50 results on '"Di Lorenzo, Paolo"'
Search Results
2. Reducing the In band Network Telemetry overhead through the spatial sampling: Theory and experimental results
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Polverini, Marco, Sardellitti, Stefania, Barbarossa, Sergio, Cianfrani, Antonio, Di Lorenzo, Paolo, and Listanti, Marco
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- 2024
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3. Adaptive resource optimization for edge inference with goal-oriented communications
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Binucci, Francesco, Banelli, Paolo, Di Lorenzo, Paolo, and Barbarossa, Sergio
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- 2022
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4. Dynamic edge computing empowered by reconfigurable intelligent surfaces
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Di Lorenzo, Paolo, Merluzzi, Mattia, Calvanese Strinati, Emilio, and Barbarossa, Sergio
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- 2022
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5. Distributed data clustering over networks
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Altilio, Rosa, Di Lorenzo, Paolo, and Panella, Massimo
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- 2019
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6. Cytoreduction Plus Hyperthermic Intraperitoneal Chemotherapy in Primary and Recurrent Ovarian Cancer: A Single-Center Cohort Study.
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Framarini, Massimo, D'Acapito, Fabrizio, Di Pietrantonio, Daniela, Tauceri, Francesca, Di Lorenzo, Paolo, Solaini, Leonardo, and Ercolani, Giorgio
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HYPERTHERMIC intraperitoneal chemotherapy ,OVARIAN cancer ,OVARIAN epithelial cancer ,SURVIVAL rate ,COHORT analysis ,PROGRESSION-free survival ,SALPINGECTOMY - Abstract
Epithelial ovarian cancer (EOC) is the most frequent cause of death among women with gynecologic malignant tumors. Primary debulking surgery (PDS) with maximal surgical effort to reach completeness of cytoreduction, followed by chemotherapy, has become the standard of care; moreover, some experiences have shown that a comprehensive treatment approach of surgery combined with hyperthermic intraperitoneal chemotherapy (HIPEC) could improve the prognosis of ovarian cancer. We carried out a retrospective analysis of all consecutive sixty-six patients diagnosed with primary advanced or recurrent ovarian cancer who underwent debulking surgery plus HIPEC in a single center between September 2005 and October 2020. For 33 patients with primary EOC, with a median follow-up period of 70 months, the median overall survival was 56 months (range: 48.1–96.9); and the median disease-free survival (DFS) was 13 months (range: 19.9–53.7). In the recurrent population, the median follow-up period was 78 months, the median overall survival (OS) was 82 months (range: 48.1–96.9), and the median DFS was 17 months (range: 19.7–53.0). In our study, we have found that CRS plus HIPEC is feasible, with very low rates of major complications and good results in terms of overall survival. [ABSTRACT FROM AUTHOR]
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- 2023
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7. Indocyanine Green versus Radiotracer with or without Blue Dye for Sentinel Lymph Node Mapping in Stage >IB1 Cervical Cancer (>2 cm)
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Di Martino, Giampaolo, Crivellaro, Cinzia, De Ponti, Elena, Bussi, Beatrice, Papadia, Andrea, Zapardiel, Ignacio, Vizza, Enrico, Elisei, Federica, Diestro, Maria Dolores, Locatelli, Luca, Gasparri, Maria Luisa, Di Lorenzo, Paolo, Mueller, Michael, and Buda, Alessandro
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- 2017
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8. A framework for parallel and distributed training of neural networks
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Scardapane, Simone and Di Lorenzo, Paolo
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- 2017
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9. From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
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Battiloro, Claudio, Spinelli, Indro, Telyatnikov, Lev, Bronstein, Michael, Scardapane, Simone, and Di Lorenzo, Paolo
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Computer Science - Neural and Evolutionary Computing ,Neural and Evolutionary Computing (cs.NE) ,Machine Learning (cs.LG) - Abstract
Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it. However, most of LGI methods assume to have a (noisy, incomplete, improvable, ...) input graph to rewire and can solely learn regular graph topologies. In the wake of the success of Topological Deep Learning (TDL), we study Latent Topology Inference (LTI) for learning higher-order cell complexes (with sparse and not regular topology) describing multi-way interactions between data points. To this aim, we introduce the Differentiable Cell Complex Module (DCM), a novel learnable function that computes cell probabilities in the complex to improve the downstream task. We show how to integrate DCM with cell complex message passing networks layers and train it in a end-to-end fashion, thanks to a two-step inference procedure that avoids an exhaustive search across all possible cells in the input, thus maintaining scalability. Our model is tested on several homophilic and heterophilic graph datasets and it is shown to outperform other state-of-the-art techniques, offering significant improvements especially in cases where an input graph is not provided., Under review. 17 pages, 5 figures
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- 2023
10. Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces
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Stylianopoulos, Kyriakos, Merluzzi, Mattia, Di Lorenzo, Paolo, and Alexandropoulos, George C.
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Emerging Technologies (cs.ET) ,Information Theory (cs.IT) ,Computer Science - Information Theory ,Computer Science - Emerging Technologies ,Machine Learning (cs.LG) - Abstract
In this paper, we propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge, in the context of 6G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new data are continuously generated/collected by a set of devices and are handled through a dynamic queueing system. Building on the marriage between Lyapunov stochastic optimization and deep reinforcement learning (DRL), we devise a dynamic learning algorithm that jointly optimizes the data compression scheme, the allocation of radio resources (i.e., power, transmission precoding), the computation resources (i.e., CPU cycles), and the RIS reflectivity parameters (i.e., phase shifts), with the aim of performing energy-efficient edge classification with end-to-end (E2E) delay and inference accuracy constraints. The proposed strategy enables dynamic control of the system and of the wireless propagation environment, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. Numerical results assess the performance of the proposed RIS-empowered edge inference strategy in terms of trade-off between energy, delay, and accuracy of a classification task.
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- 2023
11. Distributed semi-supervised support vector machines
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Scardapane, Simone, Fierimonte, Roberto, Di Lorenzo, Paolo, Panella, Massimo, and Uncini, Aurelio
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- 2016
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12. Tangent Bundle Filters and Neural Networks: from Manifolds to Cellular Sheaves and Back
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Battiloro, Claudio, Wang, Zhiyang, Riess, Hans, Di Lorenzo, Paolo, and Ribeiro, Alejandro
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Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical Engineering and Systems Science - Signal Processing ,Machine Learning (cs.LG) - Abstract
In this work we introduce a convolution operation over the tangent bundle of Riemannian manifolds exploiting the Connection Laplacian operator. We use the convolution 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.
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- 2022
13. Cell Attention Networks
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Giusti, Lorenzo, Battiloro, Claudio, Testa, Lucia, Di Lorenzo, Paolo, Sardellitti, Stefania, and Barbarossa, Sergio
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FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Computer Science - Artificial Intelligence ,Machine Learning (cs.LG) - Abstract
Since their introduction, graph attention networks achieved outstanding results in graph representation learning tasks. However, these networks consider only pairwise relationships among nodes and then they are not able to fully exploit higher-order interactions present in many real world data-sets. In this paper, we introduce Cell Attention Networks (CANs), a neural architecture operating on data defined over the vertices of a graph, representing the graph as the 1-skeleton of a cell complex introduced to capture higher order interactions. In particular, we exploit the lower and upper neighborhoods, as encoded in the cell complex, to design two independent masked self-attention mechanisms, thus generalizing the conventional graph attention strategy. The approach used in CANs is hierarchical and it incorporates the following steps: i) a lifting algorithm that learns {\it edge features} from {\it node features}; ii) a cell attention mechanism to find the optimal combination of edge features over both lower and upper neighbors; iii) a hierarchical {\it edge pooling} mechanism to extract a compact meaningful set of features. The experimental results show that CAN is a low complexity strategy that compares favorably with state of the art results on graph-based learning tasks., Preprint, under review
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- 2022
14. Multiscale Causal Structure Learning
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D'Acunto, Gabriele, Di Lorenzo, Paolo, and Barbarossa, Sergio
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Methodology (stat.ME) ,FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Statistics - Methodology ,Machine Learning (cs.LG) - Abstract
The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causal Structure Learning (MS-CASTLE), to estimate the structure of linear causal relationships occurring at different time scales. Differently from existing approaches, MS-CASTLE takes explicitly into account instantaneous and lagged inter-relations between multiple time series, represented at different scales, hinging on stationary wavelet transform and non-convex optimization. MS-CASTLE incorporates, as a special case, a single-scale version named SS-CASTLE, which compares favorably in terms of computational efficiency, performance and robustness with respect to the state of the art onto synthetic data. We used MS-CASTLE to study the multiscale causal structure of the risk of 15 global equity markets, during covid-19 pandemic, illustrating how MS-CASTLE can extract meaningful information thanks to its multiscale analysis, outperforming SS-CASTLE. We found that the most persistent and strongest interactions occur at mid-term time resolutions. Moreover, we identified the stock markets that drive the risk during the considered period: Brazil, Canada and Italy. The proposed approach can be exploited by financial investors who, depending to their investment horizon, can manage the risk within equity portfolios from a causal perspective.
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- 2022
15. Advanced low grade serous ovarian cancer: A retrospective analysis of surgical and chemotherapeutic management in two high volume oncological centers.
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Di Lorenzo, Paolo, Conteduca, Vincenza, Scarpi, Emanuela, Adorni, Marco, Multinu, Francesco, Garbi, Annalisa, Betella, Ilaria, Grassi, Tommaso, Bianchi, Tommaso, Di Martino, Giampaolo, Amadori, Andrea, Maniglio, Paolo, Strada, Isabella, Carinelli, Silvestro, Jaconi, Marta, Aletti, Giovanni, Zanagnolo, Vanna, Maggioni, Angelo, Savelli, Luca, and De Giorgi, Ugo
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OVARIAN cancer ,CYTOREDUCTIVE surgery ,CANCER chemotherapy ,ONCOLOGIC surgery ,SURVIVAL rate ,SALPINGECTOMY - Abstract
Background: Low-grade serous ovarian cancer (LGSOC) is a rare entity with different behavior compared to high-grade serous (HGSOC). Because of its general low chemosensitivity, complete cytoreductive surgery with no residual disease is crucial in advanced stage LGSOC. We evaluated the impact of optimal cytoreduction on survival outcome both at first diagnosis and at recurrence. Methods: We retrospectively studied consecutive patients diagnosed with advanced LGSOCs who underwent cytoreductive surgery in two oncological centers from January 1994 to December 2018. Survival curves were estimated by the Kaplan–Meier method, and 95% confidence intervals (95% CI) were estimated using the Greenwood formula. Results: A total of 92 patients were included (median age was 47 years, IQR 35– 64). The median overall survival (OS) was 142.3 months in patients with no residual disease (RD), 86.4 months for RD 1–10 mm and 35.2 months for RD >10 mm (p = 0.002). Progression-free survival (PFS) was inversely related to RD after primary cytoreductive surgery (RD = 0 vs RD = 1–10 mm vs RD >10 mm, p = 0.002). On multivariate analysis, RD 1–10 mm (HR = 2.30, 95% CI 1.30– 4.06, p = 0.004), RD >10 mm (HR = 3.89, 95% CI 1.92–7.88, p = 0.0004), FIGO stage IV (p = 0.001), and neoadjuvant chemotherapy (NACT) (p = 0.010) were independent predictors of PFS. RD >10 mm (HR = 3.13, 95% CI 1.52–6.46, p = 0.004), FIGO stage IV (p <0.0001) and NACT (p = 0.030) were significantly associated with a lower OS. Conclusions: Optimal cytoreductive surgery improves survival outcomes in advanced stage LGSOCs. When complete debulking is impossible, a RD <10 mm confers better OS compared to an RD >10 mm in this setting of patients. [ABSTRACT FROM AUTHOR]
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- 2022
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16. Wireless Environment as a Service Enabled by Reconfigurable Intelligent Surfaces: The RISE-6G Perspective
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Strinati, Emilio Calvanese Alexandropoulos, George C. and Sciancalepore, Vincenzo Di Renzo, Marco Wymeersch, Henk and Dinh-Thuy Phan-huy Crozzoli, Maurizio D'Errico, Raffaele De Carvalho, Elisabeth Popovski, Petar Di Lorenzo, Paolo and Bastianelli, Luca Belouar, Mathieu Mascolo, Julien Etienne and Gradoni, Gabriele Phang, Sendy Lerosey, Geoffroy Denis, Benoit
- Abstract
The design of 6th Generation (6G) wireless networks points towards flexible connect-and-compute technologies capable to support innovative services and use cases. Targeting the 2030 horizon, 6G networks are poised to pave the way for sustainable human-centered smart societies and vertical industries, such that wireless networks will be transformed into a distributed smart connectivity infrastructure, where new terminal types are embedded in the daily environment. In this context, the RISE-6G project aims at investigating innovative solutions that capitalize on the latest advances in the emerging technology of Reconfigurable Intelligent Surfaces (RISs), which offers dynamic and goal-oriented radio wave propagation control, enabling the concept of the wireless environment as a service. The project will focus on: i) the realistic modeling of RIS-assisted signal propagation, ii) the investigation of the fundamental limits of RIS-empowered wireless communications and sensing, and iii) the design of efficient algorithms for orchestrating networking RISs, in order to implement intelligent, sustainable, and dynamically programmable wireless environments enabling diverse services that go well beyond the SG capabilities. RISE-6G will offer two unprecedented proof-of-concepts for realizing controlled wireless environments in near-future use cases.
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- 2021
17. Observing bandlimited graph processes from subsampled measurements
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Isufi, E., Banelli, Paolo, Di Lorenzo, Paolo, Leus, G.J.T., and Matthews, Michael B.
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Bandlimiting ,0209 industrial biotechnology ,Theoretical computer science ,Computer science ,Linear system ,graph signal processing ,sampling ,observability ,020206 networking & telecommunications ,Observable ,02 engineering and technology ,Graph ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,Topological graph theory ,Observability - Abstract
This work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process bandwidth, and the sampling strategy. Moreover, conditions when the graph process is observable from a few samples and (sub)optimal sampling strategies that jointly exploit the nature of the graph structure and graph process are proposed. Finally, numerical tests are conducted to illustrate the benefits of the proposed approach.
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- 2019
18. Sentinel Lymph Node Mapping With Near-Infrared Fluorescent Imaging Using Indocyanine Green: A New Tool for Laparoscopic Platform in Patients With Endometrial and Cervical Cancer
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Buda, Alessandro, Bussi, Beatrice, Di Martino, Giampaolo, Di Lorenzo, Paolo, Palazzi, Sharon, Grassi, Tommaso, and Milani, Rodolfo
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- 2016
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19. Contributors
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Aviyente, Selin, Banelli, Paolo, Barbarossa, Sergio, Borgnat, Pierre, Ceci, Elena, Chang, Shih-Fu, Cheng, Chao, Chen, Jie, Chepuri, Sundeep Prabhakar, Corinzia, Luca, Dedecius, Kamil, Djurić, Petar M., Eksin, Ceyhun, Garnaev, Andrey, Gesbert, David, Giannakis, Georgios B., Gonçalves, Paulo, Hero, Alfred, Hlawatsch, Franz, Hoiles, William, Inaltekin, Hazer, Jun, Kwang-Sung, Kailkhura, Bhavya, Kalogeratos, Argyris, Kanna, Sithan, Kar, Soummya, de Kerret, Paul, Krishnamurthy, Vikram, Leshem, Amir, Leus, Geert, Li, Zhenguo, Liu, Sijia, Liu, Ying, Di Lorenzo, Paolo, Mandic, Danilo P., Mardani, Morteza, Marques, Antonio G., Mateos, Gonzalo, Matta, Vincenzo, Merluzzi, Mattia, Moura, José M.F., Moulines, Eric, Nowak, Robert, Oselio, Brandon, Plataniotis, Konstantinos, Poor, H. Vincent, Rabbat, Michael G., Ribeiro, Alejandro, Richard, Cédric, Sardellitti, Stefania, Sayed, Ali H., Scaglione, Anna, Scaman, Kevin, van der Schaar, Mihaela, Segarra, Santiago, Spachos, Petros, Sprintson, Alex, Swenson, Brian, Tekin, Cem, Ting, Shang Kee, Trappe, Wade, Tremblay, Nicolas, Varshney, Pramod K., Vayatis, Nicolas, Vempaty, Aditya, Villafañe-Delgado, Marisel, Wai, Hoi-To, Wang, Daifeng, Wu, Xiao-Ming, Xia, Yili, Xu, Jie, Zhang, Simpson, and Zhao, Xiaochuan
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- 2018
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20. TROPIC-D21 Scenario and Requirements
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Becvar, Zdenek, Vondra, Michal, Dolezal, Jakub, Mach, Pavel, Rohlik, Matej, Agustín de Dios, Adrián|||0000-0002-5723-9739, Lagén Morancho, Sandra, Muñoz Medina, Olga|||0000-0002-8739-7068, Pascual Iserte, Antonio|||0000-0001-5596-2029, Torrellas Socastro, Marc, Vidal Manzano, José|||0000-0002-1985-2065, Lobillo Vilela, Felicia, Juan, Ana, Puente, Miguel Angel, Simamora, Lamhot, Anugrahani, Melani, Kelana, Eka, Hariyanto, Hari, Ari Wibowo, Franciscus X., Ludfy, Ahmad, Budiono, Karno, Nainggolan, Lesmin, Chandra, Lunel, Goldhammer, Mariana, de Marinis, Enrico, Gambetti, Fabrizio, Lo Presti, D., Mei, Alessandro, Stefa, Julinda, Vivier, Guillaume, Sezginer, Serdar, Sarkiss, Mireille, Barbarossa, Sergio, di Lorenzo, Paolo, Sardellitti, Stefania, Baiocchi, Andrea, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, and Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
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Femto-cloud ,SCM ,LTE ,Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors [Àrees temàtiques de la UPC] ,Computació en núvol ,Informàtica [Àrees temàtiques de la UPC] ,Architecture ,Cloud computing ,Small cell ,Xarxes locals sense fil Wi-Fi ,Femtocells - Abstract
This deliverable defines general framework for consequent technical workpackages in TROPIC. First, scenarios from the business point of view are defined and further transformed to more technical representation. Then, abstraction models are introduced for both communication and computation parts of femto-cloud concept. Furthermore, requirements need to be met to ensure efficient and feasible utilization of the femto-cloud are presented. To enable prove of the femto-cloud concept and to compare the developed techniques and algorithms a set of performance indicators to be considered in all technical workpackages are listed and defined. To that end, the models and simulation settings are defied in the last section of the document
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- 2013
21. Joint Optimization of Radio Resources and Code Partitioning in Mobile Edge Computing
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Di Lorenzo, Paolo, Barbarossa, Sergio, and Sardellitti, Stefania
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Networking and Internet Architecture (cs.NI) ,FOS: Computer and information sciences ,Computer Science - Networking and Internet Architecture - Abstract
The aim of this paper is to propose a computation offloading strategy for mobile edge computing. We exploit the concept of call graph, which models a generic computer program as a set of procedures related to each other through a weighted directed graph. Our goal is to derive the optimal partition of the call graph establishing which procedures are to be executed locally or remotely. The main novelty of our work is that the optimal partition is obtained jointly with the selection of radio parameters, e.g., transmit power and constellation size, in order to minimize the energy consumption at the mobile handset, under a latency constraint taking into account transmit time and execution time. We consider both single and multi-channel transmission strategies and we prove that a globally optimal solution can be achieved in both cases. Finally, we propose a suboptimal strategy aimed at solving a relaxed version of the original problem in order to tradeoff complexity and performance of the proposed framework. Finally, several numerical results illustrate under what conditions in terms of call graph topology, communication strategy, and computation parameters, the proposed offloading strategy provides large performance gains., Submitted to IEEE Transactions on Signal Processing
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- 2013
22. Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies.
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Di Lorenzo, Paolo, Banelli, Paolo, Isufi, Elvin, Barbarossa, Sergio, and Leus, Geert
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ADAPTIVE signal processing , *GRAPH theory , *SAMPLING methods , *COMPUTER algorithms , *TIME-varying systems , *STOCHASTIC convergence - Abstract
The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed to design the sampling probability at each node in the graph, with the aim of optimizing the tradeoff between steady-state performance, graph sampling rate, and convergence rate of the adaptive algorithms. Finally, a distributed RLS strategy is derived and shown to be convergent to its centralized counterpart. Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and recovery strategies for (distributed) adaptive learning of signals defined over graphs. [ABSTRACT FROM AUTHOR]
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- 2018
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23. Stochastic Training of Neural Networks via Successive Convex Approximations.
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Scardapane, Simone and Di Lorenzo, Paolo
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ARTIFICIAL neural networks , *NONCONVEX programming , *STOCHASTIC convergence - Abstract
This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of nonconvex optimization, going under the general name of successive convex approximation techniques. The basic idea is to iteratively replace the original (nonconvex, highly dimensional) learning problem with a sequence of (strongly convex) approximations, which are both accurate and simple to optimize. Different from similar ideas (e.g., quasi-Newton algorithms), the approximations can be constructed using only first-order information of the NN function, in a stochastic fashion, while exploiting the overall structure of the learning problem for a faster convergence. We discuss several use cases, based on different choices for the loss function (e.g., squared loss and cross-entropy loss), and for the regularization of the NN’s weights. We experiment on several medium-sized benchmark problems and on a large-scale data set involving simulated physical data. The results show how the algorithm outperforms the state-of-the-art techniques, providing faster convergence to a better minimum. Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance. In particular, each computational unit can optimize a tailored surrogate function defined on a randomly assigned subset of the input variables, whose dimension can be selected depending entirely on the available computational power. [ABSTRACT FROM AUTHOR]
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- 2018
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24. Bio-Inspired Dynamic Radio Access in Cognitive Networks based on Social Foraging Swarms
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DI LORENZO, Paolo and Barbarossa, Sergio
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Bio-inspired networking ,Spectrum sensing ,Ingegneria industriale e dell'informazione::TELECOMUNICAZIONI [Settori Disciplinari MIUR] ,Distributed Optimization ,Cognitive radio ,Bio-inspired networking, Cognitive radio, Distributed Optimization, Spectrum sensing ,Settori Disciplinari MIUR::Ingegneria industriale e dell'informazione::TELECOMUNICAZIONI - Abstract
There is strong trend, in current research on communication and sensor networks, to study selforganizing, self-healing systems. This poses great challenges to the research on decentralized systems, but at the same offers great potentials for future developments, especially in view of the current trend towards miniaturized systems. Even if the development of self-organizing systems is probably at the beginning, biological systems offers many examples of self-organization and selfhealing. This is as testified, for example, by swarming behaviors, brain activity, and so on. It is then of great interest to derive mathematical models of biological systems and see how they can suggest novel design tools for engineers. Signal Processing can play a big role in this cross-fertilization, as it can help to find out manageable mathematical problems, study their behavior and test the performance in the presence of disturbances. The challenge is to establish a cross-fertilization of ideas from biological to artificial systems, as well as to help understanding biological systems as such. This dissertation considers the problem of dynamic radio access based on sensing in cognitive radio networks. In particular, we follow a rather alternative path with respect to more conventional approaches and, inspired by biological models, we formulate the search for radio resources, i.e. time and frequency slots, as the search for food by a flock of birds swarming in a cooperative manner, but without any centralized control. The interference distribution in the time-frequency plane takes the role of the food spatial distribution: The birds (radio nodes) fly (allocate their resources) over the regions (time-frequency domain) where there is more food (less interference). During the flight, the birds move (choose their time-frequency slots) in a coordinated way, even in the absence of any central control, in order to avoid collisions (conflicts over common radio resources), yet maintaining the swarm cohesion (i.e., avoiding unnecessary spread in the occupancy of the time-frequency plane). This procedure is applied to the dynamic resource allocation in the frequency domain and in the time-frequency domain, where the primary users in a cognitive radio system are modeled as statistically independent homogeneous continuous-time Markov processes. A rigorous mathematical analysis of the proposed algorithm is also derived. First, we study the stability and the cohesiveness of the swarm in case of local interactions among the nodes, providing closed form expressions for the upper and lower bounds of the swarm size as a function of the network connectivity. Then, using stochastic approximation arguments, we derive the convergence properties of the swarming algorithm in the presence of random disturbances introduced by realistic channels, i.e., link failures, quantization, noise and estimation errors. Spectrum sensing is a critical prerequisite in envisioned applications of wireless cognitive radio networks which promise to resolve the perceived bandwidth scarcity versus under-utilization dilemma. Creating an interference map of the operational region plays an instrumental role in enabling spatial frequency reuse and allowing for dynamic spectrum allocation in a hierarchical access model comprising primary and secondary users. For such purpose, a distributed technique for cooperative spectrum estimation in cognitive radio systems is proposed based on a basis expansion model of the power spectral density map in frequency. The proposed method, based on diffusion adaptation algorithms, estimates and learns the interference profile through local cooperation and without the need for a central processor. Convergence and mean square analysis of the diffusion filter applied to the distributed cooperative sensing problem is also derived. Finally, it is proposed a dynamic resource allocation technique combining a distributed diffusion algorithm, for implementing cooperative sensing, with a swarming technique, for allocating resources in a parsimonious way (i.e., avoiding unnecessary spread in the frequency domain), yet avoiding collisions. In particular, the procedure is applied to the dynamic resource allocation problem in the frequency domain. Numerical results show the improvement that results in the resource allocation performance due to the cooperative estimation of the spectrum. Furthermore, it is shown how the proposed technique endows the resulting bio-inspired network with powerful learning and adaptation capabilities.
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- 2012
25. FREEDOM Project. Interference coordination protocols in femto-based networks
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Rosi, Guido, Pucci, Fabrizio, Imponente, Giovanni, de Marinis, Enrico, Baiocchi, Andrea, Barbarossa, Sergio, di Lorenzo, Paolo, Agustín de Dios, Adrián, Muñoz Medina, Olga, Vidal Manzano, José, Goldhamer, M., Rodríguez Fonollosa, Javier, Pastore, A., Sardellitti, Stefania, Pescosolido, Loreto, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, and Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
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Jocs cooperatius (Matemàtiques) ,Sistemes MIMO ,Genetic optimization ,Enginyeria de la telecomunicació::Processament del senyal [Àrees temàtiques de la UPC] ,Cooperative games (Mathematics) ,MIMO systems - Abstract
This document looks into coordinated radio resource allocation algorithms in deployments of huge number of femto-access points (FAP) by exploiting the message exchange at control-plane level through the wired ISP backhaul link. The algorithms present in this document are based on decentralized cooperative strategies supported by Game Theory. Decentralized resource allocation algorithms based on price exchange have been derived under different criteria: guaranteeing a minimum rate with the minimum power, maximizing the weighted sum-rate of the system, maximizing the opportunistic throughput when there are different source of randomness like random link failures and noise quantization or when the activity of macro-users is not known by FAPs (coordinated channel sensing or modelling the activity). Additionally, a genetic-based resource allocation is investigated, providing centralized and decentralized algorithms, to which the Game Theory solutions are challenged. In all cases, the techniques derived require modifications of the current LTE-A standard which are identified. Finally, some of those algorithms have been evaluated in a Realistic corporate scenario elucidating the advantages of coordinated resource allocation techniques proposed.
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- 2011
26. Distributed nonconvex optimization over time-varying networks.
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Di Lorenzo, Paolo and Scutari, Gesualdo
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- 2016
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27. LMS estimation of signals defined over graphs.
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Di Lorenzo, Paolo, Barbarossa, Sergio, Banelli, Paolo, and Sardellitti, Stefania
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- 2016
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28. Distributed Adaptive Learning of Graph Signals.
- Author
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Di Lorenzo, Paolo, Banelli, Paolo, Barbarossa, Sergio, and Sardellitti, Stefania
- Subjects
- *
INSTRUCTIONAL systems , *SIGNAL processing , *SAMPLING theorem , *DISTRIBUTED decision making , *FOURIER transforms - Abstract
The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean-square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
29. Synthesis of poly(aryleneethynylene)s bearing glucose units as substituents
- Author
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Babudri, Francesco, Colangiuli, Donato, Di Lorenzo, Paolo A., Farinola, Gianluca Maria, Omar, Hassan Omar, and Naso, Francesco
- Subjects
carbohydrates (lipids) - Abstract
A series of poly(aryleneethynylene)s functionalized with acetylated glucopyranosyl units were synthesized by the Pd-catalyzed reaction of trimethylsilylethynyl derivatives with aromatic halides in the presence of silver oxide.
- Published
- 2003
30. Uncertainty principle and sampling of signals defined on graphs.
- Author
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Tsitsvero, Mikhail, Barbarossa, Sergio, and Di Lorenzo, Paolo
- Published
- 2015
- Full Text
- View/download PDF
31. Distributed nonconvex optimization over networks.
- Author
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Di Lorenzo, Paolo and Scutari, Gesualdo
- Published
- 2015
- Full Text
- View/download PDF
32. Computation offloading strategies based on energy minimization under computational rate constraints.
- Author
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Barbarossa, Sergio, Di Lorenzo, Paolo, and Sardellitti, Stefania
- Published
- 2014
- Full Text
- View/download PDF
33. Distributed least mean squares strategies for sparsity-aware estimation over Gaussian Markov random fields.
- Author
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Di Lorenzo, Paolo and Barbarossa, Sergio
- Published
- 2014
- Full Text
- View/download PDF
34. Diffusion Adaptation Strategies for Distributed Estimation Over Gaussian Markov Random Fields.
- Author
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Di Lorenzo, Paolo
- Subjects
- *
GAUSSIAN Markov random fields , *SOLID solutions , *MOLECULAR vibration , *PROPERTIES of matter , *SIGNAL processing - Abstract
The aim of this paper is to propose diffusion strategies for distributed estimation over adaptive networks, assuming the presence of spatially correlated measurements distributed according to a Gaussian Markov random field (GMRF) model. The proposed methods incorporate prior information about the statistical dependency among observations, while at the same time processing data in real time and in a fully decentralized manner. A detailed mean-square analysis is carried out in order to prove stability and evaluate the steady-state performance of the proposed strategies. Finally, we also illustrate how the proposed techniques can be easily extended in order to incorporate thresholding operators for sparsity recovery applications. Numerical results show the potential advantages of using such techniques for distributed learning in adaptive networks deployed over GMRF. [ABSTRACT FROM PUBLISHER]
- Published
- 2014
- Full Text
- View/download PDF
35. Communicating While Computing: Distributed mobile cloud computing over 5G heterogeneous networks.
- Author
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Barbarossa, Sergio, Sardellitti, Stefania, and Di Lorenzo, Paolo
- Abstract
Current estimates of mobile data traffic in the years to come foresee a 1,000 increase of mobile data traffic in 2020 with respect to 2010, or, equivalently, a doubling of mobile data traffic every year. This unprecedented growth demands a significant increase of wireless network capacity. Even if the current evolution of fourth-generation (4G) systems and, in particular, the advancements of the long-term evolution (LTE) standardization process foresees a significant capacity improvement with respect to third-generation (3G) systems, the European Telecommunications Standards Institute (ETSI) has established a roadmap toward the fifth-generation (5G) system, with the aim of deploying a commercial system by the year 2020 [1]. The European Project named ?Mobile and Wireless Communications Enablers for the 2020 Information Society? (METIS), launched in 2012, represents one of the first international and large-scale research projects on fifth generation (5G) [2]. In parallel with this unparalleled growth of data traffic, our everyday life experience shows an increasing habit to run a plethora of applications specifically devised for mobile devices, (smartphones, tablets, laptops)for entertainment, health care, business, social networking, traveling, news, etc. However, the spectacular growth in wireless traffic generated by this lifestyle is not matched with a parallel improvement on mobile handsets? batteries, whose lifetime is not improving at the same pace [3]. This determines a widening gap between the energy required to run sophisticated applications and the energy available on the mobile handset. A possible way to overcome this obstacle is to enable the mobile devices, whenever possible and convenient, to offload their most energy-consuming tasks to nearby fixed servers. This strategy has been studied for a long time and is reported in the literature under different names, such as cyberforaging [4] or computation offloading [5], [6]. In recent years, a strong impulse to computation offloading has come through cloud computing (CC), which enables the users to utilize resources on demand. The resources made available by a cloud service provider are: 1) infrastructures, such as network devices, storage, servers, etc., 2) platforms, such as operating systems, offering an integrated environment for developing and testing custom applications, and 3) software, in the form of application programs. These three kinds of services are labeled, respectively, as infrastructure as a service, platform as a service, and software as a service. In particular, one of the key features of CC is virtualization, which makes it possible to run multiple operating systems and multiple applications over the same machine (or set of machines), while guaranteeing isolation and protection of the programs and their data. Through virtualization, the number of virtual machines (VMs) can scale on ?demand, thus improving the overall system computational efficiency. Mobile CC (MCC) is a specific case of CC where the user accesses the cloud services through a mobile handset [5]. The major limitations of today?s MCC are the energy consumption associated to the radio access and the latency experienced in reaching the cloud provider through a wide area network (WAN). Mobile users located at the edge of macrocellular networks are particularly disadvantaged in terms of power consumption and, furthermore, it is very difficult to control latency over a WAN. As pointed out in [7]?[9], humans are acutely sensitive to delay and jitter: as latency increases, interactive response suffers. Since the interaction times foreseen in 5G systems, in particular in the so-called tactile Internet [10], are quite small (in the order of milliseconds), a strict latency control must be somehow incorporated in near future MCC. Meeting this constraint requires a deep ?rethinking of the overall service chain, from the physical layer up to virtualization. [ABSTRACT FROM PUBLISHER]
- Published
- 2014
- Full Text
- View/download PDF
36. Computation offloading for mobile cloud computing based on wide cross-layer optimization.
- Author
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Barbarossa, Sergio, Sardellitti, Stefania, and Di Lorenzo, Paolo
- Published
- 2013
37. Joint allocation of computation and communication resources in multiuser mobile cloud computing.
- Author
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Barbarossa, Sergio, Sardellitti, Stefania, and Di Lorenzo, Paolo
- Published
- 2013
- Full Text
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38. Distributed control of wireless ad-hoc networks connectivity incorporating realistic channel models.
- Author
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Di Lorenzo, Paolo and Barbarossa, Sergio
- Published
- 2013
39. Decentralized estimation and control of algebraic connectivity of random ad-hoc networks.
- Author
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Di Lorenzo, Paolo and Barbarossa, Sergio
- Published
- 2013
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- View/download PDF
40. Sparse diffusion LMS for distributed adaptive estimation.
- Author
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Di Lorenzo, Paolo, Barbarossa, Sergio, and Sayed, Ali H.
- Abstract
The goal of this paper is to propose diffusion LMS techniques for distributed estimation over adaptive networks, which are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to improve the performance of the diffusion strategies. We provide convergence and performance analysis of the proposed method, showing under what conditions it outperforms the unregularized diffusion version. Simulation results illustrate the advantage of the proposed filter under the sparsity assumption on the true coefficient vector. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
41. Bio-inspired swarming for dynamic radio access based on diffusion adaptation.
- Author
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Di Lorenzo, Paolo, Barbarossa, Sergio, and Sayed, Ali H.
- Published
- 2011
42. Distributed stochastic pricing for sum-rate maximization in femtocell networks with random graph and quantized communications.
- Author
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Di Lorenzo, Paolo, Omilipo, Marco, and Barbarossa, Sergio
- Published
- 2011
- Full Text
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43. Optimal beamforming for range-doppler ambiguity suppression in squinted SAR systems.
- Author
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Di Lorenzo, Paolo and Barbarossa, Sergio
- Published
- 2011
- Full Text
- View/download PDF
44. Insights on Tax Evasion Using a Monetary Circuit Model.
- Author
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Di Lorenzo, Paolo
- Subjects
TAX evasion ,MONETARY policy ,BUDGET ,GOVERNMENT policy ,FISCAL policy ,TAX rates - Abstract
The paper analyses the impact of tax evasion on government budgets, using a monetary circuit model. It is possible to show that, under specific parameter values, evading taxes on profit becomes an attractive proposition when government policies tax capital and employment in different ways and on different timescales. Without adequate tax enforcement, government is reduced to acting as a financial backer for companies. Furthermore, a lower tax rate on labour income enabling money to flow from households to companies through private consumption reduces incentives for tax evasion. These results hold true even if the government decides to implement a balanced budget fiscal policy. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
45. KEPLER PHOTOMETRY OF FOUR RADIO-LOUD ACTIVE GALACTIC NUCLEI IN 2010–2012.
- Author
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WEHRLE, ANN E., WIITA, PAUL J., UNWIN, STEPHEN C., DI LORENZO, PAOLO, REVALSKI, MITCHELL, SILANO, DANIEL, and SPRAGUE, DAN
- Subjects
PHOTOMETRY ,KEPLER'S equation ,ACTIVE galactic nuclei ,QUASARS ,SEYFERT galaxies ,ACCRETION disks ,BLACK holes - Abstract
We have used Kepler photometry to characterize variability in four radio-loud active galactic nuclei (AGN; three quasars and one object tentatively identified as a Seyfert 1.5 galaxy) on timescales from minutes to months, comparable to the light crossing time of the accretion disk around the central supermassive black hole or the base of the relativistic jet. Kepler’s almost continuous observations provide much better temporal coverage than is possible from ground-based observations. We report the first such data analyzed for quasars. We have constructed power spectral densities using eight Kepler quarters of long-cadence (30-minute) data for three AGN, six quarters for one AGN and two quarters of short-cadence (1-minute) data for all four AGN. On timescales longer than about 0.2–0.6 days, we find red noise with mean power-law slopes ranging from −1.8 to −1.2, consistent with the variability originating in turbulence either behind a shock or within an accretion disk. Each AGN has a range of red noise slopes which vary slightly by month and quarter of observation. No quasi-periodic oscillations of astrophysical origin were detected.We detected flares of several days long when brightness increased by 3%–7% in two objects. No flares on timescales of minutes to hours were detected. Our observations imply that the duty cycle for enhanced activity in these radio-loud AGN is small. These well-sampled AGN light curves provide an impetus to develop more detailed models of turbulence in jets and instabilities in accretion disks. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
46. Decentralized Resource Assignment in Cognitive Networks Based on Swarming Mechanisms Over Random Graphs.
- Author
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Di Lorenzo, Paolo, Barbarossa, Sergio, and Sayed, Ali H.
- Subjects
- *
STOCHASTIC approximation , *CONVERGENCE (Telecommunication) , *RESOURCE allocation , *RADIO access networks , *FEMTOCELLS - Abstract
This paper proposes a distributed resource assignment strategy for cognitive networks mimicking a swarm foraging mechanism, assuming that the communication among the cognitive nodes is impaired by random link failures and quantization noise. Using results from stochastic approximation theory, we propose a swarm mechanism that converges almost surely to a final allocation even in the presence of imperfect communication scenarios. The theoretical findings are corroborated by numerical results showing that the only effect of the random link failures is to decrease the convergence rate of the algorithm. We propose then a fast swarming approach, robust to random disturbances, that adapts its behavior with respect to the interference power perceived by every node, thus increasing the speed of convergence and improving the resource allocation capabilities. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
47. A Bio-Inspired Swarming Algorithm for Decentralized Access in Cognitive Radio.
- Author
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Di Lorenzo, Paolo and Barbarossa, Sergio
- Subjects
- *
BIOMIMICRY , *RADIO networks , *RADIO interference , *RADIO resource management , *ALGORITHMS , *MARKOV processes , *MATHEMATICAL models , *BIRD behavior - Abstract
The goal of this paper is to propose a bio-inspired radio access mechanism for cognitive networks mimicking the behavior of a flock of birds swarming in search for food in a cohesive fashion without colliding with each other. The equivalence between swarming and radio resource allocation is established by modeling the interference distribution in the resource domain, e.g., frequency and time, as the spatial distribution of food, while the position of the single bird represents the radio resource chosen by each radio node. The swarming mechanism is enforced by letting every node allocate its resources (power/bits) in the time-frequency regions where the interference is minimum (the food density is maximum), avoiding collisions with other nodes (birds), yet limiting the spread in the time-frequency domain (i.e., maintaining the swarm cohesion). The solution is given as the distributed minimization of a functional, borrowed from social foraging swarming models, containing the average interference plus repulsion and attraction terms that help to avoid conflicts and maintain cohesiveness, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
48. ACCESS.
- Author
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Russell, Jim, Aspot, Jenny, Zrakic', Marko, Howie, John, Hamley, Andrew, Clarson, John, Boniface, Pete, Di Lorenzo, Paolo, Evans, David, Bollmann, Martin, Blunt, Geoff, Aaron, Jessica, and Lazarus, Jason
- Subjects
MOTORCYCLE racing ,DUCATI motorcycle ,SUPERBIKES ,PERIODICALS - Abstract
Several letters to the editor are presented in response to articles published in previous issues including an article on two 250cc Ducati Desmo twins, "Anyone who thought Marquez was a flash in the pan probably feels a bit silly right now" and an article on the new Daytona 675 bike.
- Published
- 2013
49. A Novel GC-FID Analytical Strategy Reveals Adulteration of Fuels by Vegetable Oil.
- Author
-
Francescangeli, Adriano, Daniele, Vito, Di Lorenzo, Paolo Antonio, and Franco, Magda
- Abstract
Background: Adulteration of diesel fuel by the addition of vegetable oil is a problem that touches several countries around the world and bypasses the complexity of the specifications regarding the automotive diesel fuel distinguishing between customs, fiscal, and commercial–environmental specifications. At an international level, the adoption of the same analysis methods is important for the harmonization processes and the fluidity of the market. In this context, we assist to the diffusion of the same fraud touching several countries or continents since the limit of the same methods are common to many specifications. For several European countries, the revenue lost as a result of this adulteration consists of billions of euros per year. This enormous amount of illicit money feeds organized criminal networks with huge social and environmental damages. Objective: This work presents a GC method to quantify vegetable oils in the range of 0.2–7% (w/w) in adulterated diesel fuel, intended for use as motor fuel, with or without extraneous heavy mineral oil. Methods: Our study was realized on 100 fraudulent samples collected by the Italian fiscal police in regular oil stations and by the Italian fiscal police and customs officers as a consequence of controls on trucks transporting, in suspicious circumstances, “oil” often declared “antistick and anticorrosion oil” or “protective oil.” Conclusions and Highlights: High reliability of results, external validity, ease of replication, simple instrumentation, and sample preparation make this method well suited for a new “smart” protocol for diesel fuels analysis for customs, fiscal, and antifraud purposes. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
50. Synthesis of poly(aryleneethynylene)s bearing glucose units as substituents.
- Author
-
Babudri F, Colangiuli D, Di Lorenzo PA, Farinola GM, Omar OH, and Naso F
- Abstract
A series of poly(aryleneethynylene)s functionalized with acetylated glucopyranosyl units were synthesized by the Pd-catalyzed reaction of trimethylsilylethynyl derivatives with aromatic halides in the presence of silver oxide.
- Published
- 2003
- Full Text
- View/download PDF
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