Naturalistic Affective Expression Classification by a Multi-Stage Approach Based on Hidden Markov Models

In naturalistic behaviour, the affective states of a person change at a rate much slower than the typical rate at which video or audio is recorded (e.g. 25fps for video). Hence, there is a high probability that consecutive recorded instants of expressions represent a same affective content. In this paper, a multi-stage automatic affective expression recognition system is proposed which uses Hidden Markov Models (HMMs) to take into account this temporal relationship and finalize the classification process. The hidden states of the HMMs are associated with the levels of affective dimensions to convert the classification problem into a best path finding problem in HMM. The system was tested on the audio data of the Audio/Visual Emotion Challenge (AVEC) datasets showing performance significantly above that of a one-stage classification system that does not take into account the temporal relationship, as well as above the baseline set provided by this Challenge. Due to the generality of the approach, this system could be applied to other types of affective modalities

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisakis model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: Linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers error rates and then to evaluate the information expressed by the classifiers confusion matrices. © Springer Science+Business Media, LLC 2011

Status of the CRESST Dark Matter Search

The CRESST experiment aims for a detection of dark matter in the form of WIMPs. These particles are expected to scatter elastically off the nuclei of a target material, thereby depositing energy on the recoiling nucleus. CRESST uses scintillating CaWO4 crystals as such a target. The energy deposited by an interacting particle is primarily converted to phonons which are detected by transition edge sensors. In addition, a small fraction of the interaction energy is emitted from the crystals in the form of scintillation light which is measured in coincidence with the phonon signal by a separate cryogenic light detector for each target crystal. The ratio of light to phonon energy permits the discrimination between the nuclear recoils expected from WIMPs and events from radioactive backgrounds which primarily lead to electron recoils. CRESST has shown the success of this method in a commissioning run in 2007 and, since then, further investigated possibilities for an even better suppression of backgrounds. Here, we report on a new class of background events observed in the course of this work. The consequences of this observation are discussed and we present the current status of the experiment.Comment: Proceedings of the 13th International Workshop on Low Temperature Detectors, 4 pages, 3 figure

First Dark Matter Results from the XENON100 Experiment

The XENON100 experiment, in operation at the Laboratori Nazionali del Gran Sasso in Italy, is designed to search for dark matter WIMPs scattering off 62 kg of liquid xenon in an ultra-low background dual-phase time projection chamber. In this letter, we present first dark matter results from the analysis of 11.17 live days of non-blind data, acquired in October and November 2009. In the selected fiducial target of 40 kg, and within the pre-defined signal region, we observe no events and hence exclude spin-independent WIMP-nucleon elastic scattering cross-sections above 3.4 x 10^-44 cm^2 for 55 GeV/c^2 WIMPs at 90% confidence level. Below 20 GeV/c^2, this result constrains the interpretation of the CoGeNT and DAMA signals as being due to spin-independent, elastic, light mass WIMP interactions.Comment: 5 pages, 5 figures. Matches published versio

Solar neutrino detection in a large volume double-phase liquid argon experiment

Precision measurements of solar neutrinos emitted by specific nuclear reaction chains in the Sun are of great interest for developing an improved understanding of star formation and evolution. Given the expected neutrino fluxes and known detection reactions, such measurements require detectors capable of collecting neutrino-electron scattering data in exposures on the order of 1 ktonne yr, with good energy resolution and extremely low background. Two-phase liquid argon time projection chambers (LAr TPCs) are under development for direct Dark Matter WIMP searches, which possess very large sensitive mass, high scintillation light yield, good energy resolution, and good spatial resolution in all three cartesian directions. While enabling Dark Matter searches with sensitivity extending to the "neutrino floor" (given by the rate of nuclear recoil events from solar neutrino coherent scattering), such detectors could also enable precision measurements of solar neutrino fluxes using the neutrino-electron elastic scattering events. Modeling results are presented for the cosmogenic and radiogenic backgrounds affecting solar neutrino detection in a 300 tonne (100 tonne fiducial) LAr TPC operating at LNGS depth (3,800 meters of water equivalent). The results show that such a detector could measure the CNO neutrino rate with ~15% precision, and significantly improve the precision of the 7Be and pep neutrino rates compared to the currently available results from the Borexino organic liquid scintillator detector.Comment: 21 pages, 7 figures, 6 table

Recognition of Facial Expressions by Cortical Multi-scale Line and Edge Coding

Face-to-face communications between humans involve emotions, which often are unconsciously conveyed by facial expressions and body gestures. Intelligent human-machine interfaces, for example in cognitive robotics, need to recognize emotions. This paper addresses facial expressions and their neural correlates on the basis of a model of the visual cortex: the multi-scale line and edge coding. The recognition model links the cortical representation with Paul Ekman's Action Units which are related to the different facial muscles. The model applies a top-down categorization with trends and magnitudes of displacements of the mouth and eyebrows based on expected displacements relative to a neutral expression. The happy vs. not-happy categorization yielded a. correct recognition rate of 91%, whereas final recognition of the six expressions happy, anger, disgust, fear, sadness and surprise resulted in a. rate of 78%

Material screening and selection for XENON100

Results of the extensive radioactivity screening campaign to identify materials for the construction of XENON100 are reported. This Dark Matter search experiment is operated underground at Laboratori Nazionali del Gran Sasso (LNGS), Italy. Several ultra sensitive High Purity Germanium detectors (HPGe) have been used for gamma ray spectrometry. Mass spectrometry has been applied for a few low mass plastic samples. Detailed tables with the radioactive contaminations of all screened samples are presented, together with the implications for XENON100.Comment: 8 pages, 1 figur

Implications on Inelastic Dark Matter from 100 Live Days of XENON100 Data

The XENON100 experiment has recently completed a dark matter run with 100.9 live-days of data, taken from January to June 2010. Events in a 48kg fiducial volume in the energy range between 8.4 and 44.6 keVnr have been analyzed. A total of three events have been found in the predefined signal region, compatible with the background prediction of (1.8 \pm 0.6) events. Based on this analysis we present limits on the WIMP-nucleon cross section for inelastic dark matter. With the present data we are able to rule out the explanation for the observed DAMA/LIBRA modulation as being due to inelastic dark matter scattering off iodine at a 90% confidence level.Comment: 3 pages, 3 figure