Efficient Learning of Relational Object Class Models

We present an efficient method for learning part-based object class models from unsegmented images represented as sets of salient features. A model includes parts’ appearance, as well as location and scale relations between parts. The object class is generatively modeled using a simple Bayesian network with a central hidden node containing location and scale information, and nodes describing object parts. The model’s parameters, however, are optimized to reduce a loss function of the training error, as in discriminative methods. We show how boosting techniques can be extended to optimize the relational model proposed, with complexity linear in the number of parts and the number of features per image. This efficiency allows our method to learn relational models with many parts and features. The method has an advantage over purely generative and purely discriminative approaches for learning from sets of salient features, since generative method often use a small number of parts and features, while discriminative methods tend to ignore geometrical relations between parts. Experimental results are described, using some bench-mark data sets and three sets of newly collected data, showing the relative merits of our method in recognition and localization tasks.     

Multifactor,Sequentially Releasing Scaffolds for Tissue Engineering:Fabrication Using Solvent/Nonsolvent Sintering Technology

Biodegradable constructs, providing both mechanical support to growing tissues and timed release of biological agents, are highly desired in tissue engineering. This study aimed to develop a platform technology that responds to these challenges. Accordingly, we report herein on model systems in which microspheres of poly(suberic anhydride), containing all-trans retinoic acid (atRA), and poly(d,l -lactic acid-co-glycolic acid), containing bovine serum albumin (BSA), were co-sintered at room temperature, using a solvent/nonsolvent mixture. These scaffolds release about 60% of atRA and negligible amounts of BSA within the first five days, followed by slower and steady release of BSA. They have pores of 150–500 μm and a compressive modulus of 200 kPa. Myoblasts and fibroblasts were seeded on the loaded scaffolds and both showed enhanced proliferation rates. Based on sound thermodynamic principles of polymer science, this technology demonstrates an as yet unachieved degree of versatility. It allows for the tailoring of “intelligent” scaffolds that preserve the integrity of the incorporated agents and of advanced modalities to release various drugs in a scheduled manner.     

Cultural accommodation: Hybridity and the framing of social obligation.

Implications of cultural accommodation–hybridization were explored within the framework of individualism–collectivism. Individualism highlights the personal and centralizes individuals as the unit of analyses, whereas collectivism highlights the social and contextualizes individuals as parts of connected social units. In 2 experiments, the ways in which individualism, collectivism, and identity salience influence social obligation to diverse others was explored. The authors varied the personal goal interrupted (achievement–pleasure), the target (individual–group), and focus (in-group–larger society) of social obligation within subjects. The authors hypothesized that collectivism would increase obligation to the in-group when identity was made salient; that individualism alone would dampen social obligation; and that cultural accommodation–hybridization (being high in both individualism and collectivism) would increase obligation to larger society. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Cultural psychology, a new look: Reply to Bond (2002), Fiske (2002), Kitayama (2002), and Miller (2002).

M. H. Bond (2002) (see record 2002-00183-002), A. P. Fiske (2002) (see record 2002-00183-003), S. Kitayama (2002) (see record 2002-00183-004), and J. G. Miller (2002) (see record 2002-00183-005) joined D. Oyserman, H. M. Coon, and M. Kemmelmeier (2002) (see record 2002-00183-001) in highlighting limitations of the individualism-collectivism model of culture. Concern is warranted; nevertheless, individualism-collectivism helps structure discourse on the influence of culture on the mind. To avoid level-of-analysis entanglements, Oyserman et al propose an integrative model that includes distal, proximal, and situated cultural features of societies and internalized models of these features, highlights the importance of subjective construal, and uses evolutionary perspectives to clarify the basic problems cultures address. Framed this way, it is clear that, depending on situational requirements, both individualism- and collectivism-focused strategies are adaptive; thus, it is likely that human minds have adapted to think both ways. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The ability to manipulate behavior and to understand manipulation of beliefs: A comparison of individuals with autism, mental retardation, and normal development.

This study investigated the ability to deceive in participants with autism, mental retardation ( MR ), and normal development. The authors used S. Hala, M. Chandler, and A. S. Fritz's (see record, 78:20899) procedures, in which children deceive by creating false trails or by erasing all trails and lying about the true location of a hidden object. Participants with autism and those with MR did not differ in their ability to use a deceptive method to manipulate the behavior of another person. Participants with autism were significantly less able than participants with MR to understand that they manipulated the beliefs of another person by predicting the outcome of their deceptive act. The normal group outperformed the group with autism but not the group with MR on both parts of the task. Different possible interpretations of the results are discussed, including a deficit in theory of mind and a deficit in executive control functions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Minimal Decomposition of Model-Based Invariants

Model-based invariants are relations between model parameters and image measurements, which are independent of the imaging parameters. Such relations are true for all images of the model. Here we describe an algorithm which, given L independent model-based polynomial invariants describing some shape, will provide a linear re-parameterization of the invariants. This re-parameterization has the properties that: (i) it includes the minimal number of terms, and (ii) the shape terms are the same in all the model-based invariants. This final representation has 2 main applications: (1) it gives new representations of shape in terms of hyperplanes, which are convenient for object recognition; (2) it allows the design of new linear shape from motion algorithms. In addition, we use this representation to identify object classes that have universal invariants.     

Attachment working models and cognitive openness in close relationships: A test of chronic and temporary accessibility effects.

In 3 studies the authors explored the impact of chronic and temporary accessibility of attachment working models on cognitive openness in the realm of close relationships. In Study 1, participants rated their attachment style and perceptions of their romantic partner. In Study 2, they recalled a relationship defined by a particular attachment orientation and rated perceptions of the targeted partner. Study 3 was similar to Study 1, but secure attachment was primed for half of the participants. In all the studies, cognitive openness was assessed by changes in the perception of the partner after being exposed to information that disconfirmed expectations. Secure attachment was related to higher cognitive openness and better recall of expectation-incongruent information, mainly when this information was positively valued. The discussion emphasizes the implications of the chronic and contextual aspects of attachment working models for information processing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A comparative analysis of parallel programming language complexity and performance

Several parallel programming languages, libraries and environments have been developed to ease the task of writing programs for multiprocessors. Proponents of each approach often point out various language features that are designed to provide the programmer with a simple programming interface. However, virtually no data exist that quantitatively evaluate the relative ease of use of different parallel programming languages. The paper borrows techniques from the software engineering field to quantify the complexity of three predominant programming models: shared-memory, message-passing and high-performance Fortran. It is concluded that traditional software complexity metrics are effective indicators of the relative complexity of parallel programming languages. The impact of complexity on run-time performance is also discussed in the context of message-passing vs. HPF on an IBM SP2. © 1998 John Wiley & Sons, Ltd.     

A Content Analysis of Print News Coverage of Media Violence and Aggression Research

We conducted a content analysis of news articles (N = 540) to examine whether news coverage of media violence accurately reflects scientific knowledge about exposure to media violence and its effects on viewer aggression. The analysis revealed that over the past 30 years, news articles generally suggested that a link between media violence and aggression exists. However, the tone shifted sharply back toward a neutral conclusion since 2000. This shift may be attributable to the type of medium discussed (e.g., television vs. video games), the number of unaffiliated sources that are cited in the news article, and the sex of the journalist. Implications for how this news coverage may influence news readers are discussed.     

Evaluating an automated mediator for joint narratives in a conflict situation

ABSTRACT

Joint narratives are often used in the context of reconciliation interventions for people in social conflict situations, which arise, for example, due to ethnic or religious differences. The interventions aim to encourage a change in attitudes of the participants towards each other. Typically, a human mediator is fundamental for achieving a successful intervention. In this work, we present an automated approach to support remote interactions between pairs of participants as they contribute to a shared story in their own language. A key component is an automated cognitive tutor that guides the participants through a controlled escalation/de-escalation process during the development of a joint narrative. We performed a controlled study comparing a trained human mediator to the automated mediator. The results demonstrate that an automated mediator, although simple at this stage, effectively supports interactions and helps to achieve positive outcomes comparable to those attained by the trained human mediator.