Instructing Cooperating Assembly Robots through Situated Dialogues in Natural Language

We present an assembly cell consisting of two cooperating robots and a variety of sensors. It offers a number of complex skills necessary for constructing aggregates from elements of a toy construction set. A high degree of flexibility was achieved because the skills were realised only through sensory feedback, not by resorting to fixtures or specialised tools. The operation of the cell is completely controlled through natural language. Results from experiments in cognitive sciences and computer linguistics were incorporated to integrate natural language with vision as well as to control the construction dialogue between a human instructor and the robotic system. The experimental setup is described; a sample dialogue demonstrates the capabilites of the cell. A brief discussion of issues for further research concludes the paper. 1 Introduction 4 1 Introduction Endowing a single robot or a group of cooperating robots with the ability to carry a goal-directed conversation in natural la..

Rose: Generating Sequence Families

2 2 Introduction 3 3 Systems and Methods 5 4 Algorithm 6 4.1 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 4.2 The Root Sequence . . . . . . . . . . . . . . . . . . . . . . . . . 8 4.3 The Mutation Guide Tree . . . . . . . . . . . . . . . . . . . . . . 8 4.3.1 Adjusting the Edge Lengths . . . . . . . . . . . . . . . . 8 4.4 Creation of Child Sequences . . . . . . . . . . . . . . . . . . . . 10 4.5 Sequence Motifs . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.6 Creation of Indels . . . . . . . . . . . . . . . . . . . . . . . . . . 11 5 Implementation 13 5.1 Input/Output formats . . . . . . . . . . . . . . . . . . . . . . . . 13 5.2 Resource Requirements . . . . . . . . . . . . . . . . . . . . . . . 13 5.3 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 5.3.1 A Protein Sequence Family . . . . . . . . . . . . . . . . 14 5.3.2 A Simple DNA Sequence Family with Motif . . . . . . . 14 5.3.3 A Protein Sequence Family with Varying..

Divide-and-Conquer Multiple Sequence Alignment

Contents 1 Introduction 1 1.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Analysis of Differences: Sequence Alignment 7 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Global Sequence Alignment . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Pairwise Sequence Alignment . . . . . . . . . . . . . . . . . . 9 2.2.2 Multiple Sequence Alignment . . . . . . . . . . . . . . . . . . 11 2.3 Alignment Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.1 Single Letter Substitutions . . . . . . . . . . . . . . . . . . . . 13 2.3.2 Pairwise Alignment Score . . . . . . . . . . . . . . . . . . . . 15 2.3.3 Multiple Sequence Alignment Score . . . . . . . . . . . . . . . 18 2.4 The Problem . . . . . . . . .

Suffix Tree Construction and Storage with Limited Main Memory

Abstract. Suffix trees have been established as one of the most versatile index structures for unstructured string data like genomic sequences and other strings. In this work, our goal is the development of algorithms for the efficient construction of suffix trees for very large strings and their convenient storage regarding fast access when main memory is limited. We present a construction algorithm which, to the best of our knowledge, is currently the fastest practical construction method for large suffix trees. Further we propose a clustered storage scheme for the suffix tree that takes into account the locality behavior of typical query types, which leads to a significant speed up particularly for the exact string matching problem. For very large strings the query time is faster than that of other recent index structures like the enhanced suffix array.

Static and Dynamic Filtering Methods for Approximate String Matching

this paper improves on this by merging the filtering and the checking phase. It evaluates the statically derived filter information during the checking phase, strengthening it by information determined dynamically. Rather than using static information once to decide whether a (complete) region must be checked, it is consulted intermittently, in order to immediately abandon a region as soon it becomes clear that, due to a poor start, an approximate match is no longer possible. To understand how this works, recall that dynamic programming gives us information about actual differences of the subwords of T and P under consideration. As opposed to this, the statically derived information tells us where guaranteed differences occur. Of course, the actual differences occur before or at the guaranteed differences. Now suppose we have a subword s passing the static filter, and we know that, after reading a prefix v 1 of s, d actual differences have occurred. Now, if the remaining suffix v 2 of s contains more than k \Gamma

2-Stage Fault Tolerant Interval Group Testing

Abstract. We study the following fault tolerant variant of the interval group testing model: Given three positive integers n, p,e, determine the minimum number of questions needed to identify a (possibly empty) set P ⊆ {1, 2,..., n} (|P | ≤ p), under the following constraints. Questions have the form “Is I ∩P � = ∅?”, where I can be any interval in {1, 2..., n}. Up to e of the answers can be erroneous or lies. Questions are to be organized in batches of non-adaptive questions. Therefore, questions in a given batch can be formulated relying only on the information gathered with the answers to the questions in the previous batches. The study of interval group testing is motivated by several applications. Among others, it has applications to the problem of identifying splice sites in a genome. To the best of our knowledge, we are the first to consider fault tolerant strategies for interval group testing. We completely characterize the fully non-adaptive situation and provide tight bounds for the case of two batch strategies. Our bounds only differ by a factor of � 11/10 for the case p = 1 and at most 2 in the general case.