Leaderless synchronization of heterogeneous oscillators by adaptively learning the group model

International audienc

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

Dual estimation: Constructing building energy models from data sampled at low rate

AbstractEstimation of energy models from data is an important part of advanced fault detection and diagnosis tools for smart energy purposes. Estimated energy models can be used for a large variety of management and control tasks, spanning from model predictive building control to estimation of energy consumption and user behavior. In practical implementation, problems to be considered are the fact that some measurements of relevance are missing and must be estimated, and the fact that other measurements, collected at low sampling rate to save memory, make discretization of physics-based models critical. These problems make classical estimation tools inadequate and call for appropriate dual estimation schemes where states and parameters of a system are estimated simultaneously. In this work we develop dual estimation schemes based on Extended Kalman Filtering (EKF) and Unscented Kalman Filtering (UKF) for constructing building energy models from data: in order to cope with the low sampling rate of data (with sampling time 15min), an implicit discretization (Euler backward method) is adopted to discretize the continuous-time heat transfer dynamics. It is shown that explicit discretization methods like the Euler forward method, combined with 15min sampling time, are ineffective for building reliable energy models (the discrete-time dynamics do not match the continuous-time ones): even explicit methods of higher order like the Runge–Kutta method fail to provide a good approximation of the continuous-time dynamics which such large sampling time. Either smaller time steps or alternative discretization methods are required. We verify that the implicit Euler backward method provides good approximation of the continuous-time dynamics and can be easily implemented for our dual estimation purposes. The applicability of the proposed method in terms of estimation of both states and parameters is demonstrated via simulations and using historical data from a real-life building