Offloading content routing cost from routers

The publish/subscribe paradigm has lately received much attention. In publish/subscribe systems, a specialized event-based middleware delivers notifications of events created by producers (publishers) to consumers (subscribers) interested in that particular event. It is considered a good approach for implementing Internet-wide distributed systems as it provides full decoupling of the communicating parties in time, space and synchronization. One flavor of the paradigm is content-based publish/subscribe which allows the subscribers to express their interests very accurately. In order to implement a content-based publish/subscribe middleware in way suitable for Internet scale, its underlying architecture must be organized as a peer-to-peer network of content-based routers that take care of forwarding the event notifications to all interested subscribers. A communication infrastructure that provides such service is called a content-based network. A content-based network is an application-level overlay network. Unfortunately, the expressiveness of the content-based interaction scheme comes with a price - compiling and maintaining the content-based forwarding and routing tables is very expensive when the amount of nodes in the network is large. The routing tables are usually partially-ordered set (poset) -based data structures. In this work, we present an algorithm that aims to improve scalability in content-based networks by reducing the workload of content-based routers by offloading some of their content routing cost to clients. We also provide experimental results of the performance of the algorithm. Additionally, we give an introduction to the publish/subscribe paradigm and content-based networking and discuss alternative ways of improving scalability in content-based networks. ACM Computing Classification System (CCS): C.2.4 [Computer-Communication Networks]: Distributed Systems - Distributed applications

Forest governance and climate change adaptation : Case studies of four African countries

Africa is threatened by climate change. The adaptive capacity of local communities continues to be weakened by ineffective and inefficient livelihood strategies and inappropriate development interventions. One of the greatest challenges for climate change adaptation in Africa is related to the governance of natural resources used by vulnerable poor groups as assets for adaptation. Practical and good governance activities for adaptation in Africa is urgently and much needed to support adaptation actions, interventions and planning. The adaptation role of forests has not been as prominent in the international discourse and actions as their mitigation role. This study therefore focused on the forest as one of the natural resources used for adaptation. The general objective of this research was to assess the extent to which cases of current forest governance practices in four African countries Burkina Faso, The Democratic Republic of Congo (DRC), Ghana and Sudan are supportive to the adaptation of vulnerable societies and ecosystems to impacts of climate change. Qualitative and quantitative analyses from surveys, expert consultations and group discussions were used in analysing the case studies. The entire research was guided by three conceptual sets of thinking forest governance, climate change vulnerability and ecosystem services. Data for the research were collected from selected ongoing forestry activities and programmes. The study mainly dealt with forest management policies and practices that can improve the adaptation of forest ecosystems (Study I) and the adaptive capacity through the management of forest resources by vulnerable farmers (Studies II, III, IV and V). It was found that adaptation is not part of current forest policies, but, instead, policies contain elements of risk management practices, which are also relevant to the adaptation of forest ecosystems. These practices include, among others, the management of forest fires, forest genetic resources, non-timber resources and silvicultural practices. Better livelihood opportunities emerged as the priority for the farmers. These vulnerable farmers had different forms of forest management. They have a wide range of experience and practical knowledge relevant to ensure and achieve livelihood improvement alongside sustainable management and good governance of natural resources. The contributions of traded non-timber forest products to climate change adaptation appear limited for local communities, based on their distribution among the stakeholders in the market chain. Plantation (agro)forestry, if well implemented and managed by communities, has a high potential in reducing socio-ecological vulnerability by increasing the food production and restocking degraded forest lands. Integration of legal arrangements with continuous monitoring, evaluation and improvement may drive this activity to support short, medium and long term expectations related to adaptation processes. The study concludes that effective forest governance initiatives led by vulnerable poor groups represent one practical way to improve the adaptive capacities of socio-ecological systems against the impacts of climate change in Africa.

Search for the Higgs Boson Using Neural Networks in Events with Missing Energy and b-Quark Jets in pp̅ Collisions at √s=1.96 TeV

We report on a search for the standard model Higgs boson produced in association with a $W$ or $Z$ boson in $p\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV recorded by the CDF II experiment at the Tevatron in a data sample corresponding to an integrated luminosity of 2.1 fb$^{-1}$. We consider events which have no identified charged leptons, an imbalance in transverse momentum, and two or three jets where at least one jet is consistent with originating from the decay of a $b$ hadron. We find good agreement between data and predictions. We place 95% confidence level upper limits on the production cross section for several Higgs boson masses ranging from 110$\gevm$ to 150$\gevm$. For a mass of 115$\gevm$ the observed (expected) limit is 6.9 (5.6) times the standard model prediction.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.