Nagging: A Distributed, Adversarial Search-Pruning Technique Applied to First-Order Inference

This article introduces a parallel search-pruning technique callednagging. Nagging is sufficiently general to be effective in a number ofdomains; here we focus on an implementation for first-order theorem proving,a domain both responsive to a very simple nagging model and amenable to manyrefinements of this model. Naggings scalability and intrinsic faulttolerance make it particularly suitable for application in commonlyavailable, low-bandwidth, high-latency distributed environments. We presentseveral nagging models of increasing sophistication, demonstrate theireffectiveness empirically, and compare nagging with related work in parallelsearch.     

Boosting Room‐Temperature Magneto‐Ionics in a Non‐Magnetic Oxide Semiconductor

Voltage control of magnetism through electric field‐induced oxygen motion (magneto‐ionics) could represent a significant breakthrough in the pursuit for new strategies to enhance energy efficiency in magnetically actuated devices. Boosting the induced changes in magnetization, magneto‐ionic rates and cyclability continue to be key challenges to turn magneto‐ionics into real applications. Here, it is demonstrated that room‐temperature magneto‐ionic effects in electrolyte‐gated paramagnetic Co₃O₄ films can be largely increased both in terms of generated magnetization (6 times larger) and speed (35 times faster) if the electric field is applied using an electrochemical capacitor configuration (utilizing an underlying conducting buffer layer) instead of placing the electric contacts at the side of the semiconductor (electric‐double‐layer transistor‐like configuration). This is due to the greater uniformity and strength of the electric field in the capacitor design. These results are appealing to widen the use of ion migration in technological applications such as neuromorphic computing or iontronics in general.     

From linear to nonlinear MPC: bridging the gap via the real-time iteration

ABSTRACT

Linear 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.     

Doppler Shift in a Radiating Cable System in Tunnel Environments: A Theoretical Analysis

Wireless Personal Communications - For many years, radiating cables have been used to provide coverage inside tunnels or underground venues. Due to the progress in transport technology and the...     

Wind power ramp event detection with a hybrid neuro-evolutionary approach

In this paper, a hybrid system for wind power ramp events (WPREs) detection is proposed. The system is based on modeling the detection problem as a binary classification problem from atmospheric reanalysis data inputs. Specifically, a hybrid neuro-evolutionary algorithm is proposed, which combines artificial neural networks such as extreme learning machine (ELM), with evolutionary algorithms to optimize the trained models and carry out a feature selection on the input variables. The phenomenon under study occurs with a low probability, and for this reason the classification problem is quite unbalanced. Therefore, is necessary to resort to techniques focused on providing a balance in the classes, such as the synthetic minority over-sampling technique approach, the model applied in this work. The final model obtained is evaluated by a test set using both ELM and support vector machine algorithms, and its accuracy performance is analyzed. The proposed approach has been tested in a real problem of WPREs detection in three wind farms located in different areas of Spain, in order to see the spatial generalization of the method.

     

Automatic modeling of cluttered multi‐room floor plans from panoramic images

We present a novel and light‐weight approach to capture and reconstruct structured 3D models of multi‐room floor plans. Starting from a small set of registered panoramic images, we automatically generate a 3D layout of the rooms and of all the main objects inside. Such a 3D layout is directly suitable for use in a number of real‐world applications, such as guidance, location, routing, or content creation for security and energy management. Our novel pipeline introduces several contributions to indoor reconstruction from purely visual data. In particular, we automatically partition panoramic images in a connectivity graph, according to the visual layout of the rooms, and exploit this graph to support object recovery and rooms boundaries extraction. Moreover, we introduce a plane‐sweeping approach to jointly reason about the content of multiple images and solve the problem of object inference in a top‐down 2D domain. Finally, we combine these methods in a fully automated pipeline for creating a structured 3D model of a multi‐room floor plan and of the location and extent of clutter objects. These contribution make our pipeline able to handle cluttered scenes with complex geometry that are challenging to existing techniques. The effectiveness and performance of our approach is evaluated on both real‐world and synthetic models.     

Wind farm simulations based on a DFIG machine using parallel programming

The Journal of Supercomputing - New computational techniques for simulating a large array of wind turbines are highly needed to model modern electrical grid networks. In this paper, an...     

CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope.