Learning Scene Flow With Skeleton Guidance For 3D Action Recognition

Among the existing modalities for 3D action recognition, 3D flow has been poorly examined, although conveying rich motion information cues for human actions. Presumably, its susceptibility to noise renders it intractable, thus challenging the learning process within deep models. This work demonstrates the use of 3D flow sequence by a deep spatiotemporal model and further proposes an incremental two-level spatial attention mechanism, guided from skeleton domain, for emphasizing motion features close to the body joint areas and according to their informativeness. Towards this end, an extended deep skeleton model is also introduced to learn the most discriminant action motion dynamics, so as to estimate an informativeness score for each joint. Subsequently, a late fusion scheme is adopted between the two models for learning the high level cross-modal correlations. Experimental results on the currently largest and most challenging dataset NTU RGB+D, demonstrate the effectiveness of the proposed approach, achieving state-of-the-art results.Comment: 18 pages, 3 figures, 3 tables, conferenc

Multimodal Affective State Recognition in Serious Games Applications

A challenging research issue, which has recently attracted a lot of attention, is the incorporation of emotion recognition technology in serious games applications, in order to improve the quality of interaction and enhance the gaming experience. To this end, in this paper, we present an emotion recognition methodology that utilizes information extracted from multimodal fusion analysis to identify the affective state of players during gameplay scenarios. More specifically, two monomodal classifiers have been designed for extracting affective state information based on facial expression and body motion analysis. For the combination of different modalities a deep model is proposed that is able to make a decision about player’s affective state, while also being robust in the absence of one information cue. In order to evaluate the performance of our methodology, a bimodal database was created using Microsoft’s Kinect sensor, containing feature vectors extracted from users' facial expressions and body gestures. The proposed method achieved higher recognition rate in comparison with mono-modal, as well as early-fusion algorithms. Our methodology outperforms all other classifiers, achieving an overall recognition rate of 98.3%

Periodic Table of Nucleosynthesis

<p>Matter is made of atoms. One can reasonably wonder how and where have all these atoms created?</p> <p>Our quest to explain the origin of the elements started in the late 1950's by two famous papers independently - E. M. Burbidge <em>et al.</em>, Rev. Mod. Phys. <strong>29</strong>, 547 (1957) & A.G.W. Cameron, Pub. Astron. Soc. Pac. <strong>69</strong>, 201 (1957) - whose authors claimed that the elements are created in astrophysical environments.</p> <p>This is the well-known periodic table of elements, but each element is labeled by the environment that is created (e.g Supernova explosion etc.).</p> <p>The data concerning the enviroments were from NAU Meteorite Laboratory and the LaTeX code was modified. The original can be found here: http://www.texample.net/tikz/examples/periodic-table-of-chemical-elements/</p

GEANT3 Input File for DRAGON

GEANT3 Input File to test the BGO efficiency and the recoil transmission of DRAGON.<br

Panoramic Picture of DRAGON

A Panoramic Picture of DRAGON facility at the ISAC-I experimental hall of TRIUMF.<br

Experimental studies of cross sections and angular distributions of 112Cd(p,γ)113In with application in nucleosynthesis

<p>In the study of p-process for the explanation of the abundances of p-nuclei in our solar system a huge reaction network is involved ( >20.000 reaction with >2000 nuclei), which is computed theoretically using the Hauser Feshbach model (HF).</p> <p>Experimental cross section measurements of (p,γ), (α,γ) and (n,γ) reactions are necessary because they act as regulators of the H-F model, by improving its predictions.</p> <p>Towards this direction, we deduced the first experimental measurements of angular<br>distributions and cross sections of the reaction:</p> <p>112Cd(p,γ)113In, Qvalue = 6078.07 keV</p> <p>The measurements were carried out in the Tandem accelerator lab of N.S.C.R ”Demokritos” in the energy range of E= 2.8 − 3.4 MeV . The reaction was studied using two different methods, in-beam and activation, due to the isomeric state of<br>113In (391.7 keV, t1/2=99.5 m).</p> <p>Our experimental data were compared with the theoretical predictions of the latest version of code TALYS (v1.6), and are in fairly good agreement.</p

GEANT3 Simulation Video

GEANT3 Simulation of DRAGON Vide

Experimental Proposal & Presentation

This experiment was proposed - <b>Exp. No. </b><b>S1692</b> ``<i>Breakout reactions from the pp-chain and the ν</i><i>p-process: Measurement of the ⁷</i><i>Be(α,γ)¹¹C reaction rate in inverse kinematics</i>" - and approved by TRIUMF in the meeting of the Subatomic-Physics Experiment Evaluation Committee (EEC) of July 2016

DRAGON GEANT Simulation

A GEANT3 [1] simulation of the windowless gas target of DRAGON.<br><br>[1] D. Gigliotti, Ph.D. Thesis, Univ. of Northern British Columbia, (2004).<br