108 research outputs found
Cardiovascular magnetic resonance of myocardial infarction after blunt chest trauma: a heartbreaking soccer-shot
Cardiac injury occasionally occurs as a result of blunt chest trauma. Most cardiac complications in chest trauma are due to myocardial contusion rather than direct damage to the coronary arteries. However, traumatic coronary injury has been reported, and a variety of underlying pathophysiological mechanisms have been proposed. We present a 26 year old patient presenting with an acute coronary syndrome as a consequence of a soccer-shot impact to the chest. CMR showed apical inferior infarction, as well as multiple small septal lesions which were presumed to have resulted from embolization. The culprit lesion was a proximal 75% LAD stenosis with a prominent plaque-rupture and thrombus-formation, and the distal LAD was occluded by thromboembolic material
Threshold effects in excited charmed baryon decays
Motivated by recent results on charmed baryons from CLEO and FOCUS, we
reexamine the couplings of the orbitally excited charmed baryons. Due to its
proximity to the [Sigma_c pi] threshold, the strong decays of the
Lambda_c(2593) are sensitive to finite width effects. This distorts the shape
of the invariant mass spectrum in Lambda_{c1}-> Lambda_c pi^+pi^- from a simple
Breit-Wigner resonance, which has implications for the experimental extraction
of the Lambda_c(2593) mass and couplings. We perform a fit to unpublished CLEO
data which gives M(Lambda_c(2593)) - M(Lambda_c) = 305.6 +- 0.3 MeV and h2^2 =
0.24^{+0.23}_{-0.11}, with h2 the Lambda_{c1}-> Sigma_c pi strong coupling in
the chiral Lagrangian. We also comment on the new orbitally excited states
recently observed by CLEO.Comment: 9 pages, 3 figure
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition
Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for Image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on
massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted
spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid
model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including
recent deep models trained on millions of manually labelled images and videos
Online Continual Learning on Sequences
Online continual learning (OCL) refers to the ability of a system to learn
over time from a continuous stream of data without having to revisit previously
encountered training samples. Learning continually in a single data pass is
crucial for agents and robots operating in changing environments and required
to acquire, fine-tune, and transfer increasingly complex representations from
non-i.i.d. input distributions. Machine learning models that address OCL must
alleviate \textit{catastrophic forgetting} in which hidden representations are
disrupted or completely overwritten when learning from streams of novel input.
In this chapter, we summarize and discuss recent deep learning models that
address OCL on sequential input through the use (and combination) of synaptic
regularization, structural plasticity, and experience replay. Different
implementations of replay have been proposed that alleviate catastrophic
forgetting in connectionists architectures via the re-occurrence of (latent
representations of) input sequences and that functionally resemble mechanisms
of hippocampal replay in the mammalian brain. Empirical evidence shows that
architectures endowed with experience replay typically outperform architectures
without in (online) incremental learning tasks.Comment: L. Oneto et al. (eds.), Recent Trends in Learning From Data, Studies
in Computational Intelligence 89
Negative Parity 70-plet Baryon Masses in the 1/Nc Expansion
The masses of the negative parity SU(6) 70-plet baryons are analyzed in the
1/Nc expansion to order 1/Nc and to first order in SU(3) breaking. At this
level of precision there are twenty predictions. Among them there are the well
known Gell-Mann Okubo and equal spacing relations, and four new relations
involving SU(3) breaking splittings in different SU(3) multiplets. Although the
breaking of SU(6) symmetry occurs at zeroth order in 1/Nc, it turns out to be
small. The dominant source of the breaking is the hyperfine interaction which
is of order 1/Nc. The spin-orbit interaction, of zeroth order in 1/Nc, is
entirely fixed by the splitting between the singlet states Lambda(1405) and
Lambda(1520), and the spin-orbit puzzle is solved by the presence of other
zeroth order operators involving flavor exchange.Comment: 31 pages, 3 figure
Association of TP53 codon 72 polymorphism and the outcome of adjuvant therapy in breast cancer patients
Identification of cardiomyopathy associated circulating miRNA biomarkers in patients with muscular dystrophy using a complementary cardiovascular magnetic resonance and plasma profiling approach
An Efficient Human Activity Recognition Technique Based on Deep Learning
In this paper, we present a new deep learning-based human activity recognition technique. First, we track and extract human body from each frame of the video stream. Next, we abstract human silhouettes and use them to create binary space-time maps (BSTMs) which summarize human activity within a defined time interval. Finally, we use convolutional neural network (CNN) to extract features from BSTMs and classify the activities. To evaluate our approach, we carried out several tests using three public datasets: Weizmann, Keck Gesture and KTH Database. Experimental results show that our technique outperforms conventional state-of-the-art methods in term of recognition accuracy and provides comparable performance against recent deep learning techniques. It’s simple to implement, requires less computing power, and can be used for multi-subject activity recognition
Review of journal of cardiovascular magnetic resonance 2010
There were 75 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2010, which is a 34% increase in the number of articles since 2009. The quality of the submissions continues to increase, and the editors were delighted with the recent announcement of the JCMR Impact Factor of 4.33 which showed a 90% increase since last year. Our acceptance rate is approximately 30%, but has been falling as the number of articles being submitted has been increasing. In accordance with Open-Access publishing, the JCMR articles go on-line as they are accepted with no collating of the articles into sections or special thematic issues. Last year for the first time, the Editors summarized the papers for the readership into broad areas of interest or theme, which we felt would be useful to practitioners of cardiovascular magnetic resonance (CMR) so that you could review areas of interest from the previous year in a single article in relation to each other and other recent JCMR articles [1]. This experiment proved very popular with a very high rate of downloading, and therefore we intend to continue this review annually. The papers are presented in themes and comparison is drawn with previously published JCMR papers to identify the continuity of thought and publication in the journal. We hope that you find the open-access system increases wider reading and citation of your papers, and that you will continue to send your quality manuscripts to JCMR for publication
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