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MRI-TRUS Image Synthesis with Application to Image-Guided Prostate Intervention
No full textAccurate and robust fusion of pre-procedure magnetic resonance imaging (MRI) to intra-procedure trans-rectal ultrasound (TRUS) imaging is necessary for image-guided prostate cancer biopsy procedures. The current clinical standard for image fusion relies on non-rigid surface-based registration between semi-automatically segmented prostate surfaces in both the MRI and TRUS. This surface-based registration method does not take advantage of internal anatomical prostate structures, which have the potential to provide useful information for image registration. However, non-rigid, multi-modal intensity-based MRI-TRUS registration is challenging due to highly non-linear intensities relationships between MRI and TRUS. In this paper, we present preliminary work using image synthesis to cast this problem into a mono-modal registration task by using a large database of over 100 clinical MRI-TRUS image pairs to learn a joint model of MR-TRUS appearance. Thus, given an MRI, we use this learned joint appearance model to synthesize the patient’s corresponding TRUS image appearance with which we could potentially perform mono-modal intensity-based registration. We present preliminary results of this approach
Dynamic SLAs for Clouds
No full textIn the Cloud domain, to guarantee adaptation to the needs of users and providers, Service-Level-Agreements (SLAs) would benefit from mechanisms to capture the dynamism of services. The existing SLA languages attempt to address this challenge by focusing on renegotiation of the agreement terms, which is a heavy-weight process, not really suitable for dealing with cloud dynamism. In this paper, we propose an extension of SLAC, a SLA language for clouds that we have recently defined, with a mechanism that enable dynamic modifications of the service agreement. We formally describe this extension, implement it in the SLAC framework and analyse the impacts of dynamic SLAs in some applications. The advantages of dynamic SLAs are demonstrated by comparing their effect with that of static SLA and of the “renegotiation” approach
Machine Learning for Plant Phenotyping Needs Image Processing
No full textWe found the article by Singh et al. [1] extremely interesting because it introduces and showcases the utility of machine learning for high-throughput data-driven plant phenotyping. With this letter we aim to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what is suggested in [1], both in analyzing phenotyping data (e.g., to measure growth) and when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2]
A geometrical multi-scale numerical method for coupled hygro-thermo-mechanical problems in photovoltaic laminates
No full textA comprehensive computational framework based on the finite element method for the simulation of coupled hygro-thermo-mechanical problems in photovoltaic laminates is herein proposed. While the thermo-mechanical problem takes place in the three-dimensional space of the laminate, moisture diffusion occurs in a two-dimensional domain represented by the polymeric layers and by the vertical channel cracks in the solar cells. Therefore, a geometrical multi-scale solution strategy is pursued by solving the partial differential equations governing heat transfer and thermo-elasticity in the three-dimensional space, and the partial differential equation for moisture diffusion in the two dimensional domains. By exploiting a staggered scheme, the thermo-mechanical problem is solved first via a fully implicit solution scheme in space and time, with a specific treatment of the polymeric layers as zero-thickness interfaces whose constitutive response is governed by a novel thermo-visco-elastic cohesive zone model based on fractional calculus. Temperature and relative displacements along the domains where moisture diffusion takes place are then projected to the finite element model of diffusion, coupled with the thermo-mechanical problem by the temperature and crack opening dependent diffusion coefficient. The application of the proposed method to photovoltaic modules pinpoints two important physical aspects: (i) moisture diffusion in humidity freeze tests with a temperature dependent diffusivity is a much slower process than in the case of a constant diffusion coefficient; (ii) channel cracks through Silicon solar cells significantly enhance moisture diffusion and electric degradation, as confirmed by experimental tests
Optimal design of low-frequency band gaps in anti-tetrachiral lattice meta-materials
No full textThe elastic wave propagation is investigated in the beam lattice material characterized by a square periodic cell with anti-tetrachiral microstructure. With reference to the Floquet-Bloch spectrum, focus is made on the band structure enrichments and modifications which can be achieved by equipping the cellular microstructure with tunable local resonators. By virtue of its composite mechanical nature, the so-built inertial meta-material gains enhanced capacities of passive frequency-band filtering. Indeed the number, placement and properties of the inertial resonators can be designed to open, shift and enlarge the band gaps between one or more pairs of consecutive branches in the frequency spectrum. In order to improve the meta-material performance, a nonlinear optimization problem is formulated. The maximum of the largest band gap amplitudes in the low-frequency range is selected as suited objective function. Proper inequality constraints are introduced to restrict the optimal solutions within a compact set of mechanical and geometric parameters, including only physically realistic properties of both the lattice and resonators. The optimization problems related to full and partial band gaps are solved independently, by means of a globally convergent version of the numerical method of moving asymptotes, combined with a quasi-Monte Carlo multi-start technique. The optimal solutions are discussed and compared from the qualitative and quantitative viewpoints, bringing to light the limits and potential of the meta-material performance. The clearest trends emerging from the numerical analyses are pointed out and interpreted from the physical viewpoint. Finally, some specific recommendations about the microstructural design of the meta-material are synthesized
Statistically validated network of portfolio overlaps and systemic risk
No full textCommon asset holding by financial institutions (portfolio overlap) is nowadays regarded as an important channel for financial contagion with the potential to trigger fire sales and severe losses at the systemic level. We propose a method to assess the statistical significance of the overlap between heterogeneously diversified portfolios, which we use to build a validated network of financial institutions where links indicate potential contagion channels. The method is implemented on a historical database of institutional holdings ranging from 1999 to the end of 2013, but can be applied to any bipartite network. We find that the proportion of validated links (i.e. of significant overlaps) increased steadily before the 2007–2008 financial crisis and reached a maximum when the crisis occurred. We argue that the nature of this measure implies that systemic risk from fire sales liquidation was maximal at that time. After a sharp drop in 2008, systemic risk resumed its growth in 2009, with a notable acceleration in 2013. We finally show that market trends tend to be amplified in the portfolios identified by the algorithm, such that it is possible to have an informative signal about institutions that are about to suffer (enjoy) the most significant losses (gains)
Neuroscience Evidence for Economic Humanism in Management Science: Organizational Implications and Strategy
No full textGlobalization phenomena and Information & Communication Technology (ICT) are producing deep changes worldwide. The economic environment and society where firms both cooperate and compete with each other are rapidly changing leading firms towards recognizing the role of intangible resources as a source of fresh competitive advantage. Experience, innovation and the ability to create new knowledge completely arise from the act of human resources inviting firms to focus on how to generate and shape knowledge. Therefore, the future of firms depends greatly on how managers are able to explore and exploit human resources. However, without a clear understanding of the nature of human beings and the complexity behind human interactions, we cannot understand the theory of organizational knowledge creation. Thus, how can firms discover, man- age and valorize this “human advantage”? Neuroscience can increase the understanding of how cognitive and emotional processes work; in doing so, we may be able to better understand how individuals involved in a business organization make decisions and how external factors influence their behavior, especially in terms of commitment activation and engagement level. In this respect, a neuroscientific approach to business can support managers in decision-making processes. In a scenario where economic humanism plays a central role in the process of fostering firms’ competitiveness and emerging strategies, we believe that a neuroscience approach in a business organization could be a valid source of value and inspiration for manager decision-making processes
Modularities maximization in multiplex network analysis using Many-Objective Optimization
No full textNowadays, social network analysis receives big attention from academia, industries and governments. Some practical applications such as community detection and centrality in economic networks have become main issues in this research area. Community detection algorithm for complex network analysis is mainly accomplished by the Louvain Method that seeks to find communities by heuristically finding a partitioning with maximal modularity. Traditionally, community detection applied for a network that has homogeneous semantics, for instance indicating friend relationship between people or import-export relationships between countries etc. However we increasingly deal with more complex network and also with so-called multiplex networks. In a multiplex network the set of nodes stays the same, while there are multiple sets of edges. In the analysis we would like to identify communities, but different edge sets give rise to different modularity optimizing partitions into communities. We propose to view community detection of such multilayer networks as a many-objective optimization problem. For this apply Evolutionary Many Objective Optimization and compute the Pareto fronts between different modularity layers. Then we group the objective functions into community in order to better understand the relationship and dependence between different layers (conflict, indifference, complementarily). As a case study, we compute the Pareto fronts for model problems and for economic data sets in order to show how to find the network modularity tradeoffs between different layers
From linear to nonlinear MPC: bridging the gap via the real-time iteration
No full textLinear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to recent progress in algorithms for solving online the underlying structured quadratic programs. In contrast, nonlinear MPC (NMPC) requires the deployment of more elaborate algorithms, which require longer computation times than linear MPC. Nonetheless, computational speeds for NMPC comparable to those of MPC are now regularly reported, provided that the adequate algorithms are used. In this paper, we aim at clarifying the similarities and differences between linear MPC and NMPC. In particular, we focus our analysis on NMPC based on the real-time iteration (RTI) scheme, as this technique has been successfully tested and, in some applications, requires computational times that are only marginally larger than linear MPC. The goal of the paper is to promote the understanding of RTI-based NMPC within the linear MPC community
Symbolic Performance Adaptation
No full textQuality-of-Service attributes such as performance and reliability heavily depend on the run-time conditions under which software is executed (e.g., workload fluctuation and resources availability). Therefore, it is important to design systems able to adapt their setting and behavior due to these run-time variabilities. In this paper we propose a novel approach based on queuing networks as the quantitative model to represent system configurations. To find a model that fits with continuous changes in run-time conditions we rely on an innovative combination of symbolic analysis and satisfiability modulo theory (SMT). Through symbolic analysis we represent all possible system configurations as a set of nonlinear real constraints. By formulating an SMT problem we are able to devise feasible system configurations at a small computational cost. We study the effectiveness and scalability of our approach on a three-tier web system featuring different levels of redundancy