Abstract not found

Abstract. Existing low-latency anonymity networks are vulnerable to traffic analysis, so location diversity of nodes is essential to defend against attacks. Previous work has shown that simply ensuring geographical diversity of nodes does not resist, and in some cases exacerbates, the risk of traffic analysis by ISPs. Ensuring high autonomous-system (AS) diversity can resist this weakness. However, ISPs commonly connect to many other ISPs in a single location, known as an Internet eXchange (IX). This paper shows that IXes are a single point where traffic analysis can be performed. We examine to what extent this is true, through a case study of Tor nodes in the UK. Also, some IXes sample packets flowing through them for performance analysis reasons, and this data could be exploited to de-anonymize traffic. We then develop and evaluate Bayesian traffic analysis techniques capable of processing this sampled data. 1

Bayesian probability theory is an inference calculus, which originates from a generalization of inclusion on the Boolean lattice of logical assertions to a degree of inclusion represented by a real number. Dual to this lattice is the distributive lattice of questions constructed from the ordered set of down-sets of assertions, which forms the foundation of the calculus of inquiry—a generalization of information theory. In this paper we introduce this novel perspective on these spaces in which machine learning is performed and discuss the relationship between these results and several proposed generalizations of information theory in the literature.

We describe feature vector indexing, a new, non-perfect indexing method for clause subsumption. It is suitable for both forward (i.e., finding a subsuming clause in a set) and backward (finding all subsumed clauses in a set) subsumption. Moreover, it is easy to implement, but still yields excellent performance in practice. As an added benefit, by restricting the selection of features used in the index, our technique immediately adapts to indexing modulo arbitrary AC theories with only minor loss of efficiency. Alternatively, the feature selection can be restricted to result in set subsumption. Feature vector indexing has been implemented in our equational theorem prover E, and has enabled us to integrate new simplification techniques making heavy use of subsumption. We experimentally compare the performance of the prover for a number of strategies using feature vector indexing and conventional sequential subsumption. Key words: automated theorem proving, saturation, subsumption, indexing 1

Abstract. We investigate Bayesian and Maximum Entropy methods for doing inference under uncertainty. This investigation is primarily through concrete examples that have been previously investigated in the literature. We find that it is possible to do Bayesian and MaxEnt inference using the same information, despite claims to the contrary, and that they lead to different results. We find that these differences are due to the Bayesian inference not assuming anything beyond the given prior probabilities and the data, whereas MaxEnt implicitly makes strong independence assumptions, and assumes that the given constraints are the only ones operating. We also show that maximum likelihood and maximum a posteriori estimators give different and misleading estimates in our examples compared to posterior mean estimates. We generalize the classic method of maximum entropy inference to allow for uncertainty in the constraint values. This generalized MaxEnt (GME) makes MaxEnt inference applicable to a much wider range of problems, and makes direct comparison between Bayesian and MaxEnt inference possible. Also, we show that MaxEnt is a generalized principle of independence, and this property is what makes it the preferred inference method in many cases.

Abstract. Trust is a mechanism for managing the uncertainty about autonomous entities and the information they store, and so can play an important role in any decentralized system. As a result, trust has been widely studied in multiagent systems and related fields such as the semantic web. Managing information about trust involves inference with uncertain information, decision making, and dealing with commitments and the provenance of information, all areas to which systems of argumentation have been applied. Here we discuss the application of argumentation to reasoning about trust, identifying some of the components that an argumentation-based system for reasoning about trust would need to contain and sketching the work that would be required to provide such a system. 1

Physicists have, hitherto, mostly adopted a frequentist conception of probability, according to which probability statements apply only to ensembles. It is argued that we should, instead, adopt an epistemic, or Bayesian conception, in which probabilities are conceived as logical constructs rather than physical realities, and in which probability statements do apply directly to individual events. The question is closely related to the disagreement between the orthodox school of statistical thought and the Bayesian school. It has important technical implications (it makes a difference, what statistical methodology one adopts). It may also have important implications for the interpretation of the quantum state. 1 1.

The last century saw the application of Boolean algebra toward the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in understanding the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we identified the algebra of questions as the free distributive algebra, which now allows us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper we begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. We will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, experiment to perform, or measurement to take given what they have learned and what they are designed to understand.

The effect of Richard T. Cox's contribution to probability theory was to generalize Boolean implication among logical statements to degrees of implication, which are manipulated using rules derived from consistency with Boolean algebra. These rules are known as the sum rule, the product rule and Bayes' Theorem, and the measure resulting from this generalization is probability. In this paper, I will describe how Cox's technique can be further generalized to include other algebras and hence other problems in science and mathematics. The result is a methodology that can be used to generalize an algebra to a calculus by relying on consistency with order theory to derive the laws of the calculus. My goals are to clear up the mysteries as to why the same basic structure found in probability theory appears in other contexts, to better understand the foundations of probability theory, and to extend these ideas to other areas by developing new mathematics and new physics. The relevance of this methodology will be demonstrated using examples from probability theory, number theory, geometry, information theory, and quantum mechanics.

Abstract. The possibility that we live in a special place in the universe, close to the centre of a large void, seems an appealing alternative to the prevailing interpretation of the acceleration of the universe in terms of a ΛCDM model with a dominant dark energy component. In this paper we confront the asymptotically flat Lemaitre-Tolman-Bondi (LTB) models with a series of observations, from Type Ia Supernovae to Cosmic Microwave Background and Baryon Acoustic Oscillations data. We propose two concrete LTB models describing a local void in which the only arbitrary functions are the radial dependence of the matter density ΩM and the Hubble expansion rate H. We find that all observations can be accommodated within 1 sigma, for our models with 4 or 5 independent parameters. The best fit models have a χ 2 very close to that of the ΛCDM model. A general Fortran program for comparing LTB models with cosmological observations, that has been used to make the parameter scan in this paper, is made public, and can be downloaded at