Filippo Zanella

Newton-Raphson Consensus for Distributed Convex Optimization

We address the problem of distributed unconstrained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local private multidimensional convex cost, is subject to communication constraints, and wants to collaborate to compute the minimizer of the sum of the local costs. We propose a design methodology that combines average consensus algorithms and separation of time-scales ideas. This strategy is proved, under suitable hypotheses, to be globally convergent to the true minimizer. Intuitively, the procedure lets the agents distributedly compute and sequentially update an approximated Newton-Raphson direction by means of suitable average consensus ratios. We show with numerical simulations that the speed of convergence of this strategy is comparable with alternative optimization strategies such as the Alternating Direction Method of Multipliers. Finally, we propose some alternative strategies which trade-off communication and computational requirements with convergence speed.

Asynchronous Newton-Raphson Consensus for Distributed Convex Optimization

We consider the distributed unconstrained minimization of separable convex cost functions, where the global cost is given by the sum of several local and private costs, each associated to a specific agent of a given communication network. We specifically address an asynchronous distributed optimization technique called Newton-Raphson Consensus. Beside having low computational complexity, low communication requirements and being interpretable as a distributed Newton-Raphson algorithm, the technique has also the beneficial properties of requiring very little coordination and naturally supporting time-varying topologies. In this work we analytically prove that under some assumptions it shows either local or global convergence properties, and corroborate this result by the means of numerical simulations.

Multidimensional Newton-Raphson consensus for distributed convex optimization

In this work we consider a multidimensional distributed optimization technique that is suitable for multi-agents systems subject to limited communication connectivity. In particular, we consider a convex unconstrained additive problem, i.e. a case where the global convex unconstrained multidimensional cost function is given by the sum of local cost functions available only to the specific owning agents. We show how, by exploiting the separation of time-scales principle, the multidimensional consensus-based strategy approximates a Newton-Raphson descent algorithm. We propose two alternative optimization strategies corresponding to approximations of the main procedure. These approximations introduce tradeoffs between the required communication bandwidth and the convergence speed/accuracy of the results. We provide analytical proofs of convergence and numerical simulations supporting the intuitions developed through the paper.

Camera Network Coordination for Intruder Detection

This paper proposes surveillance trajectories for a network of autonomous cameras to detect intruders. We consider smart intruders, which appear at arbitrary times and locations, are aware of the cameras configuration, and move to avoid detection for as long as possible. As performance criteria, we consider the worst case detection time (WDT) and the average detection time (ADT). We focus on the case of a chain of cameras, and we obtain the following results. First, we characterize a lower bound on the WDT and on the ADT of smart intruders. Second, we propose a team trajectory for the cameras, namely equal-waiting trajectory, with minimum WDT and with guarantees on the ADT. Third, we design a distributed algorithm to coordinate the cameras along an equal-waiting trajectory. Fourth, we design a distributed algorithm for cameras reconfiguration in the case of failure or network change. Finally, we illustrate the effectiveness and robustness of our algorithms via numerical studies and experiments.

Chloroplast Ca2+ Fluxes into and across Thylakoids Revealed by Thylakoid-Targeted Aequorin Probes

Aequorin-based probes targeted to the thylakoid lumen and membrane reveal an integrated role for thylakoids in Ca2+ homeostasis and modulation of chloroplast Ca2+ signals. Chloroplasts require a fine-tuned control of their internal Ca2+ concentration, which is crucial for many aspects of photosynthesis and for other chloroplast-localized processes. Increasing evidence suggests that calcium regulation within chloroplasts also may influence Ca2+ signaling pathways in the cytosol. To investigate the involvement of thylakoids in Ca2+ homeostasis and in the modulation of chloroplast Ca2+ signals in vivo, we targeted the bioluminescent Ca2+ reporter aequorin as a YFP fusion to the lumen and the stromal surface of thylakoids in Arabidopsis (Arabidopsis thaliana). Thylakoid localization of aequorin-based probes in stably transformed lines was confirmed by confocal microscopy, immunogold labeling, and biochemical analyses. In resting conditions in the dark, free Ca2+ levels in the thylakoid lumen were maintained at about 0.5 μm, which was a 3- to 5-fold higher concentration than in the stroma. Monitoring of chloroplast Ca2+ dynamics in different intrachloroplast subcompartments (stroma, thylakoid membrane, and thylakoid lumen) revealed the occurrence of stimulus-specific Ca2+ signals, characterized by unique kinetic parameters. Oxidative and salt stresses initiated pronounced free Ca2+ changes in the thylakoid lumen. Localized Ca2+ increases also were observed on the thylakoid membrane surface, mirroring transient Ca2+ changes observed for the bulk stroma, but with specific Ca2+ dynamics. Moreover, evidence was obtained for dark-stimulated intrathylakoid Ca2+ changes, suggesting a new scenario for light-to-dark-induced Ca2+ fluxes inside chloroplasts. Hence, thylakoid-targeted aequorin reporters can provide new insights into chloroplast Ca2+ storage and signal transduction. These probes represent novel tools with which to investigate the role of thylakoids in Ca2+ signaling networks within chloroplasts and plant cells.

The convergence rate of Newton-Raphson consensus optimization for quadratic cost functions

We consider the convergence rates of two convex optimization strategies in the context of multi agent systems, namely the Newton-Raphson consensus optimization and a distributed Gradient-Descent opportunely derived from the first. To allow analytical derivations, the convergence analyses are performed under the simplificative assumption of quadratic local cost functions. In this framework we derive sufficient conditions which guarantee the convergence of the algorithms. From these conditions we then obtain closed form expressions that can be used to tune the parameters for maximizing the rate of convergence. Despite these formulae have been derived under quadratic local cost functions assumptions, they can be used as rules-of-thumb for tuning the parameters of the algorithms in general situations.

Simultaneous Boundary Partitioning and Cameras Synchronization for Optimal Video Surveillance

This paper proposes a real-time distributed algorithm for a team of smart cameras to self-organize and perform video surveillance of an open boundary. In particular, our algorithm simultaneously partitions the boundary among the cameras, and synchronizes the motion of the cameras to optimize the surveillance performance. We focus on the detection of smart intruders, who are aware of the cameras configuration at each time instant, and who schedule their motion to avoid detection for as long as possible. We consider both the worst-case and the average detection times of smart intruders. Our algorithm achieves minimum worst-case detection time, and, under some reasonable assumptions, constant-factor optimal average detection time.

Biocontrol traits of Bacillus licheniformis GL174 , a culturable endophyte of Vitis vinifera cv. Glera

BackgroundBacillus licheniformis GL174 is a culturable endophytic strain isolated from Vitis vinifera cultivar Glera, the grapevine mainly cultivated for the Prosecco wine production. This strain was previously demonstrated to possess some specific plant growth promoting traits but its endophytic attitude and its role in biocontrol was only partially explored. In this study, the potential biocontrol action of the strain was investigated in vitro and in vivo and, by genome sequence analyses, putative functions involved in biocontrol and plant-bacteria interaction were assessed.ResultsFirstly, to confirm the endophytic behavior of the strain, its ability to colonize grapevine tissues was demonstrated and its biocontrol properties were analyzed. Antagonism test results showed that the strain could reduce and inhibit the mycelium growth of diverse plant pathogens in vitro and in vivo. The strain was demonstrated to produce different molecules of the lipopeptide class; moreover, its genome was sequenced, and analysis of the sequences revealed the presence of many protein-coding genes involved in the biocontrol process, such as transporters, plant-cell lytic enzymes, siderophores and other secondary metabolites.ConclusionsThis step-by-step analysis shows that Bacillus licheniformis GL174 may be a good biocontrol agent candidate, and describes some distinguished traits and possible key elements involved in this process. The use of this strain could potentially help grapevine plants to cope with pathogen attacks and reduce the amount of chemicals used in the vineyard.

Channel Model Identification in Wireless Sensor Networks Using a Fully Distributed Quantized Consensus Algorithm

Abstract In this paper, we consider the problem of designing a distributed strategy to estimate the channel parameters for a generic Wireless Sensor-Actor Network (WSAN). To this aim, we present a distributed least-square algorithm that complies with the constraint of transmitting only integer data through the wireless communication, which often characterizes WSAN embedded architectures. In this respect, we propose a quantized consensus strategy that mitigates the effects of the rounding operations applied to the wireless exchanged floating data. Moreover, the approach is based on a symmetric random gossip strategy, making it suitable for the actual deployment in multiagent networks. Finally, the effectiveness of the proposed algorithm and of its implementation as an open-source application is assessed and the employment of the procedure is illustrated through the application to radio-frequency localization experiments in a real world testbed.