Semisimplicity in symmetric rigid tensor categories

Let \lambda be a partition of a positive integer n. Let C be a symmetric rigid tensor category over a field k of characteristic 0 or char(k)>n, and let V be an object of C. In our main result (Theorem 4.3) we introduce a finite set of integers F(\lambda) and prove that if the Schur functor \mathbb{S}_{\lambda} V of V is semisimple and the dimension of V is not in F(\lambda), then V is semisimple. Moreover, we prove that for each d in F(\lambda) there exist a symmetric rigid tensor category C over k and a non-semisimple object V in C of dimension d such that \mathbb{S}_{\lambda} V is semisimple (which shows that our result is the best possible). In particular, Theorem 4.3 extends two theorems of Serre for C=Rep(G), G is a group, and \mathbb{S}_{\lambda} V is \wedge^n V or Sym^n V, and proves a conjecture of Serre (\cite{s1}).Comment: 15 pages, minor corrections in Subsection 4.6 and in the proof of Lemma 4.2

Query-based Deep Improvisation

In this paper we explore techniques for generating new music using a Variational Autoencoder (VAE) neural network that was trained on a corpus of specific style. Instead of randomly sampling the latent states of the network to produce free improvisation, we generate new music by querying the network with musical input in a style different from the training corpus. This allows us to produce new musical output with longer-term structure that blends aspects of the query to the style of the network. In order to control the level of this blending we add a noisy channel between the VAE encoder and decoder using bit-allocation algorithm from communication rate-distortion theory. Our experiments provide new insight into relations between the representational and structural information of latent states and the query signal, suggesting their possible use for composition purposes