86,170 research outputs found
Dominance attributions following damage to the ventromedial prefrontal cortex
Damage to the human ventromedial prefrontal cortex (VM) can result in dramatic and maladaptive changes in social behavior despite preservation of most other cognitive abilities. One important aspect of social cognition is the ability to detect social dominance, a process of attributing from particular social signals another person's relative standing in the social world. To test the role of the VM in making attributions of social dominance, we designed two experiments: one requiring dominance judgments from static pictures of faces, the second requiring dominance judgments from film clips. We tested three demographically matched groups of subjects: subjects with focal lesions in the VM (n=15), brain-damaged comparison subjects with lesions excluding the VM (n=11), and a reference group of normal individuals with no history of neurological disease (n=32). Contrary to our expectation, we found that subjects with VM lesions gave dominance judgments on both tasks that did not differ significantly from those given by the other groups. Despite their grossly normal performance, however, subjects with VM lesions showed more subtle impairments specifically when judging static faces: They were less discriminative in their dominance judgments, and did not appear to make normal use of gender and age of the faces in forming their judgments. The findings suggest that, in the laboratory tasks we used, damage to the VM does not necessarily impair judgments of social dominance, although it appears to result in alterations in strategy that might translate into behavioral impairments in real life
Detailed plan for the COMET WP3 initial research activity - list of research projects and goals, participants and timing
Detailed plan for the COMET Work Package (WP) 3
Digging Deeper into Egocentric Gaze Prediction
This paper digs deeper into factors that influence egocentric gaze. Instead
of training deep models for this purpose in a blind manner, we propose to
inspect factors that contribute to gaze guidance during daily tasks. Bottom-up
saliency and optical flow are assessed versus strong spatial prior baselines.
Task-specific cues such as vanishing point, manipulation point, and hand
regions are analyzed as representatives of top-down information. We also look
into the contribution of these factors by investigating a simple recurrent
neural model for ego-centric gaze prediction. First, deep features are
extracted for all input video frames. Then, a gated recurrent unit is employed
to integrate information over time and to predict the next fixation. We also
propose an integrated model that combines the recurrent model with several
top-down and bottom-up cues. Extensive experiments over multiple datasets
reveal that (1) spatial biases are strong in egocentric videos, (2) bottom-up
saliency models perform poorly in predicting gaze and underperform spatial
biases, (3) deep features perform better compared to traditional features, (4)
as opposed to hand regions, the manipulation point is a strong influential cue
for gaze prediction, (5) combining the proposed recurrent model with bottom-up
cues, vanishing points and, in particular, manipulation point results in the
best gaze prediction accuracy over egocentric videos, (6) the knowledge
transfer works best for cases where the tasks or sequences are similar, and (7)
task and activity recognition can benefit from gaze prediction. Our findings
suggest that (1) there should be more emphasis on hand-object interaction and
(2) the egocentric vision community should consider larger datasets including
diverse stimuli and more subjects.Comment: presented at WACV 201
Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping
Acknowledgments We thank Johan Havelaar, Aeryon Labs Inc., AeronVironment Inc. and Aeronautics Inc. for kindly permitting the use of materials in Fig. 1.Peer reviewedPublisher PD
Evaluation Factors for Multi-Stakeholder Broadband Visual Communication Projects
This paper presents a summary of multifaceted
evaluation factors that we have
identified through our research with
Broadband Visual Communication (BVC)
projects involving multiple stakeholders. The
main benefit of these evaluation factors is
that they provide a general evaluation
framework for multiple stakeholder projects.
The factors are social infrastructure,
technical infrastructure, physical space,
interaction style and content
Design of multimedia processor based on metric computation
Media-processing applications, such as signal processing, 2D and 3D graphics
rendering, and image compression, are the dominant workloads in many embedded
systems today. The real-time constraints of those media applications have
taxing demands on today's processor performances with low cost, low power and
reduced design delay. To satisfy those challenges, a fast and efficient
strategy consists in upgrading a low cost general purpose processor core. This
approach is based on the personalization of a general RISC processor core
according the target multimedia application requirements. Thus, if the extra
cost is justified, the general purpose processor GPP core can be enforced with
instruction level coprocessors, coarse grain dedicated hardware, ad hoc
memories or new GPP cores. In this way the final design solution is tailored to
the application requirements. The proposed approach is based on three main
steps: the first one is the analysis of the targeted application using
efficient metrics. The second step is the selection of the appropriate
architecture template according to the first step results and recommendations.
The third step is the architecture generation. This approach is experimented
using various image and video algorithms showing its feasibility
Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence
IEEE Access
Volume 3, 2015, Article number 7217798, Pages 1512-1530
Open Access
Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article)
Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc
a Department of Information Engineering, University of Padua, Padua, Italy
b Department of General Psychology, University of Padua, Padua, Italy
c IRCCS San Camillo Foundation, Venice-Lido, Italy
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Abstract
In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network
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