121 research outputs found
Efficient DMP generalization to time-varying targets, external signals and via-points
Dynamic Movement Primitives (DMP) have found remarkable applicability and
success in various robotic tasks, which can be mainly attributed to their
generalization and robustness properties. Nevertheless, their generalization is
based only on the trajectory endpoints (initial and target position). Moreover,
the spatial generalization of DMP is known to suffer from shortcomings like
over-scaling and mirroring of the motion. In this work we propose a novel
generalization scheme, based on optimizing online the DMP weights so that the
acceleration profile and hence the underlying training trajectory pattern is
preserved. This approach remedies the shortcomings of the classical DMP scaling
and additionally allows the DMP to generalize also to intermediate points
(via-points) and external signals (coupling terms), while preserving the
training trajectory pattern. Extensive comparative simulations with the
classical and other DMP variants are conducted, while experimental results
validate the applicability and efficacy of the proposed method
From RGB images to Dynamic Movement Primitives for planar tasks
DMP have been extensively applied in various robotic tasks thanks to their
generalization and robustness properties. However, the successful execution of
a given task may necessitate the use of different motion patterns that take
into account not only the initial and target position but also features
relating to the overall structure and layout of the scene. To make DMP
applicable to a wider range of tasks and further automate their use, we design
a framework combining deep residual networks with DMP, that can encapsulate
different motion patterns of a planar task, provided through human
demonstrations on the RGB image plane. We can then automatically infer from new
raw RGB visual input the appropriate DMP parameters, i.e. the weights that
determine the motion pattern and the initial/target positions. We compare our
method against another SoA method for inferring DMP from images and carry out
experimental validations in two different planar tasks
Generalized h-index for Disclosing Latent Facts in Citation Networks
What is the value of a scientist and its impact upon the scientific thinking?
How can we measure the prestige of a journal or of a conference? The evaluation
of the scientific work of a scientist and the estimation of the quality of a
journal or conference has long attracted significant interest, due to the
benefits from obtaining an unbiased and fair criterion. Although it appears to
be simple, defining a quality metric is not an easy task. To overcome the
disadvantages of the present metrics used for ranking scientists and journals,
J.E. Hirsch proposed a pioneering metric, the now famous h-index. In this
article, we demonstrate several inefficiencies of this index and develop a pair
of generalizations and effective variants of it to deal with scientist ranking
and with publication forum ranking. The new citation indices are able to
disclose trendsetters in scientific research, as well as researchers that
constantly shape their field with their influential work, no matter how old
they are. We exhibit the effectiveness and the benefits of the new indices to
unfold the full potential of the h-index, with extensive experimental results
obtained from DBLP, a widely known on-line digital library.Comment: 19 pages, 17 tables, 27 figure
Anxiety and depression severity in neuropsychiatric SLE are associated with perfusion and functional connectivity changes of the frontolimbic neural circuit: a resting-state f(unctional) MRI study.
peer reviewed[en] OBJECTIVE: To examine the hypothesis that perfusion and functional connectivity disturbances in brain areas implicated in emotional processing are linked to emotion-related symptoms in neuropsychiatric SLE (NPSLE).
METHODS: Resting-state fMRI (rs-fMRI) was performed and anxiety and/or depression symptoms were assessed in 32 patients with NPSLE and 18 healthy controls (HC). Whole-brain time-shift analysis (TSA) maps, voxel-wise global connectivity (assessed through intrinsic connectivity contrast (ICC)) and within-network connectivity were estimated and submitted to one-sample t-tests. Subgroup differences (high vs low anxiety and high vs low depression symptoms) were assessed using independent-samples t-tests. In the total group, associations between anxiety (controlling for depression) or depression symptoms (controlling for anxiety) and regional TSA or ICC metrics were also assessed.
RESULTS: Elevated anxiety symptoms in patients with NPSLE were distinctly associated with relatively faster haemodynamic response (haemodynamic lead) in the right amygdala, relatively lower intrinsic connectivity of orbital dlPFC, and relatively lower bidirectional connectivity between dlPFC and vmPFC combined with relatively higher bidirectional connectivity between ACC and amygdala. Elevated depression symptoms in patients with NPSLE were distinctly associated with haemodynamic lead in vmPFC regions in both hemispheres (lateral and medial orbitofrontal cortex) combined with relatively lower intrinsic connectivity in the right medial orbitofrontal cortex. These measures failed to account for self-rated, milder depression symptoms in the HC group.
CONCLUSION: By using rs-fMRI, altered perfusion dynamics and functional connectivity was found in limbic and prefrontal brain regions in patients with NPSLE with severe anxiety and depression symptoms. Although these changes could not be directly attributed to NPSLE pathology, results offer new insights on the pathophysiological substrate of psychoemotional symptomatology in patients with lupus, which may assist its clinical diagnosis and treatment
Converging evidence of impaired brain function in systemic lupus erythematosus: changes in perfusion dynamics and intrinsic functional connectivity.
peer reviewed[en] PURPOSE: Τhe study examined changes in hemodynamics and functional connectivity in patients with systemic lupus erythematosus (SLE) with or without neuropsychiatric manifestations.
METHODS: Participants were 44 patients with neuropsychiatric SLE (NPSLE), 20 SLE patients without such manifestations (non-NPSLE), and 35 healthy controls. Resting-state functional MRI (rs-fMRI) was used to obtain whole-brain maps of (a) perfusion dynamics derived through time shift analysis (TSA), (b) regional functional connectivity (intrinsic connectivity contrast (ICC) coefficients), and (c) hemodynamic-connectivity coupling. Group differences were assessed through independent samples t-tests, and correlations of rs-fMRI indices with clinical variables and neuropsychological test scores were, also, computed.
RESULTS: Compared to HC, NPSLE patients demonstrated intrinsic hypoconnectivity of anterior Default Mode Network (DMN) and hyperconnectivity of posterior DMN components. These changes were paralleled by elevated hemodynamic lag. In NPSLE, cognitive performance was positively related to higher intrinsic connectivity in these regions, and to higher connectivity-hemodynamic coupling in posterior DMN components. Uncoupling between hemodynamics and connectivity in the posterior DMN was associated with worse task performance. Non-NPSLE patients displayed hyperconnectivity in posterior DMN and sensorimotor regions paralleled by relatively increased hemodynamic lag.
CONCLUSION: Adaptation of regional brain function to hemodynamic changes in NPSLE may involve locally decreased or locally increased intrinsic connectivity (which can be beneficial for cognitive function). This process may also involve elevated coupling of hemodynamics with functional connectivity (beneficial for cognitive performance) or uncoupling, which may be detrimental for the cognitive skills of NPSLE patients
- …