No description available.

Forgetting is an operation which removes information from a set of logical statements, such that a) the language used by the logic is simplified; and b) as much information as possible from the original logical statements are preserved. Forgetting operations are useful in a variety of contexts, including knowledge representation, where it is necessary to have an operation for removing information from knowledge bases; and the problem of relevance, where logical statements are simplified by removing irrelevant information. In this thesis we consider forgetting operations on logic programs with negation-as-failure according to the stable model semantics. There are existing notions of forgetting on logic programs in the literature: the strong forgetting and weak forgetting of Zhang and Foo, and the semantic approach to forgetting introduced by Wang et al. However, these notions are inadequate: the strong and weak forgettings are defined syntactically with no obvious connections to semantic notions of forgetting; while the semantic approach of Wang et al. does not take into account ``hidden'' information encoded in unused rules. The idea of equivalence on logic programs capture the extent of information contained in a logic program. We consider that two logic programs are equivalent iff the two programs contain the same information. For logic programs, there are many different possible notions of equivalence. We look at the well-known notion of strong equivalence and a new notion of equivalence which we call T-equivalence. Associated with each of these equivalences is a consequence relation on logic program rules. We present sound and complete set of inference rules for both consequence relations. We present a novel approach to logic program forgetting which uses as its basis a set of postulates, which are defined relative to a notion of equivalence. We show that if we use T-equivalence as the equivalence relation, then the only possible forgetting operations (up to equivalence) are strong forgetting and weak forgetting. If strong equivalence is used instead, then there are also only two possible forgetting operations (up to equivalence).

The field of compressive sensing deals with the recovery of a sparse signal from a small set of measurements or linear projections of the signal. In this thesis, we introduce a stochastic framework that allows a collection of correlated sparse signals to be recovered by exploiting both intra and inter signal correlation. Our approach differs from others by not assuming that the collection of sparse signals have a common support or a common component; in some cases, this assumption does not hold true. Imagine a simplified cognitive radio problem, where users can send a single tone (sine-wave) in a finite number of frequencies; it is desired to find the used frequencies over a large area (creation of a radio map). This is a sparse problem; however, as we move spatially, the occuppied frequencies change, thus voiding the assumption of a common support/component. Our solution to multi sparse signal recovery addresses this problem, where signals that are close geographically are highly correlated and their support gradually changes as the distance between signals grow. Our approach consists of the creation of a probabilistic model that accounts for inter and intra signal correlation and then using belief propagation to calculate the posterior distribution of the signals and perform recovery.

The field of compressive sensing deals with the recovery of a sparse signal from a small set of measurements or linear projections of the signal. In this thesis, we introduce a stochastic framework that allows a collection of correlated sparse signals to be recovered by exploiting both intra and inter signal correlation. Our approach differs from others by not assuming that the collection of sparse signals have a common support or a common component; in some cases, this assumption does not hold true. Imagine a simplified cognitive radio problem, where users can send a single tone (sine-wave) in a finite number of frequencies; it is desired to find the used frequencies over a large area (creation of a radio map). This is a sparse problem; however, as we move spatially, the occuppied frequencies change, thus voiding the assumption of a common support/component. Our solution to multi sparse signal recovery addresses this problem, where signals that are close geographically are highly correlated and their support gradually changes as the distance between signals grow. Our approach consists of the creation of a probabilistic model that accounts for inter and intra signal correlation and then using belief propagation to calculate the posterior distribution of the signals and perform recovery.

Thesis--Copenhagen. / Errata: p. [316]. Includes bibliographical references (p. [305]-311).

No description available.

The disciplines of Philosophy and Religious Studies contain a great deal of literature pertaining to the nature of reality, religious belief and God. This literature may be broadly divided into two categories, namely the rational or reasonable and the non-rational or unreasonable. Both these categories, as well as the variables within these categories are often responded to as if they are self-evident entities that exist in and by themselves; for example: religious constructs, political institutions, nation states, cultural institutions and the contents of the electronic media. My purpose in this thesis is to argue that all these investigative frameworks and conceptual belief systems effectively follow a certain dynamic.

No description available.

Forgetting is an operation which removes information from a set of logical statements, such that a) the language used by the logic is simplified; and b) as much information as possible from the original logical statements are preserved. Forgetting operations are useful in a variety of contexts, including knowledge representation, where it is necessary to have an operation for removing information from knowledge bases; and the problem of relevance, where logical statements are simplified by removing irrelevant information. In this thesis we consider forgetting operations on logic programs with negation-as-failure according to the stable model semantics. There are existing notions of forgetting on logic programs in the literature: the strong forgetting and weak forgetting of Zhang and Foo, and the semantic approach to forgetting introduced by Wang et al. However, these notions are inadequate: the strong and weak forgettings are defined syntactically with no obvious connections to semantic notions of forgetting; while the semantic approach of Wang et al. does not take into account ``hidden'' information encoded in unused rules. The idea of equivalence on logic programs capture the extent of information contained in a logic program. We consider that two logic programs are equivalent iff the two programs contain the same information. For logic programs, there are many different possible notions of equivalence. We look at the well-known notion of strong equivalence and a new notion of equivalence which we call T-equivalence. Associated with each of these equivalences is a consequence relation on logic program rules. We present sound and complete set of inference rules for both consequence relations. We present a novel approach to logic program forgetting which uses as its basis a set of postulates, which are defined relative to a notion of equivalence. We show that if we use T-equivalence as the equivalence relation, then the only possible forgetting operations (up to equivalence) are strong forgetting and weak forgetting. If strong equivalence is used instead, then there are also only two possible forgetting operations (up to equivalence).

Thesis (Ph. D.)--University of Toronto, 2006. / Source: Dissertation Abstracts International, Volume: 67-06, Section: B, page: 3492. Includes bibliographical references (leaves 143-151).