We have attempted to discover formal rules for transcribing into musical notation the fugue subjects of the Well-Tempered Clavier, as this might be done by an amanuensis listening to a'dead-pan' performance on the keyboard. In this endeavour two kinds of problem arise: what are the harmonic relations between the notes, and what are the metrical units into which they are grouped? The harmonic problem is that the number of keyboard semitones between two notes does not define-- their harmonic relation, and we further develop an earlier theory of such relations, arriving at an algorithm which assigns every fugue to the right key and correctly notates every accidental in its subject. The metric problem is considered de novo, and a metrical algorithm is described whose failures to generate Bach's notation are as illuminating as its successes. INTRODUCTION The performance of a piece of music involves both the performer and the listener in a problem of interpretation. The performer must discern and express musical relationships which are not fully explicit in the musical score, and the listener must appreciate relationships which are not explicit in the performance. How the performer should convey his interpretation of the piece is an aesthetic question of the utmost delicacy; but the converse process, that of listening to a piece and discerning its structure, is partly amenable to objective investigation. This is because European classical music is written in a notation which conveys to the performer a considerable amount of information about its structure, and this information can be reconstituted by the educated listener from even a mediocre performance. The'correct' annotation of the melody in question is, of course: This gap in musical theory is all the more glaring in view of the considerable effort which has been devoted to much more ambitious undertakings, such as programmed musical composition. We are cynical enough to believe that it is only the prevailing babel in contemporary classical music which saves most of these compositions from being treated with the derision which they merit, and that if any progress is to be made in this direction it will first be essential to formalize the most elementary facts about musical cOmpetence, such as those we have just mentioned.

K.A.PATON 411 24 Centromere finding: some shape descriptors for small chromosome outlines.

Networks as models of word storage: G. R. Kiss

The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

By studying biological systems, Several definitions for the term robot have been proposed principles may be discovered that can be used, perhaps by (Jablonowski and Posey, 1985). None of these definitions analogy, to improve the functional components of a robot are adequate because they exclude robot intelligence of as well as their cooperation.

Addison-Wesley Publishing Company Reading, Massachusetts Menlo Park, California London Amsterdam Don Mills, Ontario Sydney This book is in The Addison-Wesley Series in Artificial Intelligence. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.

D E PA R T M E N T O F E L E CT R IC A L ENGINEERING UNIVE RSITY O F ILLIN O IS C H A M P A IG N, IL L.

We propose a topic modeling approach to the prediction of preferences in pairwise comparisons. We develop a new generative model for pairwise comparisons that accounts for multiple shared latent rankings that are prevalent in a population of users. This new model also captures inconsistent user behavior in a natural way. We show how the estimation of latent rankings in the new generative model can be formally reduced to the estimation of topics in a statistically equivalent topic modeling problem. We leverage recent advances in the topic modeling literature to develop an algorithm that can learn shared latent rankings with provable consistency as well as sample and computational complexity guarantees. We demonstrate that the new approach is empirically competitive with the current state-of-the-art approaches in predicting preferences on some semi-synthetic and real world datasets.

We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models. Using the matrix factorization approach to collaborative filtering, the CKF accounts for time evolution by modeling each low-dimensional latent embedding as a multidimensional Brownian motion. Each observation is a random variable whose distribution is parameterized by the dot product of the relevant Brownian motions at that moment in time. This is naturally interpreted as a Kalman filter with multiple interacting state space vectors. We also present a method for learning a dynamically evolving drift parameter for each location by modeling it as a geometric Brownian motion. We handle posterior intractability via a mean-field variational approximation, which also preserves tractability for downstream calculations in a manner similar to the Kalman filter. We evaluate the model on several large datasets, providing quantitative evaluation on the 10 million Movielens and 100 million Netflix datasets and qualitative evaluation on a set of 39 million stock returns divided across roughly 6,500 companies from the years 1962-2014.

Intelligent systems that learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that characterize the impact of interactivity, demonstrate ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. We argue that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives. After giving a glimpse of the progress that has been made so far, we discuss the challenges that we face in moving the field forward.