Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav

Cosmic Neutrino Pevatrons: A Brand New Pathway to Astronomy, Astrophysics, and Particle Physics

The announcement by the IceCube Collaboration of the observation of 28 cosmic neutrino candidates has been greeted with a great deal of justified excitement. The data reported so far depart by 4.3\sigma from the expected atmospheric neutrino background, which raises the obvious question: "Where in the Cosmos are these neutrinos coming from?" We review the many possibilities which have been explored in the literature to address this question, including origins at either Galactic or extragalactic celestial objects. For completeness, we also briefly discuss new physics processes which may either explain or be constrained by IceCube data.Comment: This is a review article solicited for the inaugural edition of Journal of High Energy Astrophysics (JHEAp). Matching version accepted for publicatio

Carbon Fertilisation is the Primary Driver of Shrub Encroachment in South African Savannahs

Woody plant encroachment has been documented for savannahs and grasslands in nearly all continents. Yet the drivers of this process remain unclear, with a range of local and global factors postulated. The traditional ecological narrative dictates that shrub encroachment is a localised phenomenon, resulting from poor land management regimes. The most frequently proposed mechanisms are overgrazing and suppression of fire, both of which are common management techniques in sub-Saharan Africa. More recently, increased focus has been directed at the role of global factors in woody cover dynamics. As savannah woody cover is constrained by both total and wet season rainfall, changes in precipitation regime have been proposed as drivers of shrub encroachment. This theory has been supported by small-scale field experiments showing shrubs disproportionally benefiting from increases in rainfall frequency, amount, and variability. A further potential global factor is the ongoing rise in atmospheric CO2 concentrations since the industrial revolution. A theoretical understanding of water limitations to woody cover in savannahs makes it reasonable to assume that CO2-driven increases would be concentrated in water-limited environments. This has been observed across South Africa using aerial photography and globally using satellite-derived Rain Use Efficiency (RUE). Here, we combine satellite-derived fractional woody cover maps with a suite of potentially explanatory variables, to elucidate on the potential drivers and mechanisms of woody cover change, in South African savannahs. The study area consists of the Limpopo and North West Provinces in northern South Africa. These municipalities cover a plurality of the savannah biome within South Africa (193,200 km2, 49% of the total savannah area), in addition to containing 33,830 km2 of grassland. More specifically, we test the three, abovementioned, competing hypotheses on the drivers of woody encroachment for South African savannahs. Our modelling framework was developed using Generalised Additive Models (GAM). We collated a series of 11 variables that have a hypothetical basis for explaining woody cover changes. These variables can be grouped into three categories: rainfall-derived, human, and natural factors. Fractional woody cover changes were mapped using Landsat-derived % woody cover layers, based on the methodology developed in Higginbottom et al. (2018, ISPRS Journal Ph&RS, 139, 88-102). In summary, two five-year epochs (1984-1988 and 2008-2012) of Landsat imagery were used to generate pixel-level seasonal spectral variability metrics, at 120 m resolution. Reducing the pixel resolutions improves the classification accuracies, and is more suited for observing overall trends. High-resolution imagery were classified into woody/non woody masks, and used as training data for a Random Forest regression for the fractional cover of each 120m-pixel. The Random Forest model was applied to the Landsat metric stacks to generate the two epochal maps. We then calculated both the absolute percentage change, and the relative percentage change in woody cover between the two maps. The fitted models had R2 values of 0.39 for absolute change and 0.41 for relative change. The results show that the modelled variables most closely matched the a-priori responses of the carbon fertilisation hypothesis. In recent years, this explanation has been postulated by studies using a variety of methods to account for the observed woody encroachment. Further work in this arena is still necessary, particularly where the data sources are sub-optimal. Land-use history and rainfall dynamics are especially difficult to quantify and would require further investigation. Furthermore, additional factors, such as reactive nitrogen deposition and mega-fauna extinctions, are likely to be relevant but where not included in our models. If carbon fertilisation is the key driver of shrub encroachment in savannahs, it would raise concerns for future environmental change: as CO2 levels continue to rise more savannahs and grassland are likely to experience an increase in woody cover levels, which has been linked to savannah land degradation

AVIAN DIVERSITY AND ABUNDANCE IN RELATION TO SEASON, LIVESTOCK PRESENCE AND VEGETATION COVER IN A MEDITERRANEAN COASTAL WETLAND

Coastal wetlands are considered as systems of high avian diversity and are usually used for livestock production throughout the world. In this study, the diversity and seasonal abundance of avian species were monitored for two years on a monthly basis in a coastal grazing land in Evros Delta (Greece). The effects of livestock (cattle) presence and different classes of vegetation cover on the number of bird species were also investigated. A total of 96 bird species belonging to 29 families were recorded. The most commonly encountered species was the Eurasian skylark Alauda arvensis. The cattle presence was not significantly correlated (p>0.05) with the abundance of recorded bird species. On the contrary, patches with vegetation cover 25.1 - 50.0% and 50.1 - 75.0 % were used by more bird species in relation to patches with cover ≤25.0% or >75.0%. We concluded that the use of livestock grazing to preserve the desired vegetation cover (25 – 75%) is a promising management tool

Local Model Reconstruction Attacks in Federated Learning and their Uses

In this paper, we initiate the study of local model reconstruction attacks for federated learning, where a honest-but-curious adversary eavesdrops the messages exchanged between a targeted client and the server, and then reconstructs the local/personalized model of the victim. The local model reconstruction attack allows the adversary to trigger other classical attacks in a more effective way, since the local model only depends on the client's data and can leak more private information than the global model learned by the server. Additionally, we propose a novel model-based attribute inference attack in federated learning leveraging the local model reconstruction attack. We provide an analytical lower-bound for this attribute inference attack. Empirical results using real world datasets confirm that our local reconstruction attack works well for both regression and classification tasks. Moreover, we benchmark our novel attribute inference attack against the state-of-the-art attacks in federated learning. Our attack results in higher reconstruction accuracy especially when the clients' datasets are heterogeneous. Our work provides a new angle for designing powerful and explainable attacks to effectively quantify the privacy risk in FL