Evidence of triggered star formation in G327.3-0.6. Dust-continuum mapping of an infrared dark cloud with P-ArT\'eMiS

Aims. Expanding HII regions and propagating shocks are common in the environment of young high-mass star-forming complexes. They can compress a pre-existing molecular cloud and trigger the formation of dense cores. We investigate whether these phenomena can explain the formation of high-mass protostars within an infrared dark cloud located at the position of G327.3-0.6 in the Galactic plane, in between two large infrared bubbles and two HII regions. Methods: The region of G327.3-0.6 was imaged at 450 ? m with the CEA P-ArT\'eMiS bolometer array on the Atacama Pathfinder EXperiment telescope in Chile. APEX/LABOCA and APEX-2A, and Spitzer/IRAC and MIPS archives data were used in this study. Results: Ten massive cores were detected in the P-ArT\'eMiS image, embedded within the infrared dark cloud seen in absorption at both 8 and 24 ?m. Their luminosities and masses indicate that they form high-mass stars. The kinematical study of the region suggests that the infrared bubbles expand toward the infrared dark cloud. Conclusions: Under the influence of expanding bubbles, star formation occurs in the infrared dark areas at the border of HII regions and infrared bubbles.Comment: 4 page

Big data analytics for time critical maritime and aerial mobility forecasting

The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics

EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design

The radio transceiver of an IoT device is often where most of the energy is consumed. For this reason, most research so far has focused on low power circuit and energy efficient physical layer designs, with the goal of reducing the average energy per information bit required for communication. While these efforts are valuable per se, their actual effectiveness can be partially neutralized by ill-designed network, processing and resource management solutions, which can become a primary factor of performance degradation, in terms of throughput, responsiveness and energy efficiency. The objective of this paper is to describe an energy-centric and context-aware optimization framework that accounts for the energy impact of the fundamental functionalities of an IoT system and that proceeds along three main technical thrusts: 1) balancing signal-dependent processing techniques (compression and feature extraction) and communication tasks; 2) jointly designing channel access and routing protocols to maximize the network lifetime; 3) providing self-adaptability to different operating conditions through the adoption of suitable learning architectures and of flexible/reconfigurable algorithms and protocols. After discussing this framework, we present some preliminary results that validate the effectiveness of our proposed line of action, and show how the use of adaptive signal processing and channel access techniques allows an IoT network to dynamically tune lifetime for signal distortion, according to the requirements dictated by the application

Optimal column layout for hybrid workloads

Data-intensive analytical applications need to support both efficient reads and writes. However, what is usually a good data layout for an update-heavy workload, is not well-suited for a read-mostly one and vice versa. Modern analytical data systems rely on columnar layouts and employ delta stores to inject new data and updates. We show that for hybrid workloads we can achieve close to one order of magnitude better performance by tailoring the column layout design to the data and query workload. Our approach navigates the possible design space of the physical layout: it organizes each column’s data by determining the number of partitions, their corresponding sizes and ranges, and the amount of buffer space and how it is allocated. We frame these design decisions as an optimization problem that, given workload knowledge and performance requirements, provides an optimal physical layout for the workload at hand. To evaluate this work, we build an in-memory storage engine, Casper, and we show that it outperforms state-of-the-art data layouts of analytical systems for hybrid workloads. Casper delivers up to 2.32x higher throughput for update-intensive workloads and up to 2.14x higher throughput for hybrid workloads. We further show how to make data layout decisions robust to workload variation by carefully selecting the input of the optimization.http://www.vldb.org/pvldb/vol12/p2393-athanassoulis.pdfPublished versionPublished versio

Communications-Inspired Projection Design with Application to Compressive Sensing

We consider the recovery of an underlying signal x \in C^m based on projection measurements of the form y=Mx+w, where y \in C^l and w is measurement noise; we are interested in the case l < m. It is assumed that the signal model p(x) is known, and w CN(w;0,S_w), for known S_W. The objective is to design a projection matrix M \in C^(l x m) to maximize key information-theoretic quantities with operational significance, including the mutual information between the signal and the projections I(x;y) or the Renyi entropy of the projections h_a(y) (Shannon entropy is a special case). By capitalizing on explicit characterizations of the gradients of the information measures with respect to the projections matrix, where we also partially extend the well-known results of Palomar and Verdu from the mutual information to the Renyi entropy domain, we unveil the key operations carried out by the optimal projections designs: mode exposure and mode alignment. Experiments are considered for the case of compressive sensing (CS) applied to imagery. In this context, we provide a demonstration of the performance improvement possible through the application of the novel projection designs in relation to conventional ones, as well as justification for a fast online projections design method with which state-of-the-art adaptive CS signal recovery is achieved.Comment: 25 pages, 7 figures, parts of material published in IEEE ICASSP 2012, submitted to SIIM