Tangent Bundle Filters and Neural Networks: From Manifolds to Cellular Sheaves and Back

In this work we introduce a convolution operation over the tangent bundle of Riemannian manifolds exploiting the Connection Laplacian operator. We use this convolution operation to define tangent bundle filters and tangent bundle neural networks (TNNs), novel continuous architectures operating on tangent bundle signals, i.e. vector fields over manifolds. We discretize TNNs both in space and time domains, showing that their discrete counterpart is a principled variant of the recently introduced Sheaf Neural Networks. We formally prove that this discrete architecture converges to the underlying continuous TNN. We numerically evaluate the effectiveness of the proposed architecture on a denoising task of a tangent vector field over the unit 2-sphere

Distributed tensor completion over networks

The aim of this paper is to propose a novel distributed strategy for tensor completion, where (partial) data are collected over a network of agents with sparse, but connected, topology. The method hinges on the canonical polyadic decomposition, also known as PARAFAC, to complete the low-rank tensor in a distributed fashion. To deal with the nonconvex and distributed nature of the learning problem, we exploit a convexification/decomposition technique based on successive convex approximations, while using dynamic consensus to diffuse information over the network and force asymptotic agreement among the agents. Asymptotic convergence to stationary solutions of the centralized problem is established under mild conditions. Finally, numerical results assess the performance of the proposed method over both synthetic and real data

Dynamic resource optimization for adaptive federated learning at the wireless network edge

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient descent (SGD) to perform distributed learning and adaptation. Hinging on Lyapunov stochastic optimization tools, we dynamically optimize radio parameters (i.e., set of transmitting devices, transmit powers) and computation resources (i.e., CPU cycles at devices and at server) in order to strike the best trade-off between energy, latency, and performance of the federated learning task. The general framework is then customized to the case of federated least mean squares (LMS) estimation. Numerical results illustrate the effectiveness of our strategy to perform energy-efficient, low-latency, federated machine learning at the wireless network edge

Ruolo dei nuovi farmaci nel tumore del polmone

In recent years, the epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF) have been recognized as a central players and regulators of cancer cell proliferation, apoptosis and angiogenesis and, therefore, as a potentially relevant therapeutic target. Several strategies for EGFR and VEGF targeting have been developed, the most successful being represented by monoclonal antibodies (MAbs), that directly interfere with ligand-receptor binding and small molecule tyrosine kinase inhibitors (TKIs), that interfere with activation/phophorylation of EGFR and VEGF. These agents have been authorized in advanced cancers, including , non small cell lung cancer (NSCLC)Negli ultimi decenni il contributo degli studi di biologia molecolare ed in particolare la conoscenza dell’intera sequenza del genoma umano, hanno consentito di identificare nuovi bersagli farmacologici in grado di interferire con eventi chiave della trasformazione e proliferazione della cellula neoplastica.. Nel contesto degli agenti biologici mirati, gli inibitori dell’EGFR e del VEGF sembrano essere i più promettenti per la terapia di alcune neoplasie tra cui il tumore del polmone non a piccole cellule

Dynamic resource optimization for decentralized estimation in energy harvesting IoT networks

We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: i) accuracy of the recovery procedure, and ii) stability of the batteries around a prescribed operating level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet of Things (IoT) scenarios