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State estimation allows to monitor power networks, exploiting field measurements to derive the most likely grid state. In the literature, measurement errors are usually assumed to follow zero-mean Gaussian distributions; however, it has been shown that this assumption often does not hold. One such example is when considering pseudo-measurements. In distribution networks, a significant amount of pseudo-measurements might be necessary, due to the scarcity of real-time measurements. In this paper, a state estimator is presented which allows to model measurement uncertainty with any continuous distribution, without approximations. This becomes possible by writing state estimation as a general maximum-likelihood estimation-based constrained optimization problem. To realistically describe distribution networks, three-phase unbalanced power flow equations are used. Results are presented that illustrate the differences in accuracy and computational effort between different uncertainty modeling methods, for the IEEE European Low Voltage Test Feeder.

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. Crucially however, these frameworks require large human-annotated datasets for training and the resulting models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming based computational strategy that generates transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets, a feature which confers tremendous flexibility, speed, and functionality to this approach. We also deployed Kartezio to solve semantic and instance segmentation problems in four real-world Use Cases, and showcase its utility in imaging contexts ranging from high-resolution microscopy to clinical pathology. By successfully implementing Kartezio on a portfolio of images ranging from subcellular structures to tumoral tissue, we demonstrated the flexibility, robustness and practical utility of this fully explicable evolutionary designer for semantic and instance segmentation., Comment: 36 pages, 6 main Figures. The Extended Data Movie is available at the following link: https://www.youtube.com/watch?v=r74gdzb6hdA. The source code is available on Github: https://github.com/KevinCortacero/Kartezio

To address the challenges and exploit the opportunities that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these require accurate network models to work effectively, including user phase connectivity. However, such information is often not available. This paper proposes a novel method to identify the phase connectivity of single- and three-phase distribution consumers using smart meter measurements. The method is based on state estimation and mixed-integer linear programming, and requires shorter measurement collection times compared to statistical and machine learning-based techniques, for the same level of accuracy. Consequently, the duration of measurement efforts/campaigns aimed at obtaining this type of information reduces, accelerating the system knowledge acquisition process and potentially reducing the campaigns' costs. A technique is illustrated that allows to solve large networks in acceptable time despite the considerable number of integer variables. Extensive computational results are presented for publicly available low voltage feeders.

This paper proposes a novel congestion mitigation strategy for low voltage residential feeders in which the rising power demand due to the electrification of the transport and heating systems leads to congestion problems. The strategy is based on requiring residential customers to limit their demand for a certain amount of time in exchange for economic benefits. The main novelty of the method consists of combining a thorough representation of the network physics with advanced constraints that ensure the comfort of residential users, in a scalable manner that suits real systems. The mitigation strategy is presented from a DSO perspective, and takes the form of contracts between users and system operator. The focus on user comfort aims to make the contracts appealing, encouraging users to voluntarily enroll in the proposed mitigation scheme. The presented solution is implemented as a mixed-integer multi-period optimal power flow problem which relies on a linearized three-phase power flow formulation. Calculations on 100 real-life distribution feeders are performed, to analyze the congestion-relieving potential of several possible system operator-user contracts. From a planning perspective, the results can help the system operator define contractual terms that make a specific congestion mitigation scheme effective and viable. From an operational perspective, the same calculations can be used to optimally schedule power reduction on a day-ahead basis.

State estimation plays a key role in the transition from the passive to the active operation of distribution systems, as it allows to monitor these networks and, successively, to perform control actions. However, designing state estimators for distribution systems carries a significant amount of challenges. This is due to the physical complexity of the networks, e.g., phase unbalance, and limited measurements. Furthermore, the features of the distribution system present significant local variations, e.g., voltage level and number and type of customers, which makes it hard to design a "one-size-fits-all" state estimator. The present paper introduces a unifying framework that allows to easily implement and compare diverse unbalanced static state estimation models. This is achieved by formulating state estimation as a general constrained optimization problem. The advantages of this approach are described and supported by numerical illustration on a large set of distribution feeders. The framework is also implemented and made available open-source.

We present ongoing work on a tool that consists of two parts: (i) A raw micro-level abstract world simulator with an interface to (ii) a 3D game engine, translator of raw abstract simulator data to photorealistic graphics. Part (i) implements a dedicated cellular automata (CA) on reconfigurable hardware (FPGA) and part (ii) interfaces with a deep learning framework for training neural networks. The bottleneck of such an architecture usually lies in the fact that transferring the state of the whole CA significantly slows down the simulation. We bypass this by sending only a small subset of the general state, which we call a 'locus of visibility', akin to a torchlight in a darkened 3D space, into the simulation. The torchlight concept exists in many games but these games generally only simulate what is in or near the locus. Our chosen architecture will enable us to simulate on a micro level outside the locus. This will give us the advantage of being able to create a larger and more fine-grained simulation which can be used to train neural networks for use in games., Comment: Published as a short paper at IEEE CIG2017

This paper describes a successful attempt to combine two relatively new technologies: Stream Control Transmission Protocol (SCTP) and the programming language Go, achieved by extending the existing Go network library with SCTP. SCTP is a reliable, message-oriented transport layer protocol, similar to TCP and UDP. It offers sequenced delivery of messages over multiple streams, network fault tolerance via multihoming support, resistance against flooding and masquerade attacks and congestion avoidance procedures. It has improvements over wider-established network technologies and is gradually gaining traction in the telecom and Internet industries. Go is an open source, concurrent, statically typed, compiled and garbage-collected language, developed by Google Inc. Go's main design goals are simplicity and ease of use and it has a syntax broadly similar to C. Go has good support for networked and multicore computing and as a system language is often used for networked applications, however it doesn't yet support SCTP. By combining SCTP and Go, software engineers can exploit the advantages of both technologies. The implementation of SCTP extending the Go network library was done on FreeBSD and Mac OS X, the two operating systems that contain the most up to date implementation of the SCTP specification., Comment: Published in the proceedings of AsiaBSD 2013, at Tokyo, Japan

The display of antisocial behavior by children and youths in America is recognized as one of the most pressing concerns facing educators today. To meet the educational needs of these students while maintaining safe school environments, school districts across the nation have increasingly looked toward alternative educational programs. Approximately 2%, or roughly 280,000 youths, attend alternative schools in the United States (J. A. Grunbaum et al., 1999). For the most part, these students are at high risk for school failure or display significantly challenging behavior so that school officials have sought to have them educated in classrooms or facilities away from other students. As a result, students with significant behavioral concerns or educational disabilities are clustered together into alternative educational programs. The author explores alternative education programs serving students who display antisocial behavior and discusses research on the impact of alternative school placement on the display of antisocial, violent, and aggressive behavior within these settings. The author also focuses on the social context of the alternative school that might exacerbate the development of antisocial behavior and highlights empirically validated prevention and intervention efforts. (Contains 1 table.)

Review of the current knowledge concerning youth violence and gang behavior considers risk factors for violence and gang formation, functions served by violence and gang membership, and strategies that have been empirically validated to be either beneficial or ineffective. Following an introductory chapter, the first chapter looks at the nature of aggression, including risk factors associated with aggression and violence, the function of violence, and peer association and the development of violence and aggression. The following chapter focuses on youth gangs including reasons for gang memberships and why schools cannot combat violence and gang activity alone. A self-study to help schools identify needs and resources is included. Next, prevention and intervention programs are addressed with emphasis on empirically validated or promising programs. The role of functional assessment of behavior and the student is then considered, followed by some cautionary notes concerning grouping high-risk students together for intervention, use of highly punitive consequences for behavior, and employing gang members or ex-gang members to deter gang affiliation. (Contains approximately 75 references.) (DB)

This introductory article discusses social contexts that help promote either student success or student failure. It explores the effects of family, peers, neighborhood and community, and school on student learning and achievement. The content of following articles is described. (Contains references.) (CR)

Maintains that schools must help children become adults in the most violent nation in the industrialized world. Discusses four types of youth violence: (1) situational; (2) relationship; (3) predatory; and (4) psychopathological. Concludes that schoolwide prevention programs are essential. (CFR)