Benchmark Analysis of Representative Deep Neural Network Architectures

This work presents an in-depth analysis of the majority of the deep neural networks (DNNs) proposed in the state of the art for image recognition. For each DNN multiple performance indices are observed, such as recognition accuracy, model complexity, computational complexity, memory usage, and inference time. The behavior of such performance indices and some combinations of them are analyzed and discussed. To measure the indices we experiment the use of DNNs on two different computer architectures, a workstation equipped with a NVIDIA Titan X Pascal and an embedded system based on a NVIDIA Jetson TX1 board. This experimentation allows a direct comparison between DNNs running on machines with very different computational capacity. This study is useful for researchers to have a complete view of what solutions have been explored so far and in which research directions are worth exploring in the future; and for practitioners to select the DNN architecture(s) that better fit the resource constraints of practical deployments and applications. To complete this work, all the DNNs, as well as the software used for the analysis, are available online.Comment: Will appear in IEEE Acces

Aesthetics Assessment of Images Containing Faces

Recent research has widely explored the problem of aesthetics assessment of images with generic content. However, few approaches have been specifically designed to predict the aesthetic quality of images containing human faces, which make up a massive portion of photos in the web. This paper introduces a method for aesthetic quality assessment of images with faces. We exploit three different Convolutional Neural Networks to encode information regarding perceptual quality, global image aesthetics, and facial attributes; then, a model is trained to combine these features to explicitly predict the aesthetics of images containing faces. Experimental results show that our approach outperforms existing methods for both binary, i.e. low/high, and continuous aesthetic score prediction on four different databases in the state-of-the-art.Comment: Accepted by ICIP 201

Disentangling Image Distortions in Deep Feature Space

Previous literature suggests that perceptual similarity is an emergent property shared across deep visual representations. Experiments conducted on a dataset of human-judged image distortions have proven that deep features outperform classic perceptual metrics. In this work we take a further step in the direction of a broader understanding of such property by analyzing the capability of deep visual representations to intrinsically characterize different types of image distortions. To this end, we firstly generate a number of synthetically distorted images and then we analyze the features extracted by different layers of different Deep Neural Networks. We observe that a dimension-reduced representation of the features extracted from a given layer permits to efficiently separate types of distortions in the feature space. Moreover, each network layer exhibits a different ability to separate between different types of distortions, and this ability varies according to the network architecture. Finally, we evaluate the exploitation of features taken from the layer that better separates image distortions for: i) reduced-reference image quality assessment, and ii) distortion types and severity levels characterization on both single and multiple distortion databases. Results achieved on both tasks suggest that deep visual representations can be unsupervisedly employed to efficiently characterize various image distortions

Electromagnetic analysis of the plasma chamber of an ECR-based charge breeder.

The optimization of the efficiency of an ECR-based charge breeder is a twofold task: efforts must be paid to maximize the capture of the injected 1+ ions by the confined plasma and to produce high charge states to allow post-acceleration at high energies. Both tasks must be faced by studying in detail the electrons heating dynamics, influenced by the microwave-to-plasma coupling mechanism. Numerical simulations are a powerful tools for obtaining quantitative information about the wave-to-plasma interaction process: this paper presents a numerical study of the microwaves propagation and absorption inside the plasma chamber of the PHOENIX charge breeder, which the selective production of exotic species project, under construction at Legnaro National Laboratories, will adopt as charge breeder. Calculations were carried out with a commercial 3D FEM solver: first, all the resonant frequencies were determined by considering a simplified plasma chamber; then, the realistic geometry was taken into account, including a cold plasma model of increasing complexity. The results gave important information about the power absorption and losses and will allow the improvement of the plasma model to be used in a refined step of calculation reproducing the breeding process itself

Semi-supervised cross-lingual speech emotion recognition

Speech emotion recognition (SER) on a single language has achieved remarkable results through deep learning approaches over the last decade. However, cross-lingual SER remains a challenge in real-world applications due to (i) a large difference between the source and target domain distributions, (ii) the availability of few labeled and many unlabeled utterances for the new language. Taking into account previous aspects, we propose a Semi-Supervised Learning (SSL) method for cross-lingual emotion recognition when a few labels from the new language are available. Based on a Convolutional Neural Network (CNN), our method adapts to a new language by exploiting a pseudo-labeling strategy for the unlabeled utterances. In particular, the use of a hard and soft pseudo-labels approach is investigated. We thoroughly evaluate the performance of the method in a speaker-independent setup on both the source and the new language and show its robustness across five languages belonging to different linguistic strains

Experimental investigation of non-linear wave to plasma interaction in a quasi-flat magnetostatic field

A characterization of wave-to-plasma interaction in a quasi-flat magnetostatic field at 3.75 GHz has been carried out by using a small-wire movable RF antenna, connected to a spectrum analyzer. The coupling between electromagnetic and electrostatic waves leads to a characteristic spectral emission in low frequency range and around the pumping wave frequency. The most relevant results consist in the broadening of the pumping wave spectrum above critical RF power thresholds and in the generation of sidebands of the pumping frequency, with corresponding components in low frequency domain. The non-linearities are accompanied by the generation of overdense plasmas and intense fluxes of X-rays

Dependence on Frequency of the Electromagnetic Field Distribution inside a Cylindrical CavityExcited through an Off-Axis Aperture

To explain the relevant changes in the electron cyclotron resonance ion source behaviour for small variations of the exciting radiation frequency, we determine the spatial distribution of the field within the cavity for every resonant mode

Innovative Analytical Method for X-ray Imaging and Space-Resolved Spectroscopy of ECR Plasmas

At the Italian National Institute for Nuclear Physics-Southern National Laboratory (INFN-LNS), and in collaboration with the ATOMKI laboratories, an innovative multi-diagnostic system with advanced analytical methods has been designed and implemented. This is based on several detectors and techniques (Optical Emission Spectroscopy, RF systems, interfero-polarimetry, X-ray detectors), and here we focus on high-resolution, spatially resolved X-ray spectroscopy, performed by means of a X-ray pin-hole camera setup operating in the 0.5–20 keV energy domain. The diagnostic system was installed at a 14 GHz Electron Cyclotron Resonance (ECR) ion source (ATOMKI, Debrecen), enabling high-precision, X-ray, spectrally resolved imaging of ECR plasmas heated by hundreds of Watts. The achieved spatial and energy resolutions were 0.5 mm and 300 eV at 8 keV, respectively. Here, we present the innovative analysis algorithm that we properly developed to obtain Single Photon-Counted (SPhC) images providing the local plasma-emitted spectrum in a High-Dynamic-Range (HDR) mode, by distinguishing fluorescence lines of the materials of the plasma chamber (Ti, Ta) from plasma (Ar). This method allows for a quantitative characterization of warm electrons population in the plasma (and its 2D distribution), which are the most important for ionization, and to estimate local plasma density and spectral temperatures. The developed post-processing analysis is also able to remove the readout noise that is often observable at very low exposure times (msec). The setup is now being updated, including fast shutters and trigger systems to allow simultaneous space and time-resolved plasma spectroscopy during transients, stable and turbulent regimes