On the Expressive Power of Counting

http://www.springerlink.com/content/f713305702708547/?p=e5505b568c714aaab33444b548f67070π=7We investigate the expressive power of various extensions of first-order, inductive, and infinitary logic with counting quantifiers. We consider in particular a LOGSPACE extension of first-order logic, and a PTIME extension of fixpoint logic with counters. Counting is a fundamental tool of algorithms. It is essential in the case of unordered structures. Our aim is to understand the expressive power gained with a limited counting ability. We consider two problems: (i) unnested counters, and (ii) counters with no free variables. We prove a hierarchy result based on the arity of the counters under the first restriction. The proof is based on a game technique that is introduced in the paper. We also establish results on the asymptotic probabilities of sentences with counters under the second restriction. In particular, we show that first-order logic with equality of the cardinalities of relations has a 0/1 law

A New Perspective and Extension of the Gaussian Filter

The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filter. GFs represent the belief of the current state by a Gaussian with the mean being an affine function of the measurement. We show that this representation can be too restrictive to accurately capture the dependences in systems with nonlinear observation models, and we investigate how the GF can be generalized to alleviate this problem. To this end, we view the GF from a variational-inference perspective. We analyse how restrictions on the form of the belief can be relaxed while maintaining simplicity and efficiency. This analysis provides a basis for generalizations of the GF. We propose one such generalization which coincides with a GF using a virtual measurement, obtained by applying a nonlinear function to the actual measurement. Numerical experiments show that the proposed Feature Gaussian Filter (FGF) can have a substantial performance advantage over the standard GF for systems with nonlinear observation models.Comment: Will appear in Robotics: Science and Systems (R:SS) 201

Guarded Teams: The Horizontally Guarded Case

Team semantics admits reasoning about large sets of data, modelled by sets of assignments (called teams), with first-order syntax. This leads to high expressive power and complexity, particularly in the presence of atomic dependency properties for such data sets. It is therefore interesting to explore fragments and variants of logic with team semantics that permit model-theoretic tools and algorithmic methods to control this explosion in expressive power and complexity. We combine here the study of team semantics with the notion of guarded logics, which are well-understood in the case of classical Tarski semantics, and known to strike a good balance between expressive power and algorithmic manageability. In fact there are two strains of guardedness for teams. Horizontal guardedness requires the individual assignments of the team to be guarded in the usual sense of guarded logics. Vertical guardedness, on the other hand, posits an additional (or definable) hypergraph structure on relational structures in order to interpret a constraint on the component-wise variability of assignments within teams. In this paper we investigate the horizontally guarded case. We study horizontally guarded logics for teams and appropriate notions of guarded team bisimulation. In particular, we establish characterisation theorems that relate invariance under guarded team bisimulation with guarded team logics, but also with logics under classical Tarski semantics

The Recursive Record Semantics of Objects Revisited

In a call-by-value language, representing objects as recursive records requires using an unsafe fixpoint. We design, for a core language including extensible records, a type system which rules out unsafe recursion and still supports the reconstruction of a principal type. We illustrate the expressive power of this language with respect to object-oriented programming by introducing a sub-language for «mixin-based» programming

Conservative Extensions in Guarded and Two-Variable Fragments

We investigate the decidability and computational complexity of (deductive) conservative extensions in fragments of first-order logic (FO), with a focus on the two-variable fragment FO$^2$ and the guarded fragment GF. We prove that conservative extensions are undecidable in any FO fragment that contains FO$^2$ or GF (even the three-variable fragment thereof), and that they are decidable and 2\ExpTime-complete in the intersection GF$^2$ of FO$^2$ and GF

Common aetiology for diverse language skills in 41/2-year-old twins

Multivariate genetic analysis was used to examine the genetic and environmental aetiology of the interrelationships of diverse linguistic skills. This study used data from a large sample of 4 1/2 year-old twins who were tested on measures assessing articulation, phonology, grammar, vocabulary, and verbal memory. Phenotypic analysis suggested two latent factors: articulation (2 measures) and general language (the remaining 7), and a genetic model incorporating these factors provided a good fit to the data. Almost all genetic and shared environmental influences on the 9 measures acted through the two latent factors. There was also substantial aetiological overlap between the two latent factors, with a genetic correlation of 0·64 and shared environment correlation of 1·00. We conclude that to a large extent, the same genetic and environmental factors underlie the development of individual differences in a wide range of linguistic skills

Deep Markov Random Field for Image Modeling

Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.Comment: Accepted at ECCV 201

Bisimulation Quantifiers and Uniform Interpolation for Guarded First Order Logic

The idea that the good model-theoretic and algorithmic properties of Modal Logics are due to the guarded nature of their quantification was put forward by Andreka, van Benthem and Nemeti in a series of papers in the 1990s, exploiting the satisfiability problem, the tree model property, and other similar properties of the Guarded Fragment of First Order Logic (GF). Since then, further work on the Guarded Fragment has been done by various authors, in some cases reinforcing this idea, in some others not. At least at first sight, Craig interpolation is on the negative side: there are implications in GF without an interpolant in GF, while Modal Logic (and even the μ-calculus, a powerful extension of Modal Logic) enjoys a much stronger form of interpolation, the uniform one, in which the interpolant of a valid implication not only exists, but only depends on the antecedent and on the common language of antecedent and consequent. However, Hoogland and Marx proved that Craig interpolation is restored in GF if we consider the modal character of GF with more attention, that is, if relations appearing on guards are viewed as “modalities” and the rest as “propositions”, and only the latter enter in the common language. In this paper we strengthen this result by showing that GF enjoys a Modal Uniform Interpolation Theorem (in the sense of Hoogland and Marx)

Rate of Convergence of Increasing Path-Vector Routing Protocols

A good measure of the rate of convergence of path-vector protocols is the number of synchronous iterations required for convergence in the worst case. From an algebraic perspective, the rate of convergence depends on the expressive power of the routing algebra associated with the protocol. For example in a network of $n$ nodes, shortest-path protocols are guaranteed to converge in $O(n)$ iterations. In contrast the algebra underlying the Border Gateway Protocol (BGP) is in some sense too expressive and the protocol is not guaranteed to converge. There is significant interest in finding well-behaved algebras that still have enough expressive power to satisfy network operators. Recent theoretical results have shown that by constraining routing algebras to those that are ``strictly increasing'' we can guarantee the convergence of path-vector protocols. Currently the best theoretical worst-case upper bound for the convergence of such algebras is $O(n!)$ iterations. However in practice it is difficult to find examples that do not converge in $n$ iterations. In this paper we close this gap. We first present a family of network configurations that converges in $\Theta(n^2)$ iterations, demonstrating that the worst case is $\Omega(n^2)$ iterations. We then prove that path-vector protocols with a strictly increasing algebra are guaranteed to converge in $O(n^2)$ iterations. Together these results establish a tight $\Theta(n^2)$ bound. This is another piece of the puzzle in showing that ``strictly increasing" is, at least on a technical level, a reasonable constraint for practical policy-rich protocols. {In memory of Abha Ahuja