activity in fMRI data using affinity propagation clustering

Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) data. The huge computation load, however, makes it difficult for the practical use. We use affinity propagation clustering (APC), a new clustering algorithm especially for large data sets to detect brain functional activation from fMRI. It considers all data points as possible exemplars through the minimisation of an energy function and message-passing architecture, and obtains the optimal set of exemplars and their corresponding clusters. Four simulation studies and three in vivo fMRI studies reveal that brain functional activation can be effectively detected and that different response patterns can be distinguished using this method. Our results demonstrate that APC is superior to the k-centres clustering, as revealed by their performance measures in the weighted Jaccard coefficient and average squared error. These results suggest that the proposed APC will be useful in detecting brain functional activation from fMRI data.

Semi-Automatic Tagging of Photo Albums via Exemplar Selection and Tag Inference

As one of the emerging Web 2.0 activities, tagging becomes a popular approach to manage personal media data, such as photo albums. A dilemma in tagging behavior is the users' manual efforts and the tagging accuracy: exhaustively tagging all photos in an album is labor-intensive and time-consuming, and simply entering tags for the whole album leads to unsatisfying results. In this paper, we propose a semi-automatic tagging scheme that aims to facilitate users in photo album tagging. The scheme is able to achieve a good trade-off between manual efforts and tagging accuracy as well as to adjust tagging performance according to the user's customization. For a given album, it first selects a set of representative exemplars for manual tagging via a temporally consistent affinity propagation algorithm, and the tags of the rest of the photos are automatically inferred. Then a constrained affinity propagation algorithm is applied to select a new set of exemplars for manual tagging in an incremental manner, based on which the performance of the tag inference in the previous round can be estimated. If the results are not satisfying enough, a further round of exemplar selection and tag inference will be implemented. This process repeats until satisfactory tagging results are achieved, and users can also stop the process at any time. Experimental results on real-world Flickr photo albums have demonstrated the effectiveness and usefulness of the proposed scheme.

A Model Comparison for Characterizing Protein Motions from Structure

To understand the extent structure plays in determining protein dynamics, a comparative study is made using three computational models that characterize native state dynamics starting from known protein structures taken from four distinct SCOP classifications. A geometrical simulation [1] (FRODA) is performed based on an initial rigid cluster decomposition using FIRST [2], and the results are compared to the commonly employed elastic network model (ANM) and molecular dynamics (MD) simulations. The essential dynamics is quantified by a direct analysis of a mode subspace constructed from ANM and a principal component analysis (PCA) on both the FRODA and MD trajectories using root mean square inner (RMSIP) product and principal angles (PA). Relative subspace sizes and overlaps are visualized using the projection of displacement vectors on the model modes. Additionally, a mode subspace is constructed from PCA on an exemplar set of X-ray crystal structures in order to determine similarly with respect to the generated ensembles. Our quantitative analysis reveals there is significant overlap across the three model subspaces and the model independent subspace. The subspaces generated from all three models were found to have high overlap for all four SCOP classes of proteins investigated, although FRODA provided the most robust sampling of the native basin. These results indicate that structure is the key determinant for native state dynamics. This work is supported by NIH grant 1R21HL093531.[1] Wells S, Menor S, Hespenheide B, Thorpe MF: Constrained geometric simulation of diffusive motion in proteins. Phys Biol, 2:S127-S136 (2005).[2] Jacobs DJ, Rader A, Kuhn LA, Thorpe MF: Graph Theory Predictions of Protein Flexibility. Proteins, 44:150-65 (2001).

Feature and Configuration in Face Processing: Japanese Are More Configural Than Americans

AbstractPrevious work suggests that Asians allocate more attention to configuration information than Caucasian Americans do. Yet this cultural variation has been found only with stimuli such as natural scenes and objects that require both feature‐ and configuration‐based processing. Here, we show that the cultural variation also exists in face perception—a domain that is typically viewed as configural in nature. When asked to identify a prototypic face for a set of disparate exemplars, Japanese were more likely than Caucasian Americans to use overall resemblance rather than feature matching. Moreover, in a speeded identity‐matching task, Japanese were more accurate than Americans in identifying the spatial configuration of features (e.g., eyes). Together, these findings underscore the robustness of culture’s influences on cognition.

On the computational challenges of analogy-based generalization

One prominent account of concept and category learning is that concepts and categories (jointly referred to as ‘generalizations’) are abstractions derived by extracting common analogical structure from sets of exemplars. This account enjoys considerable empirical support and has informed the design of models of learning in artificial intelligence. One aspect of the theory that has received little attention to date is the computational tractability of the processes that it postulates. In this paper, we assess the (in)tractability of analogy-based generalization using proof techniques from computational complexity theory. Our results reveal some unique computational challenges for analogy-based generalization, which it seems need to be addressed before the account can claim cognitive plausibility.

Solid-Texture Synthesis: A Survey

Solid textures are an efficient way to compactly represent 3D objects' external and internal appearance, providing practical advantages over classic 2D texturing. Two main methods exist for synthesizing solid textures. Procedural methods obtain colors through functions that algorithmically encode the texture's appearance and structural properties. Example-based methods capture and replicate the appearance as described by a set of input exemplars. These methods can also be classified as boundary independent or boundary dependent. For boundary-independent methods, the shape of the object to be textured is irrelevant, and texture information can be freely generated for each point in the space. Boundary-dependent methods conform the synthesis process to the object's actual shape so that they can exploit this information to orient and guide texture generation. This article reviews the different methodologies' strengths and weaknesses, the classes of appearances they can successfully synthesize, and failure cases. In particular, it focuses on boundary-independent methods' advantages and drawbacks compared to boundary-dependent methods.

OBJCUT: Efficient Segmentation Using Top-Down and Bottom-Up Cues

We present a probabilistic method for segmenting instances of a particular object category within an image. Our approach overcomes the deficiencies of previous segmentation techniques based on traditional grid conditional random fields (CRF), namely that 1) they require the user to provide seed pixels for the foreground and the background and 2) they provide a poor prior for specific shapes due to the small neighborhood size of grid CRF. Specifically, we automatically obtain the pose of the object in a given image instead of relying on manual interaction. Furthermore, we employ a probabilistic model which includes shape potentials for the object to incorporate top-down information that is global across the image, in addition to the grid clique potentials which provide the bottom-up information used in previous approaches. The shape potentials are provided by the pose of the object obtained using an object category model. We represent articulated object categories using a novel layered pictorial structures model. Nonarticulated object categories are modeled using a set of exemplars. These object category models have the advantage that they can handle large intraclass shape, appearance, and spatial variation. We develop an efficient method, OBJCUT, to obtain segmentations using our probabilistic framework. Novel aspects of this method include: 1) efficient algorithms for sampling the object category models of our choice and 2) the observation that a sampling-based approximation of the expected log-likelihood of the model can be increased by a single graph cut. Results are presented on several articulated (e.g., animals) and nonarticulated (e.g., fruits) object categories. We provide a favorable comparison of our method with the state of the art in object category specific image segmentation, specifically the methods of Leibe and Schiele and Schoenemann and Cremers.

Exemplar-Based Policy with Selectable Strategies and its Optimization Using GA

As an approach for dynamic control problems and decision making problems, usually formulated as Markov Decision Processes (MDPs), we focus direct policy search (DPS), where a policy is represented by a model with parameters, and the parameters are optimized so as to maximize the evaluation function by applying the parameterized policy to the problem. In this paper, a novel framework for DPS, an exemplar-based policy optimization using genetic algorithm (EBP-GA) is presented and analyzed. In this approach, the policy is composed of a set of virtual exemplars and a case-based action selector, and the set of exemplars are selected and evolved by a genetic algorithm. Here, an exemplar is a real or virtual, free-styled and suggestive information such as ``take the action A at the state S'' or ``the state S1 is better to attain than S2''. One advantage of EBP-GA is the generalization and localization ability for policy expression, based on case-based reasoning methods. Another advantage is that both the introduction of prior knowledge and the extraction of knowledge after optimization are relatively straightforward. These advantages are confirmed through the proposal of two new policy expressions, experiments on two different problems and their analysis.

Band selection for hyperspectral imagery using affinity propagation

Hyperspectral imagery generally contains enormous amounts of data because of hundreds of spectral bands. Band selection is often adopted to reduce computational cost and accelerate knowledge discovery and other tasks such as subsequent classification. An exemplar-based clustering algorithm termed affinity propagation for band selection is proposed. Affinity propagation is derived from factor graph, and operates by initially considering all data points as potential cluster centres (exemplars) and then exchanging messages between data points until a good set of exemplars and clusters emerges. Affinity propagation has been applied to computer vision and bioinformatics, and shown to be much faster than other clustering methods for large data. By combining the information about the discriminative capability of each individual band and the correlation/similarity between bands, the exemplars generated by affine propagation have higher importance and less correlation/similarity. The performance of band selection is evaluated through a pixel image classification task. Experimental results demonstrate that, compared with some popular band selection methods, the bands selected by affinity propagation best characterise the hyperspectral imagery from the pixel classification standpoint.

Stimulus set size and statistical coverage of the grammar in artificial grammar learning

Adults and children acquire knowledge of the structure of their environment on the basis of repeated exposure to samples of structured stimuli. In the study of inductive learning, a straightforward issue is how much sample information is needed to learn the structure. The present study distinguishes between two measures for the amount of information in the sample: set size and the extent to which the set of exemplars statistically covers the underlying structure. In an artificial grammar learning experiment, learning was affected by the sample's statistical coverage of the grammar, but not by its mere size. Our result suggests an alternative explanation of the set size effects on learning found in previous studies (McAndrews & Moscovitch, 1985; Meulemans & Van der Linden, 1997), because, as we argue, set size was confounded with statistical coverage in these studies.

Effects of explicit rules in learning to spell open- and closed-syllable words

Second graders ( N = 222; 7.7 years of age) practiced with open- and closed-syllable words in a computer-assisted training program and appropriate spelling rules were either explicitly provided during practice or not. Also, children practiced either with a small set of exemplars or with a large set; the latter condition was expected to promote the use of rules. A control group consisting of 209 second graders, who did not practice in the computer-assisted training program, was also included in the study. Learning effects of practice were established by testing the spelling of practiced words, as well as novel transfer words, in pre-, post-, and retention tests. The results in the posttest showed that with a large set of exemplars during practice explicitly provided spelling rules are supportive for learning to spell novel words but no further effects of explicit rules were found, either for practiced words or in the retention test.

Neural “Ignition”: Enhanced Activation Linked to Perceptual Awareness in Human Ventral Stream Visual Cortex

Human recognition performance is characterized by abrupt changes in perceptual states. Understanding the neuronal dynamics underlying such transitions could provide important insights into mechanisms of recognition and perceptual awareness. Here we examined patients monitored for clinical purposes with multiple subdural electrodes. The patients participated in a backward masking experiment in which pictures of various object categories were presented briefly followed by a mask. We recorded ECoG from 445 electrodes placed in 11 patients. We found a striking increase in gamma power (30-70 Hz) and evoked responses specifically associated with successful recognition. The enhanced activation occurred 150-200 ms after stimulus onset and consistently outlasted the stimulus presentation. We propose that the gamma and evoked potential activations reflect a rapid increase in recurrent neuronal activity that plays a critical role in the emergence of a recognizable visual percept in conscious awareness.

Automated sleep–wake staging combining robust feature extraction, artificial neural network classification, and flexible decision rules

AbstractThe classification of sleep–wake stages suffers from poor standardization in scoring criteria and heterogeneous conditioning of polysomnographic signals. To improve applicability of fully automated sleep staging, we have designed a formal classification framework to rigorously (1) select robust candidate features, (2) emulate artificial neural network classifiers, and (3) assign sleep–wake stages using flexible decision rules. An extensive database of 48 PSG records scored in 20 s epochs by two independent clinicians was used. A small subset of 2 s elementary epochs representative of each stages with unequivocal expert scores was selected to form a limited set of learning exemplars. From 16 statistical, spectral and non‐linear candidate features extracted in 2 s epochs from EEG and EMG signals, a sequential forward search selected an optimal set of five features with a 22% error rate. Multiple layer perceptrons were trained from this optimal feature set while classification accuracy was assessed using the unequivocal instance subset. A simple majority vote among 10 consecutive classifier outputs ensured a final scoring resolution comparable to that of the experts. Poor classification performance was obtained for movement time, wakefulness, and intermediate sleep stages with a 36±15% error rate (Cohen's kappa 0.48±0.18). In contrast, deep and paradoxical sleep was classified with an 82% accuracy not far from inter‐expert expert agreement (83±3%). Significant improvements should be expected using a larger learning set compensating for a high inter‐individual variability, and decision rules incorporating more domain‐knowledge. Copyright © 2009 John Wiley & Sons, Ltd.

The tight coupling between category and causal learning

The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two interconnected causal relations forming a causal chain (Experiment 1) or a common-cause model (Experiments 2a, b). One of the three events (i.e., the intermediate event of the chain, or the common cause) was presented as a set of uncategorized exemplars. Although participants were not provided with any feedback about category labels, they tended to induce categories in the first phase that maximized the predictability of their causes or effects. In the second causal learning phase, participants had the choice between transferring the newly learned categories from the first phase at the cost of suboptimal predictions, or they could induce a new set of optimally predictive categories for the second causal relation, but at the cost of proliferating different category schemes for the same set of events. It turned out that in all three experiments learners tended to transfer the categories entailed by the first causal relation to the second causal relation.

Skeletal Shape Abstraction from Examples

Learning a class prototype from a set of exemplars is an important challenge facing researchers in object categorization. Although the problem is receiving growing interest, most approaches assume a one-to-one correspondence among local features, restricting their ability to learn true abstractions of a shape. In this paper, we present a new technique for learning an abstract shape prototype from a set of exemplars whose features are in many-to-many correspondence. Focusing on the domain of 2D shape, we represent a silhouette as a medial axis graph whose nodes correspond to "parts" defined by medial branches and whose edges connect adjacent parts. Given a pair of medial axis graphs, we establish a many-to-many correspondence between their nodes to find correspondences among articulating parts. Based on these correspondences, we recover the abstracted medial axis graph along with the positional and radial attributes associated with its nodes. We evaluate the abstracted prototypes in the context of a recognition task.

Short Article: What is Learned about Fragments in Artificial Grammar Learning? A Transitional Probabilities Approach

Learning local regularities in sequentially structured materials is typically assumed to be based on encoding of the frequencies of these regularities. We explore the view that transitional probabilities between elements of chunks, rather than frequencies of chunks, may be the primary factor in artificial grammar learning (AGL). The transitional probability model (TPM) that we propose is argued to provide an adaptive and parsimonious strategy for encoding local regularities in order to induce sequential structure from an input set of exemplars of the grammar. In a variant of the AGL procedure, in which participants estimated the frequencies of bigrams occurring in a set of exemplars they had been exposed to previously, participants were shown to be more sensitive to local transitional probability information than to mere pattern frequencies.

The aquisition of sonorant voicing.

In the view of Exemplar Theory phonetic variants of words are stored by speakers in their memory as a set of exemplars organized into clusters and grow stronger or decay over time depending on language experience (Bybee, 2002). In Polish and French sonorant consonants in clusters are devoiced word finally, which does not occur in German and English because of phonotactical constraints. A study based on automatic speech alignment and voicing profile extraction from Polish, French, German, and English speech corpuses aims at defining voicing sonorant temporal structure and voicing profile acquisition. The hypothesis is that Polish and French native speakers transfer their L1 exemplars during L2 acquisition of English and German and thus devoice sonorant. After extracting voicing profiles from recordings of native Polish and French speakers speaking English and German as an L2/L3, comparison of voicing profiles of English and German native speakers will be made in order to define the exemplar transfer.

Literature, Culture and Language Learning

Literature, Culture and Language Learning

What is modern biogeography without phylogeography?

What is modern biogeography without phylogeography?