Deep neural network augmentation: generating faces for affect analysis

This paper presents a novel approach for synthesizing facial affect; either in terms of the six basic expressions (i.e., anger, disgust, fear, joy, sadness and surprise), or in terms of valence (i.e., how positive or negative is an emotion) and arousal (i.e., power of the emotion activation). The proposed approach accepts the following inputs:(i) a neutral 2D image of a person; (ii) a basic facial expression or a pair of valence-arousal (VA) emotional state descriptors to be generated, or a path of affect in the 2D VA space to be generated as an image sequence. In order to synthesize affect in terms of VA, for this person, 600,000 frames from the 4DFAB database were annotated. The affect synthesis is implemented by fitting a 3D Morphable Model on the neutral image, then deforming the reconstructed face and adding the inputted affect, and blending the new face with the given affect into the original image. Qualitative experiments illustrate the generation of realistic images, when the neutral image is sampled from fifteen well known lab-controlled or in-the-wild databases, including Aff-Wild, AffectNet, RAF-DB; comparisons with generative adversarial networks (GANs) show the higher quality achieved by the proposed approach. Then, quantitative experiments are conducted, in which the synthesized images are used for data augmentation in training deep neural networks to perform affect recognition over all databases; greatly improved performances are achieved when compared with state-of-the-art methods, as well as with GAN-based data augmentation, in all cases

Recognition of affect in the wild using deep neural networks

In this paper we utilize the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues. The proposed architecture is based on jointly training convolutional (CNN) and recurrent neural network (RNN) layers, thus exploiting both the invariant properties of convolutional features, while also modelling temporal dynamics that arise in human behaviour via the recurrent layers. Various pre-trained networks are used as starting structures which are subsequently appropriately fine-tuned to the Aff-Wild database. Obtained results show premise for the utilization of deep architectures for the visual analysis of human behaviour in terms of continuous emotion dimensions and analysis of different types of affect

Extensive spherical amyloid deposition presenting as a pituitary tumor

A 71-yr-old man was admitted for further evaluation and trans-sphenoidal surgery of a pituitary tumor. He complained of impotence and decreased libido over a period of about 40 yr. Thirty-eight yr ago he was treated for bilateral gynecomastia with galactorrhea. Endocrinological investigation at presentation revealed only mild hyperprolactinemia and hypogonadotropic hypogonadism. Pituitary magnetic resonance imaging (MRI) showed a tumor up to 2.5 cm in diameter with infiltration of the sphenoid sinus and right cavernous sinus. The tumor exhibited a heterogeneous hyperintense signal on T1-weighted images and hypointense signal on T2-weighted images. Standard trans-sphenoidal surgery was performed and a brownish mass was found inside the sella, which was removed. Histological examination of the mass revealed extensive spherical amyloid deposits with strongly positive immunohistochemical staining for prolactin. Therefore, a prolactinoma with extensive spherical amyloid deposition was diagnosed. Extensive spherical amyloid deposition is a rare finding in prolactin-secreting pituitary adenomas. So far, characteristic radiological findings by MRI have been described only twice. Due to characteristic MRI findings, the diagnosis of extensive intrasellar amyloid deposition can be entertained pre-operatively. Trans-sphenoidal surgical resection is essential to confirm the diagnosis histologically and because of the potential lack of tumor shrinkage under dopaminagonist therapy in this type of prolactinom

Morphological differences in Parkinson's disease with and without rest tremor

Background : Rest tremor is a hallmark of Parkinson's disease (PD), but its pathogenesis remains incompletely understood. Nigro-striatal dopamine deficiency correlates best with bradykinesia, but not with tremor. Oscillating neurons in one or multiple localizations within the basal gangliathalamo-cortical loop may cause rest tremor, and an active contribution of the cerebellum and the cerebello-thalamo-cortical projections has been postulated. Objective : To compare the pattern of grey matter volume in PD patients with and without tremor to identify structural correlates of rest tremor. Methods : Voxel-based morphometry (VBM) of a high-resolution 3 Tesla, T1-weighted MR images, pre-processed according to an optimized protocol using SPM2, was performed in 24 patients with mild to moderate PD comparing local grey matter volume in patients with (n = 14) and without rest tremor (n = 10). Results : Grey matter volume is decreased in the right quadrangular lobe and declive of the cerebellum in PD with tremor compared to those without (PFDR < 0.05). Conclusions : These results demonstrate for the first time morphological changes in the cerebellum in PD patients with rest tremor and highlight the involvement of the cerebellum and cerebello- thalamo-cortical circuit in the pathogenesis of parkinsonian rest tremo

Noise-based core monitoring and diagnostics: overview of the cortex project

This paper gives an overview of the CORTEX project, which is a Research and Innovation Action funded by the European Union in the Euratom 2016-2017 work program, under the Horizon 2020 framework. CORTEX, which stands for CORe monitoring Techniques and EXperimental validation and demonstration, aims at developing an innovative core monitoring technique that allows detecting anomalies in nuclear reactors, such as excessive vibrations of core internals, flow blockage, coolant inlet perturbations, etc. The technique is based on primarily using the inherent fluctuations in neutron flux recorded by in-core and ex-core instrumentation (often referred to as neutron noise), from which the anomalies will be differentiated depending on their type, location and characteristics. In addition to be non-intrusive and not requiring any external perturbation of the system, the method allows the detection of operational problems at a very early stage. Proper actions could thus be taken by utilities before such problems have any adverse effect on plant safety and reliability

Aff-Wild: Valence and Arousal ‘in-the-wild’ Challenge

The Affect-in-the-Wild (Aff-Wild) Challenge proposes a new comprehensive benchmark for assessing the performance of facial affect/behaviour analysis/understanding 'in-the-wild'. The Aff-wild benchmark contains about 300 videos (over 2,000 minutes of data) annotated with regards to valence and arousal, all captured 'in-the-wild' (the main source being Youtube videos). The paper presents the database description, the experimental set up, the baseline method used for the Challenge and finally the summary of the performance of the different methods submitted to the Affect-in-the-Wild Challenge for Valence and Arousal estimation. The challenge demonstrates that meticulously designed deep neural networks can achieve very good performance when trained with in-the-wild data

Cortical substrate of bladder control in SCI and the effect of peripheral pudendal stimulation

We investigate (i) the central representation of lower urinary tract (LUT) control and (ii–iii) the acute and 23 short-term central neuromodulatory effect of peripheral pudendal nerve stimulation in incomplete spinal 24 cord injured (SCI) patients using functional magnetic resonance imaging (fMRI). The urinary bladder of eight 25 SCI patients has been passively filled and emptied using a catheter, to identify the neural substrate of bladder 26 control (i), and with simultaneous peripheral pudendal nerve stimulation to investigate its acute central 27 neuromodulatory effect (ii). To identify the potential effects of pudendal nerve stimulation treatment (iii), 28 six patients underwent a 2-week training using pudendal nerve stimulation followed by another fMRI 29 session of bladder filling. The pre- and post-training fMRI results have been compared and correlated with 30 the patient's pre- and post-training urological status. Our results suggest that the central representation of 31 bladder filling sensation is preserved in the subacute stage of incomplete SCI. However, compared to earlier 32 data from healthy subjects, it shows decreased neural response in right prefrontal areas and increased in left 33 prefrontal regions, indicating diminished inhibitory micturition control as well as, compensatory or de- 34 compensatory reorganization of bladder control. We also provide evidence for a neuromodulatory effect of 35 acute pudendal nerve stimulation, which was most prominent in the right posterior insula, a brain region 36 implicated in homeostatic interoception in human. Pudendal stimulation training also induced significant 37 neuromodulation, predominantly signal increases, in the normal cortical network of bladder control. 38 Correlations with the patient's urological status indicate that this neuromodulatory effect may reflect the 39 clinical improvement following training