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

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

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

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

Exploring the multifaceted circumstellar environment of the luminous blue variable HR Carinae

Indexación: Web of Science; Scopus.We present a multiwavelength study of the Galactic luminous blue variable HR Carinae, based on new high-resolution mid-infrared (IR) and radio images obtained with the Very Large Telescope (VLT) and the Australia Telescope Compact Array (ATCA), which have been complemented by far-infrared Herschel-Photodetector Array Camera and Spectrometer (PACS) observations and ATCA archive data. The Herschel images reveal the large-scale distribution of the dusty emitting nebula, which extends mainly to the north-east direction, up to 70 arcsec from the central star, and is oriented along the direction of the space motion of the star. In the mid-infrared images, the brightness distribution is characterized by two arcshaped structures, tracing an inner envelope surrounding the central star more closely. At radio wavelengths, the ionized gas emission lies on the opposite side of the cold dust with respect to the position of the star, as if the ionized front were confined by the surrounding medium in the north-south direction. Comparison with previous data indicates significant changes in the radio nebula morphology and in the mass-loss rate from the central star, which has increased from 6.1 × 10-6M⊙ yr-1 in 1994-1995 to 1.17 × 10-5M⊙ yr-1 in 2014. We investigate possible scenarios that could have generated the complex circumstellar environment revealed by our multiwavelength data.https://academic.oup.com/mnras/article-lookup/doi/10.1093/mnras/stw307