A Generic Information and Consent Framework for the IoT

The Internet of Things (IoT) raises specific issues in terms of information and consent, which makes the implementation of the General Data Protection Regulation (GDPR) challenging in this context. In this report, we propose a generic framework for information and consent in the IoT which is protective both for data subjects and for data controllers. We present a high level description of the framework, illustrate its generality through several technical solutions and case studies, and sketch a prototype implementation

Episodic Learning with Control Lyapunov Functions for Uncertain Robotic Systems

Many modern nonlinear control methods aim to endow systems with guaranteed properties, such as stability or safety, and have been successfully applied to the domain of robotics. However, model uncertainty remains a persistent challenge, weakening theoretical guarantees and causing implementation failures on physical systems. This paper develops a machine learning framework centered around Control Lyapunov Functions (CLFs) to adapt to parametric uncertainty and unmodeled dynamics in general robotic systems. Our proposed method proceeds by iteratively updating estimates of Lyapunov function derivatives and improving controllers, ultimately yielding a stabilizing quadratic program model-based controller. We validate our approach on a planar Segway simulation, demonstrating substantial performance improvements by iteratively refining on a base model-free controller

On Miami shore: waltz

https://digitalcommons.ithaca.edu/sheetmusic/1107/thumbnail.jp

Someday

From the Paramount Picture The Great Victor Herberthttps://digitalcommons.ithaca.edu/sheetmusic/1243/thumbnail.jp

On deterministic error analysis in variational data assimilation

International audienceThe problem of variational data assimilation for a nonlinear evolution model is considered to identify the initial condition. The equation for the error of the optimal initial-value function through the errors of the input data is derived, based on the Hessian of the misfit functional and the second order adjoint techniques. The fundamental control functions are introduced to be used for error analysis. The sensitivity of the optimal solution to the input data (observation and model errors, background errors) is studied using the singular vectors of the specific response operators in the error equation. The relation between "quality of the model" and "quality of the prediction" via data assimilation is discussed

Ubiquitin plays an atypical role in GPCR-induced p38 MAP kinase activation on endosomes.

Protease-activated receptor 1 (PAR1) is a G protein-coupled receptor (GPCR) for thrombin and promotes inflammatory responses through multiple pathways including p38 mitogen-activated protein kinase signaling. The mechanisms that govern PAR1-induced p38 activation remain unclear. Here, we define an atypical ubiquitin-dependent pathway for p38 activation used by PAR1 that regulates endothelial barrier permeability. Activated PAR1 K63-linked ubiquitination is mediated by the NEDD4-2 E3 ubiquitin ligase and initiated recruitment of transforming growth factor-β-activated protein kinase-1 binding protein-2 (TAB2). The ubiquitin-binding domain of TAB2 was essential for recruitment to PAR1-containing endosomes. TAB2 associated with TAB1, which induced p38 activation independent of MKK3 and MKK6. The P2Y1 purinergic GPCR also stimulated p38 activation via NEDD4-2-mediated ubiquitination and TAB1-TAB2. TAB1-TAB2-dependent p38 activation was critical for PAR1-promoted endothelial barrier permeability in vitro, and p38 signaling was required for PAR1-induced vascular leakage in vivo. These studies define an atypical ubiquitin-mediated signaling pathway used by a subset of GPCRs that regulates endosomal p38 signaling and endothelial barrier disruption