812 research outputs found
A Monte Carlo method for the spread of mobile malware
A new model for the spread of mobile malware based on proximity (i.e.
Bluetooth, ad-hoc WiFi or NFC) is introduced. The spread of malware is analyzed
using a Monte Carlo method and the results of the simulation are compared with
those from mean field theory.Comment: 11 pages, 2 figure
Decays into {\pi}+{\pi}- of the f0(1370) scalar glueball candidate in pp central exclusive production experiments
The existence and properties of the f0(1370) scalar meson are rather well
established from data of antiproton annihilations at rest. However conflicting
results from Central Exclusive Production (CEP) experiments of the last
millennium and ignorance of data from antiproton annihilations at rest in H2
and D2 bubble chambers have generated doubts on the very existence of the
f0(1370). Properties of {\pi}+{\pi}- pairs produced in central exclusive
production (CEP) reactions observed in old data together with data collected in
the current decade at high energy colliders permit to show that {\pi}+{\pi}-
decays of the f0(1370) meson are directly observable as an isolated peak
between 1.1 and 1.6 GeV. Consequences of this observation and prospects for the
identification of the scalar glueball ground-state are discussed.Comment: 20 pages, 11 figure
Bryuno Function and the Standard Map
For the standard map the homotopically non-trivial invariant curves of
rotation number satisfying the Bryuno condition are shown to be analytic in the
perturbative parameter, provided the latter is small enough. The radius of
convergence of the Lindstedt series -- sometimes called critical function of
the standard map -- is studied and the relation with the Bryuno function is
derived: the logarithm of the radius of convergence plus twice the Bryuno
function is proved to be bounded (from below and from above) uniformily in the
rotation number.Comment: 120 K, Latex, 33 page
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
In this paper, we propose a new approach for facial expression recognition
using deep covariance descriptors. The solution is based on the idea of
encoding local and global Deep Convolutional Neural Network (DCNN) features
extracted from still images, in compact local and global covariance
descriptors. The space geometry of the covariance matrices is that of Symmetric
Positive Definite (SPD) matrices. By conducting the classification of static
facial expressions using Support Vector Machine (SVM) with a valid Gaussian
kernel on the SPD manifold, we show that deep covariance descriptors are more
effective than the standard classification with fully connected layers and
softmax. Besides, we propose a completely new and original solution to model
the temporal dynamic of facial expressions as deep trajectories on the SPD
manifold. As an extension of the classification pipeline of covariance
descriptors, we apply SVM with valid positive definite kernels derived from
global alignment for deep covariance trajectories classification. By performing
extensive experiments on the Oulu-CASIA, CK+, and SFEW datasets, we show that
both the proposed static and dynamic approaches achieve state-of-the-art
performance for facial expression recognition outperforming many recent
approaches.Comment: A preliminary version of this work appeared in "Otberdout N, Kacem A,
Daoudi M, Ballihi L, Berretti S. Deep Covariance Descriptors for Facial
Expression Recognition, in British Machine Vision Conference 2018, BMVC 2018,
Northumbria University, Newcastle, UK, September 3-6, 2018. ; 2018 :159."
arXiv admin note: substantial text overlap with arXiv:1805.0386
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
In this work, we propose a novel approach for generating videos of the six
basic facial expressions given a neutral face image. We propose to exploit the
face geometry by modeling the facial landmarks motion as curves encoded as
points on a hypersphere. By proposing a conditional version of manifold-valued
Wasserstein generative adversarial network (GAN) for motion generation on the
hypersphere, we learn the distribution of facial expression dynamics of
different classes, from which we synthesize new facial expression motions. The
resulting motions can be transformed to sequences of landmarks and then to
images sequences by editing the texture information using another conditional
Generative Adversarial Network. To the best of our knowledge, this is the first
work that explores manifold-valued representations with GAN to address the
problem of dynamic facial expression generation. We evaluate our proposed
approach both quantitatively and qualitatively on two public datasets;
Oulu-CASIA and MUG Facial Expression. Our experimental results demonstrate the
effectiveness of our approach in generating realistic videos with continuous
motion, realistic appearance and identity preservation. We also show the
efficiency of our framework for dynamic facial expressions generation, dynamic
facial expression transfer and data augmentation for training improved emotion
recognition models
SPEAKER VGG CCT: Cross-corpus Speech Emotion Recognition with Speaker Embedding and Vision Transformers
In recent years, Speech Emotion Recognition (SER) has been investigated
mainly transforming the speech signal into spectrograms that are then
classified using Convolutional Neural Networks pretrained on generic images and
fine tuned with spectrograms. In this paper, we start from the general idea
above and develop a new learning solution for SER, which is based on Compact
Convolutional Transformers (CCTs) combined with a speaker embedding. With CCTs,
the learning power of Vision Transformers (ViT) is combined with a diminished
need for large volume of data as made possible by the convolution. This is
important in SER, where large corpora of data are usually not available. The
speaker embedding allows the network to extract an identity representation of
the speaker, which is then integrated by means of a self-attention mechanism
with the features that the CCT extracts from the spectrogram. Overall, the
solution is capable of operating in real-time showing promising results in a
cross-corpus scenario, where training and test datasets are kept separate.
Experiments have been performed on several benchmarks in a cross-corpus setting
as rarely used in the literature, with results that are comparable or superior
to those obtained with state-of-the-art network architectures. Our code is
available at https://github.com/JabuMlDev/Speaker-VGG-CCT
- …