Group-level Emotion Recognition using Transfer Learning from Face Identification

In this paper, we describe our algorithmic approach, which was used for submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. We extracted feature vectors of detected faces using the Convolutional Neural Network trained for face identification task, rather than traditional pre-training on emotion recognition problems. In the final pipeline an ensemble of Random Forest classifiers was learned to predict emotion score using available training set. In case when the faces have not been detected, one member of our ensemble extracts features from the whole image. During our experimental study, the proposed approach showed the lowest error rate when compared to other explored techniques. In particular, we achieved 75.4% accuracy on the validation data, which is 20% higher than the handcrafted feature-based baseline. The source code using Keras framework is publicly available.Comment: 5 pages, 3 figures, accepted for publication at ICMI17 (EmotiW Grand Challenge

Diffraction based Hanbury Brown and Twiss interferometry performed at a hard x-ray free-electron laser

We demonstrate experimentally Hanbury Brown and Twiss (HBT) interferometry at a hard X-ray Free Electron Laser (XFEL) on a sample diffraction patterns. This is different from the traditional approach when HBT interferometry requires direct beam measurements in absence of the sample. HBT analysis was carried out on the Bragg peaks from the colloidal crystals measured at Linac Coherent Light Source (LCLS). We observed high degree (80%) spatial coherence of the full beam and the pulse duration of the monochromatized beam on the order of 11 fs that is significantly shorter than expected from the electron bunch measurements.Comment: 32 pages, 10 figures, 2 table

12^{12}C/13^{13}C ratio in planetary nebulae from the IUE archives

We investigated the abundance ratio of $^{12}$C/$^{13}$C in planetary nebulae by examining emission lines arising from \ion{C}{3} 2s2p ^3P_{2,1,0} \to 2s^2 ^1S_0. Spectra were retrieved from the International Ultraviolet Explorer archives, and multiple spectra of the same object were coadded to achieve improved signal-to-noise. The $^{13}$C hyperfine structure line at 1909.6 \AA was detected in NGC 2440. The $^{12}$C/$^{13}$C ratio was found to be $\sim4.4\pm$1.2. In all other objects, we provide an upper limit for the flux of the 1910 \AA line. For 23 of these sources, a lower limit for the $^{12}$C/$^{13}$C ratio was established. The impact on our current understanding of stellar evolution is discussed. The resulting high signal-to-noise \ion{C}{3} spectrum helps constrain the atomic physics of the line formation process. Some objects have the measured 1907/1909 flux ratio outside the low-electron density theoretical limit for $^{12}$C. A mixture of $^{13}$C with $^{12}$C helps to close the gap somewhat. Nevertheless, some observed 1907/1909 flux ratios still appear too high to conform to the presently predicted limits. It is shown that this limit, as well as the 1910/1909 flux ratio, are predominantly influenced by using the standard partitioning among the collision strengths for the multiplet $^1S_0$--$^3P_J$ according to the statistical weights. A detailed calculation for the fine structure collision strengths between these individual levels would be valuable.Comment: ApJ accepted: 19 pages, 3 Figures, 2 Table

How efficient are coronal mass ejections at accelerating solar energetic particles?

The largest solar energetic particle (SEP) events are thought to be due to particle acceleration at a shock driven by a fast coronal mass ejection (CME). We investigate the efficiency of this process by comparing the total energy content of energetic particles with the kinetic energy of the associated CMEs. The energy content of 23 large SEP events from 1998 through 2003 is estimated based on data from ACE, GOES, and SAMPEX, and interpreted using the results of particle transport simulations and inferred longitude distributions. CME data for these events are obtained from SOHO. When compared to the estimated kinetic energy of the associated coronal mass ejections (CMEs), it is found that large SEP events can extract ~10% or more of the CME kinetic energy. The largest SEP events appear to require massive, very energetic CMEs

Privacy preserving encrypted phonetic search of speech data

This paper presents a strategy for enabling speech recognition to be performed in the cloud whilst preserving the privacy of users. The approach advocates a demarcation of responsibilities between the client and server-side components for performing the speech recognition task. On the client-side resides the acoustic model, which symbolically encodes the audio and encrypts the data before uploading to the server. The server-side then employs searchable encryption to enable the phonetic search of the speech content. Some preliminary results for speech encoding and searchable encryption are presented