Networked international politics

Network theory and methods are becoming increasingly used to study the causes and consequences of conflict. Network analysis allows researchers to develop a better understanding of the causal dynamics and structural geometry of the complex web of interdependencies at work in the onset, incidence, and diffusion of conflict and peace. This issue features new theoretical and empirical research demonstrating how properly accounting for networked interdependencies has profound implications for our understanding of the processes thought to be responsible for the conflict behavior of state and non-state actors. The contributors examine the variation in networks of states and transnational actors to explain outcomes related to international conflict and peace. They highlight how networked interdependencies affect conflict and cooperation in a broad range of areas at the center of international relations scholarship. It is helpful to distinguish between three uses of networks, namely: (1) as theoretical tools, (2) as measurement tools, and (3) as inferential tools. The introduction discusses each of these uses and shows how the contributions rely on one or several of them. Next, Monte Carlo simulations are used to illustrate one of the strengths of network analysis, namely that it helps researchers avoid biased inferences when the data generating process underlying the observed data contains extradyadic interdependencies. </jats:p

Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery

We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery. We use a convolutional neural network to regress a confidence map indicating the locations of individual trees, which are localized using a peak finding algorithm. Our method provides complete spatial coverage by detecting trees in both public and private spaces, and can scale to very large areas. We performed a thorough evaluation of our method, supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. We trained our model on data from Southern California, and achieved a precision of 73.6% and recall of 73.3% using test data from this region. We generally observed similar precision and slightly lower recall when extrapolating to other California climate zones and image capture dates. We used our method to produce a map of trees in the entire urban forest of California, and estimated the total number of urban trees in California to be about 43.5 million. Our study indicates the potential for deep learning methods to support future urban forestry studies at unprecedented scales

The store-and-flood distributed reflective denial of service attack

Distributed reflective denial of service (DRDoS) attacks, especially those based on UDP reflection and amplification, can generate hundreds of gigabits per second of attack traffic, and have become a significant threat to Internet security. In this paper we show that an attacker can further make the DRDoS attack more dangerous. In particular, we describe a new DRDoS attack called store-and-flood DRDoS, or SF-DRDoS. By leveraging peer-to-peer (P2P) file-sharing networks, SF-DRDoS becomes more surreptitious and powerful than traditional DRDoS. An attacker can store carefully prepared data on reflector nodes before the flooding phase to greatly increase the amplification factor of an attack. We implemented a prototype of SF-DRDoS on Kad, a popular Kademlia-based P2P file-sharing network. With real-world experiments, this attack achieved an amplification factor of 2400 on average, with the upper bound of attack bandwidth at 670 Gbps in Kad. Finally, we discuss possible defenses to mitigate the threat of SF-DRDoS. ? 2014 IEEE.EICPCI-S(ISTP)

Bounded-Cost Bi-Objective Heuristic Search

There are many settings that extend the basic shortest path search problem. In Bounded-Cost Search, we are given a constant bound and the task is to find a solution within the bound. In Bi-Objective Search, each edge is associated with two costs (objectives) and the task is to minimize both objectives. In this paper, we combine both these settings into a new setting of Bounded-Cost Bi-Objective Search. We are given two bounds, one for each objective and the task is to find a solution within these bounds. We provide a scheme for normalizing the two objectives. We then introduce several algorithms for this new setting and compare them experimentally

Individual tree detection in large-scale urban environments using high-resolution multispectral imagery

Systematic maps of urban forests are useful for regional planners and ecologists to understand the spatial distribution of trees in cities. However, manually-created urban forest inventories are expensive and time-consuming to create and typically do not provide coverage of private land. Toward the goal of automating urban forest inventory through machine learning techniques, we performed a comparative study of methods for automatically detecting and localizing trees in multispectral aerial imagery of urban environments, and introduce a novel method based on convolutional neural network regression. Our evaluation is supported by a new dataset of over 1,500 images and almost 100,000 tree annotations, covering eight cities, six climate zones, and three image capture years. Our method outperforms previous methods, achieving 73.6% precision and 73.3% recall when trained and tested in Southern California, and 76.5% precision 72.0% recall when trained and tested across the entire state. To demonstrate the scalability of the technique, we produced the first map of trees across the entire urban forest of California. The map we produced provides important data for the planning and management of California’s urban forest, and establishes a proven methodology for potentially producing similar maps nationally and globally in the future