Search Results for « Deterministic »

Finite systems of deterministic ordinary nonlinear differential equations may be designed to represent forced dissipative hydrodynamic flow. Solutions of these equations can be identified with trajectories in phase space. For those systems with bounded solutions, it is found that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states. Systems with bounded solutions are shown to possess bounded numerical solutions. A simple system representing cellular convection is solved numerically. All of the solutions are found to be unstable, and almost all of them are nonperiodic. The feasibility of very-long-range weather prediction is examined in the light of these results.

The use of non-deterministic database languages is motivated using pragmatic and theoretical considerations. It is shown that non-determinism resolves some difficulties concerning the expressive power of deterministic languages: there are non-deterministic languages expressing low complexity classes of queries/updates, whereas no such deterministic languages exist. Various mechanisms yielding non-determinism are reviewed. The focus is on two closely related families of non-deterministic languages. The first consists of extensions of Datalog with negations in bodies and/or heads of rules, with non-deterministic fixpoint semantics. The second consists of non-deterministic extensions of first-order logic and fixpoint logics, using the witness operator. The ability of the various non-deterministic languages to express deterministic transformation is characterized. In particular, non-deterministic languages expressing exactly the queries/updates computable in polynomial time are exhibited, whereas it is conjectured that no analogous deterministic language exists. The connection between non-deterministic languages and determinism is also explored. Several problems of practical interest are examined, such as checking (statically or dynamically) if a given program is deterministic, detecting coincidence of deterministic and non-deterministic semantics, and verifying termination for non-deterministic programs.

In this paper we show that a wide class of probabilistic algorithms can be simulated by deterministic algorithms. Namely if there is a test in LOGSPACE so that a random sequence of length (log n)2 / log log n passes the test with probability at least 1/n then a deterministic sequence can be constructed in LOGSPACE which also passes the test. It is important that the machine performing the test gets each bit of the sequence only once. The theorem remains valid if both the test and the machine constructing the satisfying sequence have access to the same oracle of polynomial size.

Greedy transition-based parsers are very fast but tend to suffer from error propagation. This problem is aggravated by the fact that they are normally trained using oracles that are deterministic and incomplete in the sense that they assume a unique canonical path through the transition system and are only valid as long as the parser does not stray from this path. In this paper, we give a general characterization of oracles that are nondeterministic and complete, present a method for deriving such oracles for transition systems that satisfy a property we call arc decomposition, and instantiate this method for three well-known transition systems from the literature. We say that these oracles are dynamic, because they allow us to dynamically explore alternative and nonoptimal paths during training — in contrast to oracles that statically assume a unique optimal path. Experimental evaluation on a wide range of data sets clearly shows that using dynamic oracles to train greedy parsers gives substantial improvements in accuracy. Moreover, this improvement comes at no cost in terms of efficiency, unlike other techniques like beam search.

This paper surveys the deterministic scheduling of jobs m uniprocessor, multiprocessor, and job-shop environments.The survey begins with a brief introduction to the representation of task or job sets, followed by a discussion of classification categories.These categories include number of processors, task interruptlbility, job periodicity, deadlines, and number of resources.Results are given for single-processor schedules in job-shop and multIprogramming environments, flow-shop schedules, and multiprocessor schedules.They are stated in terms of optimal constructive algorithms and suboptimal heuristics.In most cases the latter are stated in terms of performance bounds related to optimal results.Annotations for most of the references are provided in the form of a table classifying the referenced studies m terms of various parameters.

I argue that there are non-trivial objective chances (that is, objective chances other than 0 and 1) even in deterministic worlds. The argument is straightforward. I observe that there are probabilistic special scientific laws even in deterministic worlds. These laws project non-trivial probabilities for the events that they concern. And these probabilities play the chance role and so should be regarded as chances as opposed, for example, to epistemic probabilities or credences. The supposition of non-trivial deterministic chances might seem to land us in contradiction. The fundamental laws of deterministic worlds project trivial probabilities for the very same events that are assigned non-trivial probabilities by the special scientific laws. I argue that any appearance of tension is dissolved by recognition of the level-relativity of chances. There is therefore no obstacle to accepting non-trivial chance-role-playing deterministic probabilities as genuine chances. 1. Introduction 2. Schaffer's Incompatibilist Argument 2.1. Chance and credence 2.2. Chance and possibility 2.3. Chance and laws 3. Special Scientific Laws 3.1. Probabilistic special scientific laws in deterministic worlds 3.2. Lewis's Humean analysis of laws 3.3. Special scientific laws and the law role 4. Deterministic Chance 4.1. Chance and laws again 4.2. Chance and credence again 4.3. Chance and possibility again 5. Chance and Causation 6. Conclusion Appendix: Times, Levels, and Chance Setups