169,729 research outputs found
Distributed Tensor Completion Over Networks
No full textThe 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
No full textThe 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
No full textIn 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 SIGNAL ESTIMATION IN ENERGY HARVESTING WIRELESS SENSOR NETWORKS
No full textWe study decentralized estimation of time-varying signals at a fusion center (FC), when energy harvesting sensors transmit sampled data over rate-constrained links We propose a dynamic strategy based on stochastic optimization for selecting radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal with guaranteed performance while ensuring stability of the batteries around a prescribed operating level. Numerical results validate the proposed approach for dynamic signal estimation under communication and energy constraints
Graph Convolutional Networks with Autoencoder-Based Compression and Multi-Layer Graph Learning
No full textThis work aims to propose a novel architecture and training strategy for graph convolutional networks (GCN). The proposed architecture, named Autoencoder-Aided GCN (AA-GCN), compresses the convolutional features in an information-rich embedding at multiple hidden layers, exploiting the presence of autoencoders before the point-wise nonlinearities. Then, we propose a novel end-to-end training procedure that learns different graph representations per layer, jointly with the GCN weights and auto-encoder parameters. As a result, the proposed strategy improves the computational scalability of the GCN, learning the best graph representations at each layer in a data-driven fashion. Several numerical results on synthetic and real data illustrate how our architecture and training procedure compares favorably with other state-of-the-art solutions, both in terms of robustness and learning performance
Dynamic resource optimization for decentralized estimation in energy harvesting IoT networks
No full textWe 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
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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