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 We perform a fit to unpublished CLEO data which gives M(Lambda_c(2593)) - M(Lambda_c) = 305.6 +- 0.3 MeV and h2^2 = 0.24^{+0.23}_{-0.11}, with h2 the Lambda_{c1}-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 Deep learning, although a breakthrough for Image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets.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

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