Competitive dynamics for behavior coordination in a joint transportation task

We address the problem of coordinating two non-holonomic mobile robots that move in formation while transporting a long payload. A competitive dynamics is introduced that gradually controls the activation and deactivation of individual behaviors. This process introduces (asymmetrical) hysteresis during behavioral switching. As a result behavioral oscillations, due to noisy information, are eliminated. Results in indoor environments show that if parameter values are chosen within reasonable ranges then, in spite of noise in the robots communi- cation and sensors, the overall robotic system works quite well even in cluttered environments. The robots overt behavior is stable and smooth.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Differential effects of lead and zinc on inhibitory avoidance learning in mice

We studied the effects of chronic intoxication with the heavy metals lead (Pb2+) and zinc (Zn2+) on memory formation in mice. Animals were intoxicated through drinking water during the pre- and postnatal periods and then tested in the step-through inhibitory avoidance memory task. Chronic postnatal intoxication with Pb2+ did not change the step-through latency values recorded during the 4 weeks of the test (ANOVA, P>0.05). In contrast, mice intoxicated during the prenatal period showed significantly reduced latency values when compared to the control group (day 1: q = 4.62, P<0.05; day 7: q = 4.42, P<0.05; day 14: q = 5.65, P<0.05; day 21: q = 3.96, P<0.05, and day 28: q = 6.09, P<0.05). Although chronic postnatal intoxication with Zn2+ did not alter a memory retention test performed 24 h after training, we noticed a gradual decrease in latency at subsequent 4-week intervals (F = 3.07, P<0.05), an effect that was not observed in the control or in the Pb2+-treated groups. These results suggest an impairment of memory formation by Pb2+ when the animals are exposed during the critical period of neurogenesis, while Zn2+ appears to facilitate learning extinction.

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Efficient mining of discriminative molecular fragments

Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

Within-person analyses of situational interest and boredom: Interactions between task-specific perceptions and achievement goals

Despite the increasing number of studies examining the correlates of interest and boredom, surprisingly little research has focused on within-person fluctuations in these emotions, making it difficult to describe their situational nature. To address this gap in the literature, this study conducted repeated measurements (12 times) on a sample of 158 undergraduate students using a variety of self-report assessments, and examined the within-person relationships between task-specific perceptions (expectancy, utility, and difficulty) and interest and boredom. This study further explored the role of achievement goals in predicting between-person differences in these within-person relationships. Utilizing hierarchical-linear modeling, we found that, on average, a higher perception of both expectancy and utility, as well as a lower perception of difficulty, was associated with higher interest and lower boredom levels within individuals. Moreover, mastery-approach goals weakened the negative within-person relationship between difficulty and interest and the negative within-person relationship between utility and boredom. Mastery-avoidance and performance-avoidance goals strengthened the negative relationship between expectancy and boredom. These results suggest how educators can more effectively instruct students with different types of goals, minimizing boredom and maximizing interest and learning.

EFFECT OF FENCAMFAMINE ON AVOIDANCE PERFORMANCES OF RATS

The effects of fencamfamine (1.0 and 5.0 mg/kg, ip, single dose) on an inhibitory task were studied in rats (N = 15 per group). Post-training treatment with fencamfamine (1.0 mg/kg) significantly increased avoidance latency from 23 +/- 3 to 146 +/- 28 and 170 +/- 33 s for training day 1 and day 7, respectively, indicating an enhancement of retention. However, retention was significantly reduced with a high dose of fencamfamine (5.0 mg/kg). These results demonstrate that fencamfamine caused a reproducible dose-related increase and reduction in avoidance latency.

Contrasting effects of acute and chronic treatment with imipramine and fluoxetine on inhibitory avoidance and escape responses in mice exposed to the elevated T-maze

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Hypervigilance-avoidance pattern in spider phobia

Cognitive-motivational theories of phobias propose that patients' behavior is characterized by a hypervigilance-avoidance pattern. This implies that phobics initially direct their attention towards fear-relevant stimuli, followed by avoidance that is thought to prevent objective evaluation and habituation. However, previous experiments with highly anxious individuals confirmed initial hypervigilance and yet failed to show subsequent avoidance. In the present study, we administered a visual task in spider phobics and controls, requiring participants to search for spiders. Analyzing eye movements during visual exploration allowed the examination of spatial as well as temporal aspects of phobic behavior. Confirming the hypervigilance-avoidance hypothesis as a whole, our results showed that, relative to controls, phobics detected spiders faster, fixated closer to spiders during the initial search phase and fixated further from spiders subsequently.

Failure cue priming and impaired cognitive performance—analyses of avoidance motivation as a mediator and fear of failure as a moderator.

The present research investigates whether and how learned symbols for failure reduce task performance. We tested the effect of number priming in two countries with different learning histories for numbers. Priming numbers associated with failure (6 in Germany and 1 in Switzerland) were hypothesized to reduce performance. As expected, in Switzerland, priming with the failure number 1 reduced performance (Study 1), whereas in Germany, priming with the failure number 6 impaired performance in analogy tasks (Study 2). Study 2 additionally analyzed the mechanism and showed that the relationship between failure number priming and performance was mediated by evoked avoidance motivation and that dispositional fear of failure moderated this mediation.

Eye movements discriminate fatigue due to chronotypical factors and time spent on task--a double dissociation

Systematic differences in circadian rhythmicity are thought to be a substantial factor determining inter-individual differences in fatigue and cognitive performance. The synchronicity effect (when time of testing coincides with the respective circadian peak period) seems to play an important role. Eye movements have been shown to be a reliable indicator of fatigue due to sleep deprivation or time spent on cognitive tasks. However, eye movements have not been used so far to investigate the circadian synchronicity effect and the resulting differences in fatigue. The aim of the present study was to assess how different oculomotor parameters in a free visual exploration task are influenced by: a) fatigue due to chronotypical factors (being a 'morning type' or an 'evening type'); b) fatigue due to the time spent on task. Eighteen healthy participants performed a free visual exploration task of naturalistic pictures while their eye movements were recorded. The task was performed twice, once at their optimal and once at their non-optimal time of the day. Moreover, participants rated their subjective fatigue. The non-optimal time of the day triggered a significant and stable increase in the mean visual fixation duration during the free visual exploration task for both chronotypes. The increase in the mean visual fixation duration correlated with the difference in subjectively perceived fatigue at optimal and non-optimal times of the day. Conversely, the mean saccadic speed significantly and progressively decreased throughout the duration of the task, but was not influenced by the optimal or non-optimal time of the day for both chronotypes. The results suggest that different oculomotor parameters are discriminative for fatigue due to different sources. A decrease in saccadic speed seems to reflect fatigue due to time spent on task, whereas an increase in mean fixation duration a lack of synchronicity between chronotype and time of the day.

Preserved obstacle avoidance during reaching in patients with left visual neglect

We asked 12 patients with left visual neglect to bisect the gap between two cylinders or to reach rapidly between them to a more distal target zone. Both tasks demanded a motor response but these responses were quite different in nature. The bisection response was a communicative act whereby the patient indicated the perceived midpoint. The reaching task carried no imperative to bisect the gap, only to maintain a safe distance from either cylinder while steering to the target zone. Optimal performance on either task could only be achieved by reference to the location of both cylinders. Our analysis focused upon the relative influence of the left and right cylinders on the lateral location of the response. In the bisection task, all neglect patients showed qualitatively the same asymmetry, with the left cylinder exerting less influence than the right. In the reaching task, the neglect group behaved like normal subjects, being influenced approximately equally by the two cylinders. This was true for all bar two of the patients, who showed clear neglect in both tasks. We conclude that the visuomotor processing underlying obstacle avoidance during reaching is preserved in most patients with left visual neglect. (C) 2004 Elsevier Ltd. All rights reserved.

Goal orientation, ability and task performance: A moderated analysis

Achievement goal orientation represents an individual's general approach to an achievement situation, and has important implications for how individuals react to novel, challenging tasks. However, theorists such as Yeo and Neal (2004) have suggested that the effects of goal orientation may emerge over time. Bell and Kozlowski (2002) have further argued that these effects may be moderated by individual ability. The current study tested the dynamic effects of a new 2x2 model of goal orientation (mastery/performance x approach/avoidance) on performance on a simulated air traffic control (ATC) task, as moderated by dynamic spatial ability. One hundred and one first-year participants completed a self-report goal orientation measure and computerbased dynamic spatial ability test and performed 30 trials of an ATC task. Hypotheses were tested using a two-level hierarchical linear model. Mastery-approach orientation was positively related to task performance, although no interaction with ability was observed. Performance-avoidance orientation was negatively related to task performance; this association was weaker at high levels of ability. Theoretical and practical implications will be discussed.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.