Symmetric Inkball Alignment with Loopy Models

Alignment tasks generally seek to establish a spatial correspondence between two versions of a text, for example between a set of manuscript images and their transcript. This paper examines a different form of alignment problem, namely pixel-scale alignment between two renditions of a handwritten word or phrase. Using loopy inkball graph models, the proposed technique finds spatial correspondences between two text images such that similar parts map to each other. The method has applications to word spotting and signature verification, and can provide analytical tools for the study of handwriting variation

Cache-aided Interference Management Using Hypercube Combinatorial Cache Designs

We consider a cache-aided interference network which consists of a library of $N$ files, $K_T$ transmitters and $K_R$ receivers (users), each equipped with a local cache of size $M_T$ and $M_R$ files respectively, and connected via a discrete-time additive white Gaussian noise channel. Each receiver requests an arbitrary file from the library. The objective is to design a cache placement without knowing the receivers' requests and a communication scheme such that the sum Degrees of Freedom (sum-DoF) of the delivery is maximized. This network model has been investigated by Naderializadeh {\em et al.}, who proposed a prefetching and a delivery schemes that achieves a sum-DoF of $\min\{\frac{{M_TK_T+K_RM_R}}{{N}}, K_R\}$. One of biggest limitations of this scheme is the requirement of high subpacketization level. This paper is the first attempt in the literature (according to our knowledge) to reduce the file subpacketization in such a network. In particular, we propose a new approach for both prefetching and linear delivery schemes based on a combinatorial design called {\em hypercube}. We show that required number of packets per file can be exponentially reduced compared to the state of the art scheme proposed by Naderializadeh {\em et al.}, or the NMA scheme. When $M_TK_T+K_RM_R \geq K_R$, the achievable one-shot sum-DoF using this approach is $\frac{{M_TK_T+K_RM_R}}{{N}}$ , which shows that 1) the one-shot sum-DoF scales linearly with the aggregate cache size in the network and 2) it is within a factor of $2$ to the information-theoretic optimum. Surprisingly, the identical and near optimal sum-DoF performance can be achieved using the hypercube approach with a much less file subpacketization.Comment: 6 pages, 4 figures, accepted by ICC 201