CAR-Net: Clairvoyant Attentive Recurrent Network

We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the agent. This allows us to visualize fine-grained semantic elements of navigation scenes that influence the prediction of trajectories. To study the impact of space on agents' trajectories, we build a new dataset made of top-view images of hundreds of scenes (Formula One racing tracks) where agents' behaviors are heavily influenced by known areas in the images (e.g., upcoming turns). CAR-Net successfully attends to these salient regions. Additionally, CAR-Net reaches state-of-the-art accuracy on the standard trajectory forecasting benchmark, Stanford Drone Dataset (SDD). Finally, we show CAR-Net's ability to generalize to unseen scenes.Comment: The 2nd and 3rd authors contributed equall

Low energy radioactive ion beams at SPES for nuclear physics and medical applications

Over the past decades many accelerator facilities have been built in order to produce radioactive nuclei. Among the falcility under construction, SPES (Selective Production of Exotic Species) is the Italian ISOL (Isotope Separation On Line) facility in the installation phase in these years in the Laboratori Nazionali di Legnaro. The innovative aspect of this facility is that the radioactive beam produced by fission induced by the proton beam, produced by a high power cyclotron, interact with a multi-disks uranium carbide target. The formed RIB will be sent directly to the low energy experimental area and, afterwards, to the post-acceleration complex. Currently the installation program concerning the SPES RIB source provides the set-up of the apparatus around the production bunker. The main objective of SPES project is to provide, in the next years, the first low-energy radioactive beams for beta decay experiments using the b-DS (beta Decay Station) set-up and for radiopharmaceutical applications by means of the IRIS (ISOLPHARM Radioactive Implantation Station) apparatus. In this work, all the specific issues related to the SPES RIB and the Low Energy beam lines will be reported. The main RIB systems, such as ion source systems, target-handling devices and the installation of low energy transport line, will be presented in detail

Whistleblowers as regulatory intermediaries: Instrumental and reflexive considerations in decentralizing regulation

This article frames whistleblowers as regulatory intermediaries who provide a response to the problem posed by the fragmentation of knowledge in a complex society and market economy. I identify two ways in which whistleblowers become regulatory intermediaries: The first is by remedying informational asymmetries between the regulator and the target (instrumental approach). Both in the United States and in the European Union, whistleblowers are protected on the basis of the value of the disclosed information for the advancement of regulatory objectives. The second way in which whistleblowers become regulatory intermediaries is by contributing to the development of “communities of compliance” and by enhancing the internal self-regulatory capacities of regulatory targets (reflexive approach). Creating internal channels of reporting and monitoring is perceived as a way to change the organizational culture of targets. Through the instrumentalism – reflexivity dipole, competing rationales and normative visions of regulatory intermediation become apparent: It could, on the one hand, facilitate state intervention and legal sanctions or, on the other hand, signal the aspiration to embed public and social values in private actors

Covalent enzyme coupling on cellulose acetate membranes for glucose sensor development

International audienceMethods for immobilizing glucose oxidase (GOx) on cellulose acetate (CA) membranes are compared. The optimal method involves covalent coupling of bovine serum albumin (BSA) to CA membrane and a subsequent reaction of the membrane with GOx, which has previously been activated with an excess of p-benzoquinone. This coupling procedure is fairly reproducible and allows the preparation of thin membranes (5-20 µm) showing high surface activities (1-3 U/cm2) which are stable over a period of 1-3 months. Electrochemical and radiolabeling experiments show that enzyme inactivation as a result of immobilization is negligible. A good correlation between surface activity of membranes and their GOx load is observed

A software-based self-test strategy for on-line testing of the scan chain circuitries in embedded microprocessors

Nowadays, Software-Based Self-Test (SBST) is growing in importance especially in the on-line test scenario for safety critical systems such as automotive. This paper concentrates on the coverage by SBST of those faults in the scan chain that can impact the behavior of the embedded processor while working in its application field. A technique is described that is able to systematically tackle these faults after a scan chain analysis. Results are demonstrating the effectiveness and showing the costs of the proposed approach on a 32-bit embedded processor included in an industrial System-on-Chip used in the automotive field. © 2013 IEEE

Long-term behaviour recognition in videos with actor-focused region attention

Long-Term activities involve humans performing complex, minutes-long actions. Differently than in traditional action recognition, complex activities are normally composed of a set of sub-actions, that can appear in different order, duration, and quantity. These aspects introduce a large intra-class variability, that can be hard to model. Our approach aims to adaptively capture and learn the importance of spatial and temporal video regions for minutes-long activity classification. Inspired by previous work on Region Attention, our architecture embeds the spatio-temporal features from multiple video regions into a compact fixed-length representation. These features are extracted with a 3D convolutional backbone specially fine-tuned. Additionally, driven by the prior assumption that the most discriminative locations in the videos are centered around the human that is carrying out the activity, we introduce an Actor Focus mechanism to enhance the feature extraction both in training and inference phase. Our experiments show that the Multi-Regional fine-tuned 3D-CNN, topped with Actor Focus and Region Attention, largely improves the performance of baseline 3D architectures, achieving state-of-the-art results on Breakfast, a well known long-term activity recognition benchmark.Pattern Recognition and BioinformaticsBUS/TNO STAF