3,508 research outputs found
View-Action Representation Learning for Active First-Person Vision
In visual navigation, a moving agent equipped with a camera is traditionally controlled by an input action and the estimation of the features from a sensory state (i.e. the camera view) is treated as a pre-processing step to perform high-level vision tasks. In this paper, we present a representation learning approach that, instead, considers both state and action as inputs. We condition the encoded feature from the state transition network on the action that changes the view of the camera, thus describing the scene more effectively. Specifically, we introduce an action representation module that generates decoded higher dimensional representations from an input action to increase the representational power. We then fuse the output from the action representation module with the intermediate response of the state transition network that predicts the future state. To enhance the discrimination capability among predictions from different input actions, we further introduce triplet ranking loss and N-tuplet loss functions, which in turn can be integrated with the regression loss. We demonstrate the proposed representation learning approach in reinforcement and imitation learning-based mapless navigation tasks, where the camera agent learns to navigate only through the view of the camera and the performed action, without external information
Ballistic charge transport in a triple-gate silicon nanowire transistor
In this paper we investigate the electrostatics and charge transport in a triplegate
Silicon Nanowire transistor. The quantum confinement in the transversal dimension
of the wire have been tackled using the Schr¨odinger equation in the Effective Mass Approximation
coupled to the Poisson equation. This system have been solved efficiently
using a Variational Method. The charge transport along the longitudinal dimension of the
wire has been considered using the semiclassical approximation, in the ballistic regime
CLUSTER-BASED 3D KEYPOINT DETECTION FOR CATEGORY-AGNOSTIC 6D POSE TRACKING
We present a model for category-agnostic 6D pose tracking. We tackle object pose tracking as a 3D keypoint detection and matching task that does not require ground-truth annotation of the keypoints. Using RGB-D data and the target object mask as inputs, we spatially segment the point cloud of the object into clusters. Each 3D point in the cluster is characterised by features encoding appearance and geometric information. We use these features to detect a keypoint for each cluster and, with the detected keypoint sets from two time instants, we recover the pose change through least-squares optimisation. The loss functions are designed to ensure that the detected keypoints are consistent over time and suitable for pose tracking
Automated detection of extended sources in radio maps: progress from the SCORPIO survey
Automated source extraction and parameterization represents a crucial
challenge for the next-generation radio interferometer surveys, such as those
performed with the Square Kilometre Array (SKA) and its precursors. In this
paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source
Automated Recognition), to detect and parametrize extended sources in radio
interferometric maps. It is based on a pre-filtering stage, allowing image
denoising, compact source suppression and enhancement of diffuse emission,
followed by an adaptive superpixel clustering stage for final source
segmentation. A parameterization stage provides source flux information and a
wide range of morphology estimators for post-processing analysis. We developed
CAESAR in a modular software library, including also different methods for
local background estimation and image filtering, along with alternative
algorithms for both compact and diffuse source extraction. The method was
applied to real radio continuum data collected at the Australian Telescope
Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU
survey. The source reconstruction capabilities were studied over different test
fields in the presence of compact sources, imaging artefacts and diffuse
emission from the Galactic plane and compared with existing algorithms. When
compared to a human-driven analysis, the designed algorithm was found capable
of detecting known target sources and regions of diffuse emission,
outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure
Safely dissolvable and healable active packaging films based on alginate and pectin
Extensive usage of long-lasting petroleum based plastics for short-lived application such as packaging has raised concerns regarding their role in environmental pollution. In this research, we have developed active, healable, and safely dissolvable alginate-pectin based biocomposites that have potential applications in food packaging. The morphological study revealed the rough surface of these biocomposite films. Tensile properties indicated that the fabricated samples have mechanical properties in the range of commercially available packaging films while possessing excellent healing effciency. Biocomposite films exhibited higher hydrophobicity properties compared to neat alginate films. Thermal analysis indicated that crosslinked biocomposite samples possess higher thermal stability in temperatures below 120 °C, while antibacterial analysis against E. coli and S. aureus revealed the antibacterial properties of the prepared samples against different bacteria. The fabricated biodegradable multi-functional biocomposite films possess various imperative properties, making them ideal for utilization as packaging material
Semantically Adversarial Learnable Filters
We present an adversarial framework to craft perturbations that mislead classifiers by accounting for the image content and the semantics of the labels. The proposed framework combines a structure loss and a semantic adversarial loss in a multi-task objective function to train a fully convolutional neural network. The structure loss helps generate perturbations whose type and magnitude are defined by a target image processing filter. The semantic adversarial loss considers groups of (semantic) labels to craft perturbations that prevent the filtered image from being classified with a label in the same group. We validate our framework with three different target filters, namely detail enhancement, log transformation and gamma correction filters; and evaluate the adversarially filtered images against three classifiers, ResNet50, ResNet18 and AlexNet, pre-trained on ImageNet. We show that the proposed framework generates filtered images with a high success rate, robustness, and transferability to unseen classifiers. We also discuss objective and subjective evaluations of the adversarial perturbations
Design and Analysis of Heterogeneous DSP/FPGA Based Architectures for 3GPP Wireless Systems
This paper shows how iterative hardware/software partitioning in heterogeneous DSP/FPGA based embedded systems can be utilized to achieve real-time deadlines of modern 3GPP wireless equalization workloads. By utilizing a well defined set of application partitioning criteria in tandem with SOC simulation tools, we are able
to show a greater than six fold improvement in application performance
and ultimately meet, and even exceed real-time data processing deadlines
The MAGNEX spectrometer: results and perspectives
This article discusses the main achievements and future perspectives of theMAGNEX spectrometer at the INFN-LNS laboratory in Catania (Italy). MAGNEX is alarge acceptance magnetic spectrometer for the detection of the ions emitted innuclear collisions below Fermi energy. In the first part of the paper anoverview of the MAGNEX features is presented. The successful application to theprecise reconstruction of the momentum vector, to the identification of the ionmasses and to the determination of the transport efficiency is demonstrated byin-beam tests. In the second part, an overview of the most relevant scientificachievements is given. Results from nuclear elastic and inelastic scattering aswell as from transfer and charge exchange reactions in a wide range of massesof the colliding systems and incident energies are shown. The role of MAGNEX insolving old and new puzzles in nuclear structure and direct reaction mechanismsis emphasized. One example is the recently observed signature of the longsearched Giant Pairing Vibration. Finally, the new challenging opportunities touse MAGNEX for future experiments are briefly reported. In particular, the useof double charge exchange reactions toward the determination of the nuclearmatrix elements entering in the expression of the half-life of neutrinolessdouble beta decay is discussed. The new NUMEN project of INFN, aiming at theseinvestigations, is introduced. The challenges connected to the major technicalupgrade required by the project in order to investigate rare processes underhigh fluxes of detected heavy ions are outlined
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