Cross‐border acquisitions by sovereign wealth funds: A legitimacy‐based view

Research SummaryDrawing on institutional economics and the legitimacy-based view of political risk, we investigate the factors determining the realization of cross-border investments by sovereign wealth funds (SWFs), whose investments often suffer from a lack of legitimacy in host countries. Using matching models on all the realized and potential investments, we find that investments are more likely to materialize when the SWF home country and the host country enjoy cordial political relations or are involved in a trade agreement. Contrary to the theoretical predictions, SWF politicization does not per se represent an impediment to the realization of investments. Rather, it has a negative effect on the likelihood of an investment's realization only in the presence of trade agreements.Managerial SummaryA recent trend in the global economy is the increasing cross-border investment activity undertaken by sovereign wealth funds (SWFs), large investment vehicles where financial and political goals often co-exist. On the grounds of possible financial or political destabilization, SWFs' cross-border investments attract scrutiny and suspicion in host countries, hindering their realization. We analyze SWF- and country-level factors that may determine the successful realization of SWFs' cross-border acquisitions. We suggest that managers ex ante select target firms and host countries by considering their fund's governance and degree of independence from home-country politics in interaction with bilateral (home-host country) political and economic relations, so as to secure legitimacy for their investments and maximize the chances that cross-border investment strategies may materialize

The Earth Expansion Evidence – A Challenge for Geology, Geophysics, Astronomy and General Knowledge

The 37th Workshop of the International School of Geophysics held on 4-9 October 2011 in Erice (Sicily, Italy), was a long awaited occasion which allowed to gather the small scientific community of expansionists. Aims, results, discussions and varia umanità of this important event are presented thereafter

Development of n-DoF Preloaded structures for impact mitigation in cobots

A core issue in collaborative robotics is that of impact mitigation, especially when collisions happen with operators. Passively compliant structures can be used as the frame of the cobot, although, usually, they are implemented by means of a single-degree-offreedom (DoF). However, n-DoF preloaded structures offer a number of advantages in terms of flexibility in designing their behavior. In this work, we propose a comprehensive framework for classifying n-DoF preloaded structures, including one-, two-, and threedimensional arrays. Furthermore, we investigate the implications of the peculiar behavior of these structures-which present sharp stiff-to-compliant transitions at designdetermined load thresholds-on impact mitigation. To this regard, an analytical n-DoF dynamic model was developed and numerically implemented. A prototype of a 10DoF structure was tested under static and impact loads, showing a very good agreement with the model. Future developments will see the application of n-DoF preloaded structures to impact-mitigation on cobots and in the field of mobile robots, as well as to the field of novel architected materials

A Kohonen SOM architecture for intrusion detection on in-vehicle communication networks

The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both as supervised and unsupervised learning. SOM found broad application in security issues, but was never performed on in-vehicle communication networks. We performed two approaches, first using a supervised X-Y fused Kohonen network (XYF) and then combining the XYF network with a K-means clustering algorithm (XYF-K) in order to improve the efficiency of the network. The models were tested on an open source dataset concerning data messages sent on a CAN bus 2.0B and containing large traffic volume with a low number of features and more than 2000 different attack types, sent totally at random. Despite the complex structure of the CAN bus dataset, the proposed architectures showed a high performance in the accuracy of the detection of attack messages

Intrusion detection for in-vehicle communication networks: An unsupervised kohonen SOM approach

The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach

Guidance for interpretation of CBD categories on introduction pathways

Technical note prepared by IUCN for the European Commission. This note has been drafted by a team of experts under the supervision of IUCN within the framework of the contract No 07.0202/2016/739524/SER/ENV.D.2 “Technical and Scientific support in relation to the Implementation of Regulation 1143/2014 on Invasive Alien Species”

Image preprocessing for artistic robotic painting

Artistic robotic painting implies creating a picture on canvas according to a brushstroke map preliminarily computed from a source image. To make the painting look closer to the human artwork, the source image should be preprocessed to render the effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and brushstroke coherence. We propose an algorithm for aerial perspective amplification based on principles of light scattering using a depth map, an algorithm for gamut compression using nonlinear hue transformation and an algorithm for image gradient filtering for obtaining a well-coherent brushstroke map with a reduced number of brushstrokes, required for practical robotic painting. The described algorithms allow interactive image correction and make the final rendering look closer to a manually painted artwork. To illustrate our proposals, we render several test images on a computer and paint a monochromatic image on canvas with a painting robot