VirtuWind: Virtual and programmable industrial network prototype deployed in operational wind park.

With anticipated exponential growth of connected devices, future industrial networks require an open solutions architecture facilitated by standards and a strong ecosystem. Such solutions should also deal with range of quality of service requirements imposed by industrial networks. Preserving strict quality of service is particularly challenging when services pass across domains of multiple provides. VirtuWind aims to develop and demonstrate a Software Defined Networking and Network Function Virtualization ecosystem, based on an open, modular and secure framework to address stringent requirements of the industrial networks. A prototype of the framework for intra-domain and inter-domain scenarios will be showcased in real Wind Parks, as a representative use case of industrial networks. This paper details this vision and explains steps forward

VirtuWind: Virtual and Programmable Industrial Network Prototype Deployed in Operational Wind Park

With anticipated exponential growth of connected devices, future industrial networks require an open solutions architecture facilitated by standards and a strong ecosystem.VirtuWind aims to develop and demonstrate an SDN and NFV ecosystem, based on an open, modular and secure framework.A prototype of the framework for intra-domain and inter-domain scenarios will be showcased in real wind parks,as a representative use case of industrial networks. Validate the economic viability of the demonstrated solution is paramount for VirtuWind. This paper details this vision and explains steps forward

Roadmaps to Utopia: Tales of the Smart City

Notions of the Smart City are pervasive in urban development discourses. Various frameworks for the development of smart cities, often conceptualized as roadmaps, make a number of implicit claims about how smart city projects proceed but the legitimacy of those claims is unclear. This paper begins to address this gap in knowledge. We explore the development of a smart transport application, MotionMap, in the context of a £16M smart city programme taking place in Milton Keynes, UK. We examine how the idealized smart city narrative was locally inflected, and discuss the differences between the narrative and the processes and outcomes observed in Milton Keynes. The research shows that the vision of data-driven efficiency outlined in the roadmaps is not universally compelling, and that different approaches to the sensing and optimization of urban flows have potential for empowering or disempowering different actors. Roadmaps tend to emphasize the importance of delivering quick practical results. However, the benefits observed in Milton Keynes did not come from quick technical fixes but from a smart city narrative that reinforced existing city branding, mobilizing a growing network of actors towards the development of a smart region. Further research is needed to investigate this and other smart city developments, the significance of different smart city narratives, and how power relationships are reinforced and constructed through them

Online Monitoring of the Osiris Reactor with the Nucifer Neutrino Detector

Originally designed as a new nuclear reactor monitoring device, the Nucifer detector has successfully detected its first neutrinos. We provide the second shortest baseline measurement of the reactor neutrino flux. The detection of electron antineutrinos emitted in the decay chains of the fission products, combined with reactor core simulations, provides an new tool to assess both the thermal power and the fissile content of the whole nuclear core and could be used by the Inter- national Agency for Atomic Energy (IAEA) to enhance the Safeguards of civil nuclear reactors. Deployed at only 7.2m away from the compact Osiris research reactor core (70MW) operating at the Saclay research centre of the French Alternative Energies and Atomic Energy Commission (CEA), the experiment also exhibits a well-suited configuration to search for a new short baseline oscillation. We report the first results of the Nucifer experiment, describing the performances of the 0.85m3 detector remotely operating at a shallow depth equivalent to 12m of water and under intense background radiation conditions. Based on 145 (106) days of data with reactor ON (OFF), leading to the detection of an estimated 40760 electron antineutrinos, the mean number of detected antineutrinos is 281 +- 7(stat) +- 18(syst) electron antineutrinos/day, in agreement with the prediction 277(23) electron antineutrinos/day. Due the the large background no conclusive results on the existence of light sterile neutrinos could be derived, however. As a first societal application we quantify how antineutrinos could be used for the Plutonium Management and Disposition Agreement.Comment: 22 pages, 16 figures - Version

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables

Poster Abstract: A benchmark for low-power wireless networking

Experimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized bench-marks, our community still uses ad-hoc setups for its exper-iments and struggles to provide a fair comparison between communication protocols. Reasons for this include the di-versity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental val-idation, we make the case for a reference benchmark to pro-mote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmark-ing methodology with the goal to gather feedback from the community rather than propose a definite solution

Poster Abstract: A benchmark for low-power wireless networking

Experimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized bench-marks, our community still uses ad-hoc setups for its exper-iments and struggles to provide a fair comparison between communication protocols. Reasons for this include the di-versity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental val-idation, we make the case for a reference benchmark to pro-mote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmark-ing methodology with the goal to gather feedback from the community rather than propose a definite solution