We present 1 an application of algorithmic complexity to trajectory refinement and event detection. Given the image data, the camera calibration and a hypothesis in the form of a vehicle trajectory, an evaluation function is defined that allows a search for the best hypothesis. The function is simply the length of the data after it has been compressed using the hypothesis. The hypothesis at whi...

Inductive learning searches an optimal hypothesis that minimizes a given loss function. It is usually assumed that the simplest hypothesis that fits the data is the best approximate to an optimal hypothesis. Since finding the simplest hypothesis is NP-hard for most representations, we generally employ various heuristics to search its closest match. Computing these heuristics incurs significant ...

Reranking models have been successfully applied to many tasks of Natural Language Processing. However, there are two aspects of this approach that need a deeper investigation: (i) Assessment of hypotheses generated for reranking at classification phase: baseline models generate a list of hypotheses and these are used for reranking without any assessment; (ii) Detection of cases where reranking ...

The constraint satisfaction problem (CSP) is a widely studied problem with numerous applications in computer science. For infinitedomain CSPs, there are many results separating tractable and NP-hard cases while upper bounds on the time complexity of hard cases are virtually unexplored. Hence, we initiate a study of the worst-case time complexity of such CSPs. We analyse backtracking algorithms ...

Themonotone constraint satisfaction problem (MCSP) is the problem of, given an existentially quantified positive formula, decide whether this formula has a model. This problem is a natural generalization of the constraint satisfaction problem, which can be seen as the problem of determining whether a conjunctive formula has a model. In this paper we study the worst-case time complexity, measure...

Markov Decision Processes are one of the most widely used frameworks to formulate probabilistic planning problems. Since planners are often risk-sensitive in high-stake situations, non-linear utility functions are often introduced to describe their preferences among all possible outcomes. Alternatively, risk-sensitive decision makers often require their plans to satisfy certain worst-case guara...

In distributed real-time systems, a task activated periodically by the completion of its preceding task can be modeled as a periodic task with activation jitter. An activation jitter of a task is defined as the difference between the worst case and best case response times of its preceding task. Because current approaches assume that the best case response time is zero or much smaller than the ...

We consider the canonical generalization of the well-studied Longest Increasing Subsequence problem to multiple sequences, called k-LCIS: Given k integer sequences X1, . . . , Xk of length at most n, the task is to determine the length of the longest common subsequence of X1, . . . , Xk that is also strictly increasing. Especially for the case of k = 2 (called LCIS for short), several algorithm...