Leaderless synchronization of heterogeneous oscillators by adaptively learning the group model

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FSTL5 expression is a marker of Group C metastatic medulloblastomas

INTRODUCTION: Medulloblastoma (MB) is the most commonmalignant brain tumor in children. Four different molecular subgroups are recognized, which differ in gene expression, genomic aberrations, histology, demographics and survival:WNT and SHH groups, having specific mutations in the homonymous pathway, and groups C and D having several genetic alternations not specific to a single pathway. The gene for follistatin-like protein 5, FSTL5, is overexpressed in nonSHH/nonWNT MBs poorly characterized. Highexpression of FSTL5 is significantly associated with reduced event-free and overall survival in non-WNT/non-SHHMBs. The major aim of this project is to study the FSTL5 expression level in pediatric MBs with metastasis at the onset. METHOD: We investigated the protein expression of biomarkers involved in metastatic pathways by IHC and FSTL5 expression level by RT-PCR in 26 metastatic MBs samples and correlated these data with the outcomes by Kaplan-Meier statistic analysis. RESULTS: 83% of Group C MBs showed high level of FSTL5 while none of these presented down-expression. Low-expression level of FSTL5 was find in 60% of SHH MBs and none showed over-expression. Kaplan-Meier test revealed that, in our cohort, highexpression ofFSTL5didnot correlatewithworse outcomewhile lowexpression of FSTL5 was associated with good prognosis and the co-presence of FSTL5 with other biomarkers correlated with poorer prognosis. CONCLUSION: FSTL5 is a marker of Group C in medulloblastomas with metastasis at the onset and the results highlighted decreased FSTL5 expression as a marker of good prognosis. Group C MBs have characteristic molecular features that confirm the poorest outcome also inMBs with metastasis at the onset

On recursive temporal difference and eligibility traces

This work studies a new reinforcement learning method in the framework of Recursive Least-Squares Temporal Difference (RLS-TD). Differently from the standard mechanism of eligibility traces, leading to RLS-TD(λ), in this work we show that the forgetting factor commonly used in gradient-based estimation has a similar role to the mechanism of eligibility traces. We adopt an instrumental variable perspective to illustrate this point and we propose a new algorithm, namely - RLS-TD with forgetting factor (RLS-TD-f). We test the proposed algorithm in a Policy Iteration setting, i.e. when the performance of an initially stabilizing controller must be improved. We take the cart-pole benchmark as experimental platform: extensive experiments show that the proposed RLS-TD algorithm exhibits larger performance improvements in the largest portion of the state space

Plug-and-play adaptation in autopilot architectures for unmanned aerial vehicles

An accepted autopilot control architecture for fixed-wing unmanned aerial vehicles (UAVs) is the so-called cascaded loop closure, in which inner velocity loops and outer position loops are successively closed with proportional-integral-derivative (PID) controllers. This architecture has become so standard that popular open-source autopilots (e.g. ArduPilot, PX4) implement it in their codes. Despite its popularity, such architecture cannot adequately cope with the inevitable uncertainty in the UAV dynamics. In this work we present a "plug-and-play" adaptive module integrated in standard cascaded autopilot architectures, so as to can guarantee adaptation in the presence of uncertainty. The proposed module is analyzed and tested in a software-in-the-loop environment for an ArduPilot-based autopilot. The tests show that, in the presence of uncertainties occurring during flight, the proposed adaptation module outperforms the original autopilot as well as non-adaptive autopilots

On Distributed Implementation of Switch-Based Adaptive Dynamic Programming

Switch-based adaptive dynamic programming (ADP) is an optimal control problem in which a cost must be minimized by switching among a family of dynamical modes. When the system dimension increases, the solution to switch-based ADP is made prohibitive by the exponentially increasing structure of the value function approximator and by the exponentially increasing modes. This technical correspondence proposes a distributed computational method for solving switch-based ADP. The method relies on partitioning the system into agents, each one dealing with a lower dimensional state and a few local modes. Each agent aims to minimize a local version of the global cost while avoiding that its local switching strategy has conflicts with the switching strategies of the neighboring agents. A heuristic algorithm based on the consensus dynamics and Nash equilibrium is proposed to avoid such conflicts. The effectiveness of the proposed method is verified via traffic and building test cases

Unsupervised detection of botnet activities using frequent pattern tree mining

A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-serviceattacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadilyincreased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and moredifficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and manyothers). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field ofdata mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item miningalgorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysisof rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervisedfashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peerhost PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of theproposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature

Adaptation to Unknown Leader Velocity in Vector-field UAV Formation

This paper presents a new adaptive method forformation control of unmanned aerial vehicles (UAVs) withlimited leader information and communication. We study aformation control protocol in the framework of vector-fieldguidance where the leader can communicate its position andorientation but not its velocity. A practical motivation for thisscenario is the so-called congestion-aware control, in whichtrade-offs between the density of unmanned vehicles andcommunication interference caused by many communicatingvehicles arise: these trade-offs may require to reduce thecommunication load to avoid interference. To compensate forthe lack of knowledge of the leader velocity, each UAV makesuse of a local estimation mechanism. The resulting method isan adaptive control method, whose stability can be establishedusing Lyapunov stability. We show that the method can beextended to a distributed communication setting with a fewneighboring UAVs in place of the leader. Extensive simulationswith different formation shapes (Y, V and T formation) showthat the proposed adaptation mechanism effectively achieves theformation despite the unknown leader velocity. The proposedmechanism has a very similar performance to the ideal casewhen the leader velocity is perfectly known, and outperforms allthe non-adaptive cases in which the followers have an incorrectknowledge of the leader velocity.Keywords: Vector field, formation control, local estimation,unknown leader velocity, adaptive control