Learning based automatic face annotation for arbitrary poses and expressions from frontal images only

Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases

Explainable Depression Detection via Head Motion Patterns

While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker. This study demonstrates the utility of fundamental head-motion units, termed kinemes, for depression detection by adopting two distinct approaches, and employing distinctive features: (a) discovering kinemes from head motion data corresponding to both depressed patients and healthy controls, and (b) learning kineme patterns only from healthy controls, and computing statistics derived from reconstruction errors for both the patient and control classes. Employing machine learning methods, we evaluate depression classification performance on the BlackDog and AVEC2013 datasets. Our findings indicate that: (1) head motion patterns are effective biomarkers for detecting depressive symptoms, and (2) explanatory kineme patterns consistent with prior findings can be observed for the two classes. Overall, we achieve peak F1 scores of 0.79 and 0.82, respectively, over BlackDog and AVEC2013 for binary classification over episodic thin-slices, and a peak F1 of 0.72 over videos for AVEC2013

From individual to group-level emotion recognition: Emoti W 5.0

Research in automatic affect recognition has come a long way. This paper describes the fifth Emotion Recognition in the Wild (EmotiW) challenge 2017. EmotiW aims at providing a common benchmarking platform for researchers working on different aspects of affective computing. This year there are two sub-challenges: A) Audio-video emotion recognition and b) group-level emotion recognition. These challenges are based on the acted facial expressions in the wild and group affect databases, respectively. The particular focus of the challenge is to evaluate method in 'in the wild' settings. 'In the wild' here is used to describe the various environments represented in the images and videos, which represent real-world (not lab like) scenarios. The baseline, data, protocol of the two challenges and the challenge participation are discussed in detail in this paper

Hadronic contribution to the muon g-2: a Dyson-Schwinger perspective

We summarize our results for hadronic contributions to the anomalous magnetic moment of the muon ($a_\mu$), the one from hadronic vacuum-polarisation (HVP) and the light-by-light scattering contribution (LBL), obtained from the Dyson-Schwinger equations (DSE's) of QCD. In the case of HVP we find good agreement with model independent determinations from dispersion relations for $a_\mu^\mathrm{HVP}$ as well as for the Adler function with deviations well below the ten percent level. From this we conclude that the DSE approach should be capable of describing $a_\mu^\mathrm{LBL}$ with similar accuracy. We also present results for LBL using a resonance expansion of the quark anti-quark T-matrix. Our preliminary value is $a_\mu^\mathrm{LBL}=(217 \pm 91) \times 10^{-11}$.Comment: Contribution to the proceedings of 'International school of nuclear physics, 33rd course', Erice-Sicily: 16 - 24 September 201

Quinine ingestion during the latter stages of a 3,000-m time trial fails to improve cycling performance

The ingestion of quinine, a bitter tastant, improves short-term (30 s) cycling performance, but it is unclear whether this effect can be integrated into the last effort of a longer race. The purpose of this study was to determine whether midtrial quinine ingestion improves 3,000-m cycling time-trial (TT) performance. Following three familiarization TTs, 12 well-trained male cyclists (mean ± SD: mass = 76.6 ± 9.2 kg, maximal aerobic power = 390 ± 50 W, maximal oxygen uptake = 4.7 ± 0.6 L/min) performed four experimental 3,000-m TTs on consecutive days. This double-blind, crossover design study had four randomized and counterbalanced conditions: (a) Quinine 1 (25-ml solution, 2 mM of quinine); (b) Quinine 2, replicate of Quinine 1; (c) a 25-ml sweet-tasting no-carbohydrate solution (Placebo); and (d) 25 ml of water (Control) consumed at the 1,850-m point of the TT. The participants completed a series of perceptual scales at the start and completion of all TTs, and the power output was monitored continuously throughout all trials. The power output for the last 1,000 m for all four conditions was similar: mean ± SD: Quinine 1 = 360 ± 63 W, Quinine 2 = 367 ± 63 W, Placebo = 364 ± 64 W, and Control = 367 ± 58 W. There were also no differences in the 3,000-m TT power output between conditions. The small perceptual differences between trials at specific 150-m splits were not explained by quinine intake. Ingesting 2 mM of quinine during the last stage of a 3,000-m TT did not improve cycling performance

Removal of xenoantigenic glycosylation patterns from porcine pulmonary heart valve matrices is dependent of the applied decellularization method

Department of Cardiac-, Thoracic-, Transplantation and Vascular Surgery, Hannover Medical School, Hannover Germany and Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Hannover Medical School, Hannover, Germany, The 6th International Medical Congress for Students and Young Doctors, May 12-14, 2016Introduction: Matrix guided tissue regeneration (GTR) based on allogeneic decellularized matrices has been shown as an overall convincing method for heart valve replacement. Nevertheless, a substantial donor shortage prevents an unlimited clinical application of human GTR-valves. Utilization of porcine decellularized heart valve matrices could offer a possible solution for overcoming this considerable limitation. In the past, implantation of xenogeneic valve tissues considered to be acellularinto human recipients, however, mostly lead to severe immune responses usually ending up into graft rejection. This study addresses the question whether potential xenoantigenic glycosylation of extracellular matrix components, like the major xenoantigen α-Gal, which served as model epitope for this study, can be removed by adjusted decellularization procedures. Materials and methods: Fresh porcine pulmonary heart valve conduits were decellularized by application of different detergent- and enzyme-based decellularization protocols. Subsequent cleavage of remaining matrix-related α-Gal epitopes was performed by enzymatic deglycosylation treatment on matrix samples of each decellularization group. Resulting tissues, mainly composed from insoluble extracellular matrix proteins, were afterwards divided into the relevant sections pulmonary artery wall specimens and pulmonary valve leaflets, frozen in liquid nitrogen, minced and finally solubilized by protease digestion. Evaluation of thus prepared solutions regarding to α-Gal contents was finally performed using a novel designed lectin-based immunoblot technique. Discussion results: Sole decellularization lead to significant removal of α-Gal, substantial varying in strong dependency to applied protocols between 30 to 50% compared to α-Gal contents of porcine native control tissues. An additional decrease of residual α-Gal in a range of another 15 to 30% was achievable by additional α-Galactosidase treatment. Combining decellularization and subsequent enzymatic digestion resulted in reductions of matrix related α-Gal contents down to levels, which could be measured for respective pulmonary valve tissues of α-Gal-KnockOut pigs. Conclusion: Residual xenoantigenic carbohydrates are detectable on insoluble matrix components of porcine pulmonary heart valves, substantially varying dependent on applied decellularization protocols. Combined with glycolytic digestions, remaining glycosylation contents are reducible to background levels. Impacts of these novel insights have to be evaluated in further in vitro as well as in vivo studies

Investigating word affect features and fusion of probabilistic predictions incorporating uncertainty in AVEC 2017

© 2017 Association for Computing Machinery. Predicting emotion intensity and severity of depression are both challenging and important problems within the broader field of affective computing. As part of the AVEC 2017, we developed a number of systems to accomplish these tasks. In particular, word affect features, which derive human affect ratings (e.g. arousal and valence) from transcripts, were investigated for predicting depression severity and liking, showing great promise. A simple system based on the word affect features achieved an RMSE of 6.02 on the test set, yielding a relative improvement of 13.6% over the baseline. For the emotion prediction sub-challenge, we investigated multimodal fusion, which incorporated a measure of uncertainty associated with each prediction within an Output-Associative fusion framework for arousal and valence prediction, whilst liking prediction systems mainly focused on text-based features. Our best emotion prediction systems provided significant relative improvements over the baseline on the test set of 39.5%, 17.6%, and 29.3% for arousal, valence, and liking. Of particular note is that consistent improvements were observed when incorporating prediction uncertainty across various system configurations for predicting arousal and valence, suggesting the importance of taking into consideration prediction uncertainty for fusion and more broadly the advantages of probabilistic predictions

Structure and deformation of the Kermadec forearc in response to subduction of the Pacific oceanic plate

The Tonga-Kermadec forearc is deforming in response to on-going subduction of the Pacific Plate beneath the Indo-Australian Plate. Previous research has focussed on the structural development of the forearc where large bathymetric features such as the Hikurangi Plateau and Louisville Ridge seamount chain are being subducted. Consequently, knowledge of the ‘background’ forearc in regions of normal plate convergence is limited. We report on an ∼250-km-long multichannel seismic reflection profile that was shot perpendicular to the Tonga-Kermadec trench at ∼28°S to determine the lateral and temporal variations in the structure, stratigraphy and deformation of the Kermadec forearc resulting solely from Pacific Plate subduction. Interpretation of the seismic profile, in conjunction with regional swath bathymetry data, shows that the Pacific Plate exhibits horst and graben structures that accommodate bending-induced extensional stresses, generated as the trenchward dip of the crust increases. Trench infill is also much thicker than expected at 1 km which, we propose, results from increased sediment flux into and along the trench. Pervasive normal faulting of the mid-trench slope most likely accommodates the majority of the observed forearc extension in response to basal subduction erosion, and a structural high is located between the mid- and upper-trench slopes. We interpret this high as representing a dense and most likely structurally robust region of crust lying beneath this region. Sediment of the upper-trench slope documents depositional hiatuses and on-going uplift of the arc. Strong along-arc currents appear to erode the Kermadec volcanic arc and distribute this sediment to the surrounding basins, while currents over the forearc redistribute deposits as sediment waves. Minor uplift of the transitional Kermadec forearc, observed just to the north of the profile, appears to relate to an underlying structural trend as well as subduction of the Louisville Ridge seamount chain 250 km to the north. Relative uplift of the Kermadec arc is observed from changes in the tilt of upper-trench slope deposits and extensional faulting of the basement immediately surrounding the Louisville Ridge

Implications of LHC Searches on SUSY Particle Spectra: The pMSSM Parameter Space with Neutralino Dark Matter

We study the implications of LHC searches on SUSY particle spectra using flat scans of the 19-parameter pMSSM phase space. We apply constraints from flavour physics, g_mu-2, dark matter and earlier LEP and Tevatron searches. The sensitivity of the LHC SUSY searches with jets, leptons and missing energy is assessed by reproducing with fast simulation the recent CMS analyses after validation on benchmark points. We present results in terms of the fraction of pMSSM points compatible with all the constraints which are excluded by the LHC searches with 1 fb^{-1} and 15 fb^{-1} as a function of the mass of strongly and weakly interacting SUSY particles. We also discuss the suppression of Higgs production cross sections for the MSSM points not excluded and contrast the region of parameter space tested by the LHC data with the constraints from dark matter direct detection experiments.Comment: 14 pages, 13 figures. v2: increased statistics, to appear in EPJ