We study spin-dependent interband resonant tunneling in double-barrier InAs/AlSb/ GaMnSb heterostructures. We demonstrate that these structures can be used as spin filters utilizing spin-selective tunneling of electrons through the light-hole resonant channel. High densities of the spin polarized electrons injected into bulk InAs make spin resonant tunneling devices a viable alternative for injecting spins into a semiconductor. Another striking feature of the proposed devices is the possibility of inducing additional resonant channels corresponding to the heavy holes. This can be implemented by saturating the in-plane magnetization in the quantum well.

A. G. Petukhov D. O. Demchenko A. N. Chantis

An electronic quantity, the correlation strength, is defined as a necessary step for understanding the properties and trends in strongly correlated electronic materials. As a test case, this is applied to the different phases of elemental Pu. Within the GW approximation we have surprisingly found a "universal" scaling relationship, where the f-electron bandwidth reduction due to correlation effects is shown to depend only on the local density approximation bandwidth and is otherwise independent of crystal structure and lattice constant.

A. Svane R. C. Albers N. E. Christensen M. van Schilfgaarde A. N. Chantis Jian-Xin Zhu

The spin-orbit interaction generally leads to spin splitting (SS) of electron and hole energy states in solids, a splitting that is characterized by a scaling with the wavevector $\bf k$. Whereas for {\it 3D bulk zincblende} solids the electron (heavy hole) SS exhibits a cubic (linear) scaling with $k$, in {\it 2D quantum-wells} the electron (heavy hole) SS is currently believed to have a mostly linear (cubic) scaling. Such expectations are based on using a small 3D envelope function basis set to describe 2D physics. By treating instead the 2D system explicitly in a multi-band many-body approach we discover a large linear scaling of hole states in 2D. This scaling emerges from hole bands coupling that would be unsuspected by the standard model that judges coupling by energy proximity. This discovery of a linear Dresselhaus k-scaling for holes in 2D implies a different understanding of hole-physics in low-dimensions.

Jun-Wei Luo Athanasios N. Chantis Mark van Schilfgaarde Gabriel Bester Alex Zunger

We use a fully relativistic layer Green's functions approach to investigate spin-dependent tunneling through a symmetric indirect band gap barrier like GaAs/AlAs/GaAs heterostructure along [100] direction. The method is based on Linear Muffin Tin Orbitals and it is within the Density Functional Theory (DFT) in the Local Density Approximation (LDA). We find that the results of our {\it ab-initio} calculations are in good agreement with the predictions of our previous empirical tight binding model [Phys. Rev. {\bf B}, 075313 (2006)]. In addition we show the $k_{||}$-dependence of the spin polarization which we did not previously include in the model. The {\it ab-initio} calculations indicate a strong $k_{||}$-dependence of the transmission and the spin polarization due to band non-parabolicity. A large window of 25-50 % spin polarization was found for a barrier of 8 AlAs monolayers at $k_{||}$ = 0.03 $2\pi/a$. Our calculations show clearly that the appearance of energy windows with significant spin polarization depends mostly on the location of transmission resonances and their corresponding zeros and not on the magnitude of the spin splitting in the barrier.

Athanasios N Chantis Titus Sandu Jialei L Xu

This paper addresses the domain shift problem for segmentation. As a solution, we propose OLVA, a novel and lightweight unsupervised domain adaptation method based on a Variational Auto-Encoder (VAE) and Optimal Transport (OT) theory. Thanks to the VAE, our model learns a shared cross-domain latent space that follows a normal distribution, which reduces the domain shift. To guarantee valid segmentations, our shared latent space is designed to model the shape rather than the intensity variations. We further rely on an OT loss to match and align the remaining discrepancy between the two domains in the latent space. We demonstrate OLVA's effectiveness for the segmentation of multiple cardiac structures on the public Multi-Modality Whole Heart Segmentation (MM-WHS) dataset, where the source domain consists of annotated 3D MR images and the unlabelled target domain of 3D CTs. Our results show remarkable improvements with an additional margin of 12.5\% dice score over concurrent generative training approaches.

Dawood Al Chanti Diana Mateus

Micro-expressions (MEs) are infrequent and uncontrollable facial events that can highlight emotional deception and appear in a high-stakes environment. This paper propose an algorithm for spatiotemporal MEs spotting. Since MEs are unusual events, we treat them as abnormal patterns that diverge from expected Normal Facial Behaviour (NFBs) patterns. NFBs correspond to facial muscle activations, eye blink/gaze events and mouth opening/closing movements that are all facial deformation but not MEs. We propose a probabilistic model to estimate the probability density function that models the spatiotemporal distributions of NFBs patterns. To rank the outputs, we compute the negative log-likelihood and we developed an adaptive thresholding technique to identify MEs from NFBs. While working only with NFBs data, the main challenge is to capture intrinsic spatiotemoral features, hence we design a recurrent convolutional autoencoder for feature representation. Finally, we show that our system is superior to previous works for MEs spotting.

Dawood Al Chanti Alice Caplier

We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. A deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle appearance and shape for contiguous slices is deployed. We uses it to propagate a reference mask annotated by a clinical expert. To handle longer changes of the muscle shape over the entire volume and to provide an accurate propagation, we devise a Bidirectional Long Short Term Memory module. Also, to train our model with a minimal amount of training samples, we propose a strategy combining learning from few annotated 2D ultrasound slices with sequential pseudo-labeling of the unannotated slices. We introduce a decremental update of the objective function to guide the model convergence in the absence of large amounts of annotated data. After training with a small number of volumes, the decremental update transitions from a weakly-supervised training to a few-shot setting. Finally, to handle the class-imbalance between foreground and background muscle pixels, we propose a parametric Tversky loss function that learns to adaptively penalize false positives and false negatives. We validate our approach for the segmentation, label propagation, and volume computation of the three low-limb muscles on a dataset of 61600 images from 44 subjects. We achieve a Dice score coefficient of over $95~\%$ and a volumetric error \textcolor{black}{of} $1.6035 \pm 0.587~\%$.

Dawood Al Chanti Vanessa Gonzalez Duque Marion Crouzier Antoine Nordez Lilian Lacourpaille Diana Mateus

Facial expression is the most natural means for human beings to communicate their emotions. Most facial expression analysis studies consider the case of acted expressions. Spontaneous facial expression recognition is significantly more challenging since each person has a different way to react to a given emotion. We consider the problem of recognizing spontaneous facial expression by learning discriminative dictionaries for sparse representation. Facial images are represented as a sparse linear combination of prototype atoms via Orthogonal Matching Pursuit algorithm. Sparse codes are then used to train an SVM classifier dedicated to the recognition task. The dictionary that sparsifies the facial images (feature points with the same class labels should have similar sparse codes) is crucial for robust classification. Learning sparsifying dictionaries heavily relies on the initialization process of the dictionary. To improve the performance of dictionaries, a random face feature descriptor based on the Random Projection concept is developed. The effectiveness of the proposed method is evaluated through several experiments on the spontaneous facial expressions DynEmo database. It is also estimated on the well-known acted facial expressions JAFFE database for a purpose of comparison with state-of-the-art methods.

Dawood Al Chanti Alice Caplier

Bag-of-Visual-Words (BoVW) approach has been widely used in the recent years for image classification purposes. However, the limitations regarding optimal feature selection, clustering technique, the lack of spatial organization of the data and the weighting of visual words are crucial. These factors affect the stability of the model and reduce performance. We propose to develop an algorithm based on BoVW for facial expression analysis which goes beyond those limitations. Thus the visual codebook is built by using k-Means++ method to avoid poor clustering. To exploit reliable low level features, we search for the best feature detector that avoids locating a large number of keypoints which do not contribute to the classification process. Then, we propose to compute the relative conjunction matrix in order to preserve the spatial order of the data by coding the relationships among visual words. In addition, a weighting scheme that reflects how important a visual word is with respect to a given image is introduced. We speed up the learning process by using histogram intersection kernel by Support Vector Machine to learn a discriminative classifier. The efficiency of the proposed algorithm is compared with standard bag of visual words method and with bag of visual words method with spatial pyramid. Extensive experiments on the CK+, the MMI and the JAFFE databases show good average recognition rates. Likewise, the ability to recognize spontaneous and non-basic expressive states is investigated using the DynEmo database.

Dawood Al Chanti Alice Caplier

The design, construction and test of a charged particle detector made of scintillation counters read by Silicon Photomultipliers (SiPM) is described. The detector, which operates in vacuum and is used as a veto counter in the NA62 experiment at CERN, has a single channel time resolution of 1.14 ns, a spatial resolution of ~2.5 mm and an efficiency very close to 1 for penetrating charged particles.

F. Ambrosino T. Capussela D. Di Filippo P. Massarotti M. Mirra M. Napolitano V. Palladino G. Saracino L. Roscilli A. Vanzanella G. Corradi D. Tagnani U. Paglia